• Open access
  • Published: 16 May 2024

Procrastination, depression and anxiety symptoms in university students: a three-wave longitudinal study on the mediating role of perceived stress

  • Anna Jochmann 1 ,
  • Burkhard Gusy 1 ,
  • Tino Lesener 1 &
  • Christine Wolter 1  

BMC Psychology volume  12 , Article number:  276 ( 2024 ) Cite this article

Metrics details

It is generally assumed that procrastination leads to negative consequences. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. Therefore, the aim of our study was to examine the harmful consequences of procrastination on students’ stress and mental health. We selected the procrastination-health model as our theoretical foundation and tried to evaluate the model’s assumption that trait procrastination leads to (chronic) disease via (chronic) stress in a temporal perspective. We chose depression and anxiety symptoms as indicators for (chronic) disease and hypothesized that procrastination leads to perceived stress over time, that perceived stress leads to depression and anxiety symptoms over time, and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

To examine these relationships properly, we collected longitudinal data from 392 university students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models.

Procrastination did lead to depression and anxiety symptoms over time. However, perceived stress was not a mediator of this effect. Procrastination did not lead to perceived stress over time, nor did perceived stress lead to depression and anxiety symptoms over time.

Conclusions

We could not confirm that trait procrastination leads to (chronic) disease via (chronic) stress, as assumed in the procrastination-health model. Nonetheless, our study demonstrated that procrastination can have a detrimental effect on mental health. Further health outcomes and possible mediators should be explored in future studies.

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Introduction

“Due tomorrow? Do tomorrow.”, might be said by someone who has a tendency to postpone tasks until the last minute. But can we enjoy today knowing about the unfinished task and tomorrow’s deadline? Or do we feel guilty for postponing a task yet again? Do we get stressed out because we have little time left to complete it? Almost everyone has procrastinated at some point when it came to completing unpleasant tasks, such as mowing the lawn, doing the taxes, or preparing for exams. Some tend to procrastinate more frequently and in all areas of life, while others are less inclined to do so. Procrastination is common across a wide range of nationalities, as well as socioeconomic and educational backgrounds [ 1 ]. Over the last fifteen years, there has been a massive increase in research on procrastination [ 2 ]. Oftentimes, research focuses on better understanding the phenomenon of procrastination and finding out why someone procrastinates in order to be able to intervene. Similarly, the internet is filled with self-help guides that promise a way to overcome procrastination. But why do people seek help for their procrastination? Until now, not much research has been conducted on the negative consequences procrastination could have on health and well-being. Therefore, in the following article we examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship on the basis of the procrastination-health model by Sirois et al. [ 3 ].

Procrastination and its negative consequences

Procrastination can be defined as the tendency to voluntarily and irrationally delay intended activities despite expecting negative consequences as a result of the delay [ 4 , 5 ]. It has been observed in a variety of groups across the lifespan, such as students, teachers, and workers [ 1 ]. For example, some students tend to regularly delay preparing for exams and writing essays until the last minute, even if this results in time pressure or lower grades. Procrastination must be distinguished from strategic delay [ 4 , 6 ]. Delaying a task is considered strategic when other tasks are more important or when more resources are needed before the task can be completed. While strategic delay is viewed as functional and adaptive, procrastination is classified as dysfunctional. Procrastination is predominantly viewed as the result of a self-regulatory failure [ 7 ]. It can be understood as a trait, that is, as a cross-situational and time-stable behavioral disposition [ 8 ]. Thus, it is assumed that procrastinators chronically delay tasks that they experience as unpleasant or difficult [ 9 ]. Approximately 20 to 30% of adults have been found to procrastinate chronically [ 10 , 11 , 12 ]. Prevalence estimates for students are similar [ 13 ]. It is believed that students do not procrastinate more often than other groups. However, it is easy to examine procrastination in students because working on study tasks requires a high degree of self-organization and time management [ 14 ].

It is generally assumed that procrastination leads to negative consequences [ 4 ]. Negative consequences are even part of the definition of procrastination. Research indicates that procrastination is linked to lower academic performance [ 15 ], health impairment (e.g., stress [ 16 ], physical symptoms [ 17 ], depression and anxiety symptoms [ 18 ]), and poor health-related behavior (e.g., heavier alcohol consumption [ 19 ]). However, most studies targeting consequences of procrastination are cross-sectional [ 4 ]. For that reason, it often remains unclear whether an examined outcome is a consequence or an antecedent of procrastination, or whether a reciprocal relationship between procrastination and the examined outcome can be assumed. Additionally, regarding negative consequences of procrastination on health, it is still largely unknown by which mechanisms they are mediated. Uncovering such mediators would be helpful in developing interventions that can prevent negative health consequences of procrastination.

The procrastination-health model

The first and only model that exclusively focuses on the effect of procrastination on health and the mediators of this effect is the procrastination-health model [ 3 , 9 , 17 ]. Sirois [ 9 ] postulates three pathways: An immediate effect of trait procrastination on (chronic) disease and two mediated pathways (see Fig.  1 ).

figure 1

Adopted from the procrastination-health model by Sirois [ 9 ]

The immediate effect is not further explained. Research suggests that procrastination creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 ]. The described feelings could have a detrimental effect on mental health [ 23 , 24 , 25 ].

The first mediated pathway leads from trait procrastination to (chronic) disease via (chronic) stress. Sirois [ 9 ] assumes that procrastination creates stress because procrastinators are constantly aware of the fact that they still have many tasks to complete. Stress activates the hypothalamic-pituitary-adrenocortical (HPA) system, increases autonomic nervous system arousal, and weakens the immune system, which in turn contributes to the development of diseases. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress. She believes that, in the short term, single incidents of procrastination cause acute stress, which leads to acute health problems, such as infections or headaches. In the long term, chronic procrastination, as you would expect with trait procrastination, causes chronic stress, which leads to chronic diseases over time. There is some evidence in support of the stress-related pathway, particularly regarding short-term effects [ 3 , 17 , 26 , 27 , 28 ]. However, as we mentioned above, most of these studies are cross-sectional. Therefore, the causal direction of these effects remains unclear. To our knowledge, long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress have not yet been investigated.

The second mediated pathway leads from trait procrastination to (chronic) disease via poor health-related behavior. According to Sirois [ 9 ], procrastinators form lower intentions to carry out health-promoting behavior or to refrain from health-damaging behavior because they have a low self-efficacy of being able to care for their own health. In addition, they lack the far-sighted view that the effects of health-related behavior only become apparent in the long term. For the same reason, Sirois [ 9 ] believes that there are no short-term, but only long-term effects of procrastination on health mediated by poor health-related behavior. For example, an unhealthy diet leads to diabetes over time. The findings of studies examining the behavioral pathway are inconclusive [ 3 , 17 , 26 , 28 ]. Furthermore, since most of these studies are cross-sectional, they are not suitable for uncovering long-term effects of trait procrastination on (chronic) disease mediated by poor health-related behavior.

In summary, previous research on the two mediated pathways of the procrastination-health model mainly found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. However, only short-term effects have been investigated so far. Moreover, longitudinal studies are needed to be able to assess the causal direction of the relationship between trait procrastination, (chronic) stress, and (chronic) disease. Consequently, our study is the first to examine long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, using a longitudinal design. (Chronic) disease could be measured by a variety of different indicators (e.g., physical symptoms, diabetes, or coronary heart disease). We choose depression and anxiety symptoms as indicators for (chronic) disease because they signal mental health complaints before they manifest as (chronic) diseases. Additionally, depression and anxiety symptoms are two of the most common mental health complaints among students [ 29 , 30 ] and procrastination has been shown to be a significant predictor of depression and anxiety symptoms [ 18 , 31 , 32 , 33 , 34 ]. Until now, the stress-related pathway of the procrastination-health model with depression and anxiety symptoms as the health outcome has only been analyzed in one cross-sectional study that confirmed the predictions of the model [ 35 ].

The aim of our study is to evaluate some of the key assumptions of the procrastination-health model, particularly the relationships between trait procrastination, (chronic) stress, and (chronic) disease over time, surveyed in the following analysis using depression and anxiety symptoms.

In line with the key assumptions of the procrastination-health model, we postulate (see Fig.  2 ):

Procrastination leads to perceived stress over time.

Perceived stress leads to depression and anxiety symptoms over time.

Procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

figure 2

The section of the procrastination-health model we examined

Materials and methods

Our study was part of a health monitoring at a large German university Footnote 1 . Ethical approval for our study was granted by the Ethics Committee of the university’s Department of Education and Psychology. We collected the initial data in 2019. Two occasions followed, each at an interval of six months. In January 2019, we sent out 33,267 invitations to student e-mail addresses. Before beginning the survey, students provided their written informed consent to participate in our study. 3,420 students took part at the first occasion (T1; 10% response rate). Of these, 862 participated at the second (T2) and 392 at the third occasion (T3). In order to test whether dropout was selective, we compared sociodemographic and study specific characteristics (age, gender, academic semester, number of assessments/exams) as well as behavior and health-related variables (procrastination, perceived stress, depression and anxiety symptoms) between the participants of the first wave ( n  = 3,420) and those who participated three times ( n  = 392). Results from independent-samples t-tests and chi-square analysis showed no significant differences regarding sociodemographic and study specific characteristics (see Additional file 1: Table S1 and S2 ). Regarding behavior and health-related variables, independent-samples t-tests revealed a significant difference in procrastination between the two groups ( t (3,409) = 2.08, p  < .05). The mean score of procrastination was lower in the group that participated in all three waves.

The mean age of the longitudinal respondents was 24.1 years ( SD  = 5.5 years), the youngest participants were 17 years old, the oldest one was 59 years old. The majority of participants was female (74.0%), 7 participants identified neither as male nor as female (1.8%). The respondents were on average enrolled in the third year of studying ( M  = 3.9; SD  = 2.3). On average, the students worked about 31.2 h ( SD  = 14.1) per week for their studies, and an additional 8.5 h ( SD  = 8.5) for their (part-time) jobs. The average income was €851 ( SD  = 406), and 4.9% of the students had at least one child. The students were mostly enrolled in philosophy and humanities (16.5%), education and psychology (15.8%), biology, chemistry, and pharmacy (12.5%), political and social sciences (10.6%), veterinary medicine (8.9%), and mathematics and computer science (7.7%).

We only used established and well evaluated instruments for our analyses.

  • Procrastination

We adopted the short form of the Procrastination Questionnaire for Students (PFS-4) [ 36 ] to measure procrastination. The PFS-4 assesses procrastination at university as a largely stable behavioral disposition across situations, that is, as a trait. The questionnaire consists of four items (e.g., I put off starting tasks until the last moment.). Each item was rated on a 5-point scale ((almost) never = 1 to (almost) always = 5) for the last two weeks. All items were averaged, with higher scores indicating a greater tendency to procrastinate. The PFS-4 has been proven to be reliable and valid, showing very high correlations with other established trait procrastination scales, for example, with the German short form of the General Procrastination Scale [ 37 , 38 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.90.

Perceived stress

The Heidelberger Stress Index (HEI-STRESS) [ 39 ] is a three-item measure of current perceived stress due to studying as well as in life in general. For the first item, respondents enter a number between 0 (not stressed at all) and 100 (completely stressed) to indicate how stressed their studies have made them feel over the last four weeks. For the second and third item, respondents rate on a 5-point scale how often they feel “stressed and tense” and as how stressful they would describe their life at the moment. We transformed the second and third item to match the range of the first item before we averaged all items into a single score with higher values indicating greater perceived stress. We proved the scale to be one-dimensional and Cronbach’s alpha for our study was 0.86.

Depression and anxiety symptoms

We used the Patient Health Questionnaire-4 (PHQ-4) [ 40 ], a short form of the Patient Health Questionnaire [ 41 ] with four items, to measure depression and anxiety symptoms. The PHQ-4 contains two items from the Patient Health Questionnaire-2 (PHQ-2) [ 42 ] and the Generalized Anxiety Disorder Scale-2 (GAD-2) [ 43 ], respectively. It is a well-established screening scale designed to assess the core criteria of major depressive disorder (PHQ-2) and generalized anxiety disorder (GAD-2) according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). However, it was shown that the GAD-2 is also appropriate for screening other anxiety disorders. According to Kroenke et al. [ 40 ], the PHQ-4 can be used to assess a person’s symptom burden and impairment. We asked the participants to rate how often they have been bothered over the last two weeks by problems, such as “Little interest or pleasure in doing things”. Response options were 0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day. Calculated as the sum of the four items, the total scores range from 0 to 12 with higher scores indicating more frequent depression and anxiety symptoms. The total scores can be categorized as none-to-minimal (0–2), mild (3–5), moderate (6–8), and severe (9–12) depression and anxiety symptoms. The PHQ-4 was shown to be reliable and valid [ 40 , 44 , 45 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.86.

Data analysis

To test our hypotheses, we performed structural equation modelling (SEM) using R (Version 4.1.1) with the package lavaan. All items were standardized ( M  = 0, SD  = 1). Due to the non-normality of some study variables and a sufficiently large sample size of N near to 400 [ 46 ], we used robust maximum likelihood estimation (MLR) for all model estimations. As recommended by Hu and Bentler [ 47 ], we assessed the models’ goodness of fit by chi-square test statistic, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), Tucker-Lewis index (TLI), and comparative fit index (CFI). A non-significant chi-square indicates good model fit. Since chi-square is sensitive to sample size, we also evaluated fit indices less sensitive to the number of observations. RMSEA and SRMR values of 0.05 or lower as well as TLI and CFI values of 0.97 or higher indicate good model fit. RMSEA values of 0.08 or lower, SRMR values of 0.10 or lower, as well as TLI and CFI values of 0.95 or higher indicate acceptable model fit [ 48 , 49 ]. First, we conducted confirmatory factor analysis for the first occasion, defining three factors that correspond to the measures of procrastination, perceived stress, and depression and anxiety symptoms. Next, we tested for measurements invariance over time and specified the measurement model, before testing our hypotheses.

Measurement invariance over time

To test for measurement invariance over time, we defined one latent variable for each of the three occasions, corresponding to the measures of procrastination, perceived stress, and depression and anxiety symptoms, respectively. As recommended by Geiser and colleagues [ 50 ], the links between indicators and factors (i.e., factor loadings and intercepts) should be equal over measurement occasions; therefore, we added indicator specific factors. A first and least stringent step of testing measurement invariance is configural invariance (M CI ). It was examined whether the included constructs (procrastination, perceived stress, depression and anxiety symptoms) have the same pattern of free and fixed loadings over time. This means that the assignment of the indicators to the three latent factors over time is supported by the underlying data. If configural invariance was supported, restrictions for the next step of testing measurement invariance (metric or weak invariance; M MI ) were added. This means that each item contributes to the latent construct to a similar degree over time. Metric invariance was tested by constraining the factor loadings of the constructs over time. The next step of testing measurement invariance (scalar or strong invariance; M SI ) consisted of checking whether mean differences in the latent construct capture all mean differences in the shared variance of the items. Scalar invariance was tested by constraining the item intercepts over time. The constraints applied in the metric invariance model were retained [ 51 ]. For the last step of testing measurement invariance (residual or strict invariance; M RI ), the residual variables were also set equal over time. If residual invariance is supported, differences in the observed variables can exclusively be attributed to differences in the variances of the latent variables.

We used the Satorra-Bentler chi-square difference test to evaluate the superiority of a more stringent model [ 52 ]. We assumed the model with the largest number of invariance restrictions – which still has an acceptable fit and no substantial deterioration of the chi-square value – to be the final model [ 53 ]. Following previous recommendations, we considered a decrease in CFI of 0.01 and an increase in RMSEA of 0.015 as unacceptable to establish measurement invariance [ 54 ]. If a more stringent model had a significant worse chi-square value, but the model fit was still acceptable and the deterioration in model fit fell within the change criteria recommended for CFI and RMSEA values, we still considered the more stringent model to be superior.

Hypotheses testing

As recommended by Dormann et al. [ 55 ], we applied autoregressive time-lagged panel models to test our hypotheses. In the first step, we specified a model (M 0 ) that only included the stabilities of the three variables (procrastination, perceived stress, depression and anxiety symptoms) over time. In the next step (M 1 ), we added the time-lagged effects from procrastination (T1) to perceived stress (T2) and from procrastination (T2) to perceived stress (T3) as well as from perceived stress (T1) to depression and anxiety symptoms (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3). Additionally, we included a direct path from procrastination (T1) to depression and anxiety symptoms (T3). If this path becomes significant, we can assume a partial mediation [ 55 ]. Otherwise, we can assume a full mediation. We compared these nested models using the Satorra-Bentler chi-square difference test and the Akaike information criterion (AIC). The chi-square difference value should either be non-significant, indicating that the proposed model including our hypotheses (M 1 ) does not have a significant worse model fit than the model including only stabilities (M 0 ), or, if significant, it should be in the direction that M 1 fits the data better than M 0 . Regarding the AIC, M 1 should have a lower value than M 0 .

Table  1 displays the means, standard deviations, internal consistencies (Cronbach’s alpha), and stabilities (correlations) of all study variables. The alpha values of procrastination, perceived stress, and depression and anxiety symptoms are classified as good (> 0.80) [ 56 ]. The correlation matrix of the manifest variables used for the analyses can be found in the Additional file 1: Table  S3 .

We observed the highest test-retest reliabilities for procrastination ( r  ≥ .74). The test-retest reliabilities for depression and anxiety symptoms ( r  ≥ .64) and for perceived stress ( r  ≥ .54) were a bit lower (see Table  1 ). The pattern of correlations shows a medium to large but positive relationship between procrastination and depression and anxiety symptoms [ 57 , 58 ]. The association between procrastination and perceived stress was small, the one between perceived stress and depression and anxiety symptoms very large (see Table  1 ).

Confirmatory factor analysis showed an acceptable to good fit (x 2 (41) = 118.618, p  < .001; SRMR = 0.042; RMSEA = 0.071; TLI = 0.95; CFI = 0.97). When testing for measurement invariance over time for each construct, the residual invariance models with indicator specific factors provided good fit to the data (M RI ; see Table  2 ), suggesting that differences in the observed variables can exclusively be attributed to differences of the latent variables. We then specified and tested the measurement model of the latent constructs prior to model testing based on the items of procrastination, perceived stress, and depression and anxiety symptoms. The measurement model fitted the data well (M M ; see Table  3 ). All items loaded solidly on their respective factors (0.791 ≤ β ≤ 0.987; p  < .001).

To test our hypotheses, we analyzed the two models described in the methods section.

The fit of the stability model (M 0 ) was acceptable (see Table  3 ). Procrastination was stable over time, with stabilities above 0.82. The stabilities of perceived stress as well as depression and anxiety symptoms were somewhat lower, ranging from 0.559 (T1 -> T2) to 0.696 (T2 -> T3) for perceived stress and from 0.713 (T2 -> T3) to 0.770 (T1 -> T2) for depression and anxiety symptoms, respectively.

The autoregressive mediation model (M 1 ) fitted the data significantly better than M 0 . The direct path from procrastination (T1) to depression and anxiety symptoms (T3) was significant (β = 0.16; p  < .001), however, none of the mediated paths (from procrastination (T1) to perceived stress (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3)) proved to be substantial. Also, the time-lagged paths from perceived stress (T1) to depression and anxiety symptoms (T2) and from procrastination (T2) to perceived stress (T3) were not substantial either (see Fig.  3 ).

To examine whether the hypothesized effects would occur over a one-year period rather than a six-months period, we specified an additional model with paths from procrastination (T1) to perceived stress (T3) and from perceived stress (T1) to depression and anxiety symptoms (T3), also including the stabilities of the three constructs as in the stability model M 0 . The model showed an acceptable fit (χ 2 (486) = 831.281, p  < .001; RMSEA = 0.048; SRMR = 0.091; TLI = 0.95; CFI = 0.95), but neither of the two paths were significant.

Therefore, our hypotheses, that procrastination leads to perceived stress over time (H1) and that perceived stress leads to depression and anxiety symptoms over time (H2) must be rejected. We could only partially confirm our third hypothesis, that procrastination leads to depression and anxiety over time, mediated by perceived stress (H3), since procrastination did lead to depression and anxiety symptoms over time. However, this effect was not mediated by perceived stress.

figure 3

Results of the estimated model including all hypotheses (M 1 ). Note Non-significant paths are dotted. T1 = time 1; T2 = time 2; T3 = time 3. *** p  < .001

To sum up, we tried to examine the harmful consequences of procrastination on students’ stress and mental health. Hence, we selected the procrastination-health model by Sirois [ 9 ] as a theoretical foundation and tried to evaluate some of its key assumptions in a temporal perspective. The author assumes that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and postulated, in line with the key assumptions of the procrastination-health model, that procrastination leads to perceived stress over time (H1), that perceived stress leads to depression and anxiety symptoms over time (H2), and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress (H3). To examine these relationships properly, we collected longitudinal data from students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models. Our first and second hypotheses had to be rejected: Procrastination did not lead to perceived stress over time, and perceived stress did not lead to depression and anxiety symptoms over time. However, procrastination did lead to depression and anxiety symptoms over time – which is in line with our third hypothesis – but perceived stress was not a mediator of this effect. Therefore, we could only partially confirm our third hypothesis.

Our results contradict previous studies on the stress-related pathway of the procrastination-health model, which consistently found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. Since most of these studies were cross-sectional, though, the causal direction of these effects remained uncertain. There are two longitudinal studies that confirm the stress-related pathway of the procrastination-health model [ 27 , 28 ], but both studies examined short-term effects (≤ 3 months), whereas we focused on more long-term effects. Therefore, the divergent findings may indicate that there are short-term, but no long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress.

Our results especially raise the question whether trait procrastination leads to (chronic) stress in the long term. Looking at previous longitudinal studies on the effect of procrastination on stress, the following stands out: At shorter study periods of two weeks [ 27 ] and four weeks [ 28 ], the effect of procrastination on stress appears to be present. At longer study periods of seven weeks [ 59 ], three months [ 28 ], six months, and twelve months, as in our study, the effect of procrastination on stress does not appear to be present. There is one longitudinal study in which procrastination was a significant predictor of stress symptoms nine months later [ 34 ]. The results of this study should be interpreted with caution, though, because the outbreak of the COVID-19 pandemic fell within the study period, which could have contributed to increased stress symptoms [ 60 ]. Unfortunately, Johansson et al. [ 34 ] did not report whether average stress symptoms increased during their study. In one of the two studies conducted by Fincham and May [ 59 ], the COVID-19 pandemic outbreak also fell within their seven-week study period. However, they reported that in their study, average stress symptoms did not increase from baseline to follow-up. Taken together, the findings suggest that procrastination can cause acute stress in the short term, for example during times when many tasks need to be completed, such as at the end of a semester, but that procrastination does not lead to chronic stress over time. It seems possible that students are able to recover during the semester from the stress their procrastination caused at the end of the previous semester. Because of their procrastination, they may also have more time to engage in relaxing activities, which could further mitigate the effect of procrastination on stress. Our conclusions are supported by an early and well-known longitudinal study by Tice and Baumeister [ 61 ], which compared procrastinating and non-procrastinating students with regard to their health. They found that procrastinators experienced less stress than their non-procrastinating peers at the beginning of the semester, but more at the end of the semester. Additionally, our conclusions are in line with an interview study in which university students were asked about the consequences of their procrastination [ 62 ]. The students reported that, due to their procrastination, they experience high levels of stress during periods with heavy workloads (e.g., before deadlines or exams). However, the stress does not last, instead, it is relieved immediately after these periods.

Even though research indicates, in line with the assumptions of the procrastination-health model, that stress is a risk factor for physical and mental disorders [ 63 , 64 , 65 , 66 ], perceived stress did not have a significant effect on depression and anxiety symptoms in our study. The relationship between stress and mental health is complex, as people respond to stress in many different ways. While some develop stress-related mental disorders, others experience mild psychological symptoms or no symptoms at all [ 67 ]. This can be explained with the help of vulnerability-stress models. According to vulnerability-stress models, mental illnesses emerge from an interaction of vulnerabilities (e.g., genetic factors, difficult family backgrounds, or weak coping abilities) and stress (e.g., minor or major life events or daily hassles) [ 68 , 69 ]. The stress perceived by the students in our sample may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. However, since we did not assess individual vulnerability and stress factors in our study, these considerations are mere speculation.

In our study, procrastination led to depression and anxiety symptoms over time, which is consistent with the procrastination-health model as well as previous cross-sectional and longitudinal evidence [ 18 , 21 , 31 , 32 , 33 , 34 ]. However, it is still unclear by which mechanisms this effect is mediated, as perceived stress did not prove to be a substantial mediator in our study. One possible mechanism would be that procrastination impairs affective well-being [ 70 ] and creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 , 62 , 71 ], which in turn could lead to depression and anxiety symptoms [ 23 , 24 , 25 ]. Other potential mediators of the relationship between procrastination and depression and anxiety symptoms emerge from the behavioral pathway of the procrastination-health model, suggesting that poor health-related behaviors mediate the effect of trait procrastination on (chronic) disease. Although evidence for this is still scarce, the results of one cross-sectional study, for example, indicate that poor sleep quality might mediate the effect of procrastination on depression and anxiety symptoms [ 35 ].

In summary, we found that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. For the most part, the relationships between procrastination, perceived stress, and depression and anxiety symptoms did not match the relationships between trait procrastination, (chronic) stress, and (chronic) disease as assumed in the procrastination-health model. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. In conclusion, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model.

Limitations and suggestions for future research

In our study, we tried to draw causal conclusions about the harmful consequences of procrastination on students’ stress and mental health. However, since procrastination is a trait that cannot be manipulated experimentally, we have conducted an observational rather than an experimental study, which makes causal inferences more difficult. Nonetheless, a major strength of our study is that we used a longitudinal design with three waves. This made it possible to draw conclusions about the causal direction of the effects, as in hardly any other study targeting consequences of procrastination on health before [ 4 , 28 , 55 ]. Therefore, we strongly recommend using a similar longitudinal design in future studies on the procrastination-health model or on consequences of procrastination on health in general.

We chose a time lag of six months between each of the three measurement occasions to examine long-term effects of procrastination on depression and anxiety symptoms mediated by perceived stress. However, more than six months may be necessary for the hypothesized effects to occur [ 72 ]. The fact that the temporal stabilities of the examined constructs were moderate or high (0.559 ≤ β ≤ 0.854) [ 73 , 74 ] also suggests that the time lags may have been too short. The larger the time lag, the lower the temporal stabilities, as shown for depression and anxiety symptoms, for example [ 75 ]. High temporal stabilities make it more difficult to detect an effect that actually exists [ 76 ]. Nonetheless, Dormann and Griffin [ 77 ] recommend using shorter time lags of less than one year, even with high stabilities, because of other influential factors, such as unmeasured third variables. Therefore, our time lags of six months seem appropriate.

It should be discussed, though, whether it is possible to detect long-term effects of the stress-related pathway of the procrastination-health model within a total study period of one year. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress, but does not address how long it might take for long-term effects to occur or when effects can be considered long-term instead of short-term. The fact that an effect of procrastination on stress is evident at shorter study periods of four weeks or less but in most cases not at longer study periods of seven weeks or more, as we mentioned earlier, could indicate that short-term effects occur within the time frame of one to three months, considering the entire stress-related pathway. Hence, it seems appropriate to assume that we have examined rather long-term effects, given our study period of six and twelve months. Nevertheless, it would be beneficial to use varying study periods in future studies, in order to be able to determine when effects can be considered long-term.

Concerning long-term effects of the stress-related pathway, Sirois [ 9 ] assumes that chronic procrastination causes chronic stress, which leads to chronic diseases over time. The term “chronic stress” refers to prolonged stress episodes associated with permanent tension. The instrument we used captures perceived stress over the last four weeks. Even though the perceived stress of the students in our sample was relatively stable (0.559 ≤ β ≤ 0.696), we do not know how much fluctuation occurred between each of the three occasions. However, there is some evidence suggesting that perceived stress is strongly associated with chronic stress [ 78 ]. Thus, it seems acceptable that we used perceived stress as an indicator for chronic stress in our study. For future studies, we still suggest the use of an instrument that can more accurately reflect chronic stress, for example, the Trier Inventory for Chronic Stress (TICS) [ 79 ].

It is also possible that the occasions were inconveniently chosen, as they all took place in a critical academic period near the end of the semester, just before the examination period began. We chose a similar period in the semester for each occasion for the sake of comparability. However, it is possible that, during this preparation periods, stress levels peaked and procrastinators procrastinated less because they had to catch up after delaying their work. This could have introduced bias to the data. Therefore, in future studies, investigation periods should be chosen that are closer to the beginning or in the middle of a semester.

Furthermore, Sirois [ 9 ] did not really explain her understanding of “chronic disease”. However, it seems clear that physical illnesses, such as diabetes or cardiovascular diseases, are meant. Depression and anxiety symptoms, which we chose as indicators for chronic disease, represent mental health complaints that do not have to be at the level of a major depressive disorder or an anxiety disorder, in terms of their quantity, intensity, or duration [ 40 ]. But they can be viewed as precursors to a major depressive disorder or an anxiety disorder. Therefore, given our study period of one year, it seems appropriate to use depression and anxiety symptoms as indicators for chronic disease. At longer study periods, we would expect these mental health complaints to manifest as mental disorders. Moreover, the procrastination-health model was originally designed to be applied to physical diseases [ 3 ]. Perhaps, the model assumptions are more applicable to physical diseases than to mental disorders. By applying parts of the model to mental health complaints, we have taken an important step towards finding out whether the model is applicable to mental disorders as well. Future studies should examine additional long-term health outcomes, both physical and psychological. This would help to determine whether trait procrastination has varying effects on different diseases over time. Furthermore, we suggest including individual vulnerability and stress factors in future studies in order to be able to analyze the effect of (chronic) stress on (chronic) diseases in a more differentiated way.

Regarding our sample, 3,420 students took part at the first occasion, but only 392 participated three times, which results in a dropout rate of 88.5%. At the second and third occasion, invitation e-mails were only sent to participants who had indicated at the previous occasion that they would be willing to participate in a repeat survey and provided their e-mail address. This is probably one of the main reasons for our high dropout rate. Other reasons could be that the students did not receive any incentives for participating in our study and that some may have graduated between the occasions. Selective dropout analysis revealed that the mean score of procrastination was lower in the group that participated in all three waves ( n  = 392) compared to the group that participated in the first wave ( n  = 3,420). One reason for this could be that those who have a higher tendency to procrastinate were more likely to procrastinate on filling out our survey at the second and third occasion. The findings of our dropout analysis should be kept in mind when interpreting our results, as lower levels of procrastination may have eliminated an effect on perceived stress or on depression and anxiety symptoms. Additionally, across all age groups in population-representative samples, the student age group reports having the best subjective health [ 80 ]. Therefore, it is possible that they are more resilient to stress and experience less impairment of well-being than other age groups. Hence, we recommend that future studies focus on other age groups as well.

It is generally assumed that procrastination leads to lower academic performance, health impairment, and poor health-related behavior. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. In consequence, the aim of our study was to examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship. We selected the procrastination-health model as a theoretical foundation and used the stress-related pathway of the model, assuming that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and collected longitudinal data from students at three occasions over a one-year period. This allowed us to draw conclusions about the causal direction of the effects, as in hardly any other study examining consequences of procrastination on (mental) health before. Our results indicate that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient on its own, that is, without the presence of other risk factors, to cause depression and anxiety symptoms. Overall, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model. Our study emphasizes the importance of identifying the consequences procrastination can have on health and well-being and determining by which mechanisms they are mediated. Only then will it be possible to develop interventions that can prevent negative health consequences of procrastination. Further health outcomes and possible mediators should be explored in future studies, using a similar longitudinal design.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

University Health Report at Freie Universität Berlin.

Abbreviations

Comparative fit index

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Generalized Anxiety Disorder Scale-2

Heidelberger Stress Index

Hypothalamic-pituitary-adrenocortical

Robust maximum likelihood estimation

Short form of the Procrastination Questionnaire for Students

Patient Health Questionnaire-2

Patient Health Questionnaire-4

Root mean square error of approximation

Structural equation modeling

Standardized root mean square residual

Tucker-Lewis index

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Conceptualization: A.J., B.G., T.L.; methodology: B.G., A.J.; validation: B.G.; formal analysis: A.J., B.G.; investigation: C.W., T.L., B.G.; data curation: C.W., T.L., B.G.; writing–original draft preparation: A.J., B.G.; writing–review and editing: A.J., T.L., B.G., C.W.; visualization: A.J., B.G.; supervision: B.G., T.L.; project administration: C.W., T.L., B.G.; All authors contributed to the article and approved the submitted version.

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Correspondence to Anna Jochmann or Burkhard Gusy .

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Selective dropout analysis and correlation matrix of the manifest variables

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Jochmann, A., Gusy, B., Lesener, T. et al. Procrastination, depression and anxiety symptoms in university students: a three-wave longitudinal study on the mediating role of perceived stress. BMC Psychol 12 , 276 (2024). https://doi.org/10.1186/s40359-024-01761-2

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Research Article

Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey

Roles Conceptualization, Data curation, Formal analysis, Software, Writing – original draft

* E-mail: [email protected]

Affiliation Statistics Discipline, Science, Engineering and Technology (SET) School, Khulna University, Khulna, Bangladesh

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Roles Data curation, Writing – original draft

Roles Data curation, Methodology

Roles Supervision, Writing – original draft, Writing – review & editing

Affiliation Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh

  • Md. Akhtarul Islam, 
  • Sutapa Dey Barna, 
  • Hasin Raihan, 
  • Md. Nafiul Alam Khan, 
  • Md. Tanvir Hossain

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  • Published: August 26, 2020
  • https://doi.org/10.1371/journal.pone.0238162
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Fig 1

The purpose of this study was to investigate the prevalence of depression and anxiety among Bangladeshi university students during the COVID-19 pandemic. It also aimed at identifying the determinants of depression and anxiety. A total of 476 university students living in Bangladesh participated in this cross-sectional web-based survey. A standardized e-questionnaire was generated using the Google Form, and the link was shared through social media—Facebook. The information was analyzed in three consecutive levels, such as univariate, bivariate, and multivariate analysis. Students were experiencing heightened depression and anxiety. Around 15% of the students reportedly had moderately severe depression, whereas 18.1% were severely suffering from anxiety. The binary logistic regression suggests that older students have greater depression (OR = 2.886, 95% CI = 0.961–8.669). It is also evident that students who provided private tuition in the pre-pandemic period had depression (OR = 1.199, 95% CI = 0.736–1.952). It is expected that both the government and universities could work together to fix the academic delays and financial problems to reduce depression and anxiety among university students.

Citation: Islam MA, Barna SD, Raihan H, Khan MNA, Hossain MT (2020) Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey. PLoS ONE 15(8): e0238162. https://doi.org/10.1371/journal.pone.0238162

Editor: Amir H. Pakpour, Qazvin University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN

Received: June 13, 2020; Accepted: August 11, 2020; Published: August 26, 2020

Copyright: © 2020 Islam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available from https://doi.org/10.7910/DVN/N5BUJR .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The outbreak of coronavirus diseases (COVID-19) has been substantially influencing the life and living of people across the world, especially after the declaration of a global pandemic by the World Health Organization in the second week of March 2020 [ 1 ]. As of June 7, 2020, around 6.91 million people were infected with the COVID-19, with a confirmed fatality of another 0.4 million worldwide [ 2 ]. Hence, many countries implemented a range of anti-epidemic measures, such as restricting travel for foreign nationals [ 3 ], closing down public spaces, and shutting down the entire transit system [ 4 , 5 ], to contain the transmission of the highly contagious infections from human-to-human.

Following the detection of first COVID-19 case on March 8, 2020 [ 6 ], Bangladesh like many other countries put the lockdown strategy into effect on March 26, 2020, to ensure ‘social distance’ through ‘home quarantine’ to curb the ‘spread’ among its population [ 7 – 9 ], since a precise treatment or vaccine for the infected and people at risk are yet to achieved by the global health community [ 10 , 11 ]. However, all education institutions were closed initially from March 18 to March 31, 2020 across the country and later extended to the mid of June 2020 in phases [ 12 , 13 ].

This unprecedented experience of ‘home quarantine’ under lockdown with the uncertainty of academic and professional career has multifaceted impacts on the mental health of students. For example, a Canadian study focusing on the effects of quarantine after the severe acute respiratory syndrome (SARS) epidemic found an association between longer duration of quarantine with a high prevalence of anxiety and depression among people [ 14 ]. The ongoing COVID-19 pandemic is creating a psycho-emotional chaotic situation as countries have been reporting a sharp rise of mental health problems, including anxiety, depression, stress, sleep disorder as well as fear, among its citizens [ 15 – 19 ], that eventually increased the substance use [ 15 ] and sometimes suicidal behavior [ 20 – 22 ]. Researchers in China observed that the greater exposure to ‘misinformation’ through social media are more likely contributing to the development of anxiety, depression, and other mental health problems among its population of different socioeconomic background [ 23 – 26 ]. Studies before the COVID-19 pandemic also suggested an inverse relationship between media exposure and mental health [ 27 , 28 ]. On the contrary, a study in South Korea during the Middle East respiratory syndrome (MERS) reported a positive relationship between risk perception and media exposure [ 29 ].

Given the unexpected circumstances, it is crucial to explore the psycho-social experience of university students in Bangladesh, especially during the COVID-19 pandemic. Such a study is expected to measure the psychological impacts of an unforeseen emergency on students, as well as to formulate and execute effective interventions and strategies to mitigate the mental health of people at large. This study was designed to address the psychological problems experienced by university students in Bangladesh.

Materials and methods

Data source.

The survey was conducted in the second week of May, from May 6 to May 12, 2020. Students enrolled in different universities across Bangladesh were the target population. An easy to understand questionnaire was used to collect ‘basic information,’ ‘depression,’ and ‘anxiety’ related information. An online-based platform was used to distribute the e-questionnaire, developed by using the Google Form, to the students. University students from all the divisions in Bangladesh were contacted through different social networks and interviewed (see Fig 1 ).

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https://doi.org/10.1371/journal.pone.0238162.g001

Sampling technique

The Snowball sampling technique was used for collecting information from students. An informed consent form was attached to the e-questionnaire, and each participant consented to participate in the survey after reading the consent form. The participants were asked to share the e-questionnaire with their friends using their personal and institutional Facebook and Messenger.

Ethical issues

This study was formally approved by the Ethical Clearance Committee of Khulna University, Bangladesh. The participants responded anonymously to the online survey by filling up an informed consent letter in the first section of the e-questionnaire. In the consent form, all the participants were provided with information concerning the research purpose, confidentiality of information, and right to revoke the participation without prior justification.

Basic information.

‘Basic Information’ contained the personal information of the respondents. Current ‘age’ of students (‘17–20’, ‘21–24’, ‘>24’), whether the student is ‘lagging behind study’ (‘yes’ and ‘no’), doing any sorts of ‘exercise during lockdown’ (‘yes’ and ‘no’), students who did ‘tuition’ before lockdown (‘yes’ and ‘no’), the gender of the student (‘male’ and ‘female’), ‘place of residence’ of students (‘rural’ and ‘urban’), is he/she ‘living with family’ during lockdown (‘yes’ and ‘no’).

Depression.

Depression was determined by using the Patient Health Questionnaire (PHQ-9). PHQ-9 is an easy way to use in a questionnaire for screening depression of the responses that are used to predict depression of an individual and what state he/she is in during the survey. The scores in PHQ-9 range from ‘0 = not at all’ to ‘3 = nearly every day’ [ 30 ]. The reason for choosing PHQ-9 was that it proved to be a useful tool for detecting depression [ 31 ]. The levels of depression for the study were categorized as ‘mild = 5–9’, ‘moderate = 10–14,’ ‘moderately severe = 15–19,’ ‘severe = ≥ 20.’

Anxiety was evaluated by using the Generalized Anxiety Disorder (GAD-7). In the questionnaire, the questions were used for screening anxiety state of an individual on a scale ranging from ‘0 = not at all sure’ to ‘3 = nearly every day’ [ 32 ]. GAD-7 has been found successful in identifying anxiety among different populations and thus used for its reliability [ 33 ]. The levels of anxiety for the study were categorized as ‘none-minimal = <5,’ ‘mild = 5–9,’ ‘moderate = 10–14 and ‘severe = ≥ 15.’.

Statistical analysis

depression among university students essay

Rather than choosing parameters that minimize the sum of squared errors (like in ordinary regression), estimation in logistic regression accepts parameters that maximize the likelihood of observing the sample values.

Table 1 shows the descriptive information of different selected variables of the university student in Bangladesh. Results show that 392 (82.4%) students were found to have mild to severe depressive symptoms, and 389 (87.7%) students were found to have mild to severe anxiety symptoms. More than 60% of the students were male (67.2%), and the rest were female. One in three students lived in rural areas (35.1%). Less than a quarter percent of students (24.8%) believed that they were not academically lagging, and just over 30% reportedly have exercise regularly during the lockdown at home.

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https://doi.org/10.1371/journal.pone.0238162.t001

Table 2 shows the prevalence of depression and anxiety among Bangladeshi university students. Out of the total 476 valid participants, 392 (82.4%) were found to have mild to severe depressive symptoms. Male (67.35%) had higher depressive symptoms than the female (32.65%) counterparts, whereas students in the early twenties (66.07%) showed higher depressive symptoms than other age groups. Depression was also prevalent among students with no physical exercise (62.24%) and those who consider themselves lagging behind others in terms of academic activities (76.78%). Besides, students living with families (96.93%) and in urban areas (65.05%) showed higher depressive symptoms. In the case of anxiety, 389 (87.7%) students exhibited mild to severe anxiety symptoms. Out of the total students suffering from an anxiety disorder, females (33.67%) had lower anxiety symptoms than males (66.33%), whereas students in the early twenties (66.58%) showed higher anxiety. Like depression, anxiety was also prevalent mostly among students with no physical exercise (61.95%), troubled with the thought of lagging behind others academically (76.60%). Moreover, students living in urban areas (62.21%) with families (96.40%) also showed symptoms of anxiety.

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https://doi.org/10.1371/journal.pone.0238162.t002

Table 3 reveals that students who thought that s/he was lagging behind others in academic activities were 1.8 times (95% CI: 1.098, 2.935) more likely to be depressed than the student with no such worries. Students living with families were 2.6 times (95% CI: 1.418, 4.751), more likely to be depressed than the students living apart from families. On the other hand, students providing supplementary classes before lockdown were 1.4 times (95% CI: 0.856, 2.227), more likely to show mild to severe anxiety symptoms than their counterparts with no such involvement. Students who were worried about their academic activities were 1.8 times (95% CI: 1.099, 2.883) more likely to exhibit mild to severe anxiety symptoms than students with no such worries. Students living with families were 1.8 times (95% CI: 1.021, 3.308), more likely to have mild to severe anxiety symptoms than students staying away from families during the lockdown.

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https://doi.org/10.1371/journal.pone.0238162.t003

COVID-19 pandemic came out as the most devastating and challenging crisis for public health in the contemporary world. Apart from the soaring mortality rate, nations across the globe have also been suffering from a spike of the excruciating psychological outcomes, i.e., anxiety and depression among people of all ages. University students are no exception, as all the educational institutions are unprecedentedly closed for more than usual, and for Bangladesh, it is more than two months in a row. Such closure, in general, triggers a sense of uncertainty about academic and professional career among the educands and intensifies persistent mental health challenges among university students [ 33 , 35 , 36 ]. Given such circumstances, the main goal of this study was to investigate the prevalence of depression and anxiety among the Bangladeshi university students during the COVID-19 pandemic and to explore the factors influencing the presence of depression and anxiety disorder.

The findings of the web-based cross-sectional survey indicate that more than two-thirds of the students were experiencing mild to severe depression (82.4%) and anxiety (87.7%). Earlier studies in Bangladesh observed the presence of both depression and anxiety among students in higher academia. For example, a survey of medical students in 2015 suggested that more than 50% of students in medical colleges are suffering from depression (54.3%) and anxiety (64.8%) [ 37 ]. Another study, on university students excluding the freshmen, complemented the previous work and found that the prevalence rate of depression and anxiety was 52.2% and 58.1%, respectively [ 38 ]. Compared to the earlier studies, our study suggests that university students in Bangladesh are experiencing an unparalleled growth of depression and anxiety under the current global pandemic situation.

The results also suggest that the university students’ involvement in private tuition is a critical factor in understanding the increased prevalence of depression and anxiety among them. In Bangladesh, a significant number of students are involved in part-time jobs, such as private tuition, to finance their educational expenses, and sometimes to support their families, and their reliance on private tutoring as a part-time job is increasing gradually [ 39 ]. However, being unable to provide tuition under the lockdown situation means disruption of regular income and joblessness. The prolonged unemployment, together with financial insecurity, is the most significant stressors contributing to the increased rates of depression and anxiety among university students in Bangladesh. A study suggests that unemployment is significantly associated with mental and somatic disorders, which could limit the individuals’ chances for feelings of achievement, accomplishment, and satisfaction, and eventually lead to the impairment of psychological functioning [ 40 ]. Self-esteem could also be affected by the loss of work as studies found that lack of family support during unemployment adversely affects the mental well-being of individuals [ 41 , 42 ].

Apparently, the sudden joblessness and financial insecurity are putting the university students in an unpleasant situation, affecting their socioeconomic and mental well-being [ 43 ]. It has been well accepted that living with families strongly generate reassurance among the individuals, therefore, reduce depression and anxiety. Because positive family environments often benefit the mental health of the vulnerable youth experiencing depression or anxiety [ 44 ]. However, this pandemic has brought extreme financial pressure on families. Most of the families have been suffering from unmanageable debts and a decline in income, thus, leaving the family members in a traumatized situation [ 45 , 46 ]. University students, who used to earn and contribute to their families before lockdown, can hardly assist their parents in this crisis moment. The results of this study suggest that despite living with family, anxiety and depressive symptoms have been increasing among university students in Bangladesh mainly due to financial insecurity.

Universities in developed countries put strict health protocols into action, such as washing hand, using face-mask, advising ‘stay-home’ strategy when sick, to facilitate continuation of education in higher academia and later switched to campus-wide online learning [ 47 , 48 ]. In Bangladesh, the protective interventions, such as wearing mask or using the personal protective equipment, are yet to be enforced largely due to limited supplies [ 49 , 50 ], hence, the government opted to implement the country-wide lockdown. Approximately two-thirds of the students are getting depressed thinking they might be falling academically behind their contemporaries in other parts of the world during the prolonged closure of universities. They, however, reiterated that the online classes could not fulfill their requirements [ 51 ] and a significant percentage of the students are still out of the reach of the online class. In addition, their research projects and internships had to be ceased since they were instructed to leave the halls (dormitories for students) of their respective universities [ 3 ]. Not only that, the Covid-19 crisis also created a severe challenge of the global reversion for the graduates to accomplish their future academic and working goals [ 52 ]. Although university closures were intended to keep students safe, for many, these notions came out with different sets of mental health issues.

Meanwhile, a study reported that graduate students generally experience significant amounts of stress and anxiety, which also affects their usual behavior [ 53 ]. The results in this study stressed on the fact that the nation-wide lockdown in Bangladesh is going to cause a significant disruption in the academic programs and create a gap in both teaching and learning. The academic delays could have long-term impacts on the psychology of students as they are more likely to be graduated later than they have expected. In this regard, faculties, as well as university authorities, should stay connected with the students using social media platforms and motivate them to move forward together during this difficult time.

Apart from the issues mentioned above, this study found no significant differences between male and female students with relation to depression or anxiety, thus complement previous studies [ 36 , 37 , 54 ]. However, Egyptian research remarked that female university students are more likely to suffer anxiety and less prone to depression than male students [ 55 ]. The current study did not find any statistically significant association between the socio-demographic variables (including place of residence and exercise) with depression and anxiety. A few studies, on the contrary, reported a significant association between socio-demographic variables [ 37 ] and exercise [ 56 ] with depression and anxiety. A Malaysian study reported substantial differences concerning age and permanent residence with depression or anxiety, however, observed no significant association between some socio-demographic variables (including gender, ethnicity, study major, monthly family income) and the psychological problems [ 36 ].

Strengths and limitations

The strengths and limitations of the current study are determined by several issues. The e-questionnaire allows to assess the prevalence of anxiety and depression among university students while maintaining the WHO recommended “social distance” during the COVID-19 pandemic, which otherwise would be impossible. Moreover, the data for the e-survey were collected by globally validated standardized tools for quantitative analysis. On the contrary, given the limited resources available and the time-sensitivity of the COVID-19 outbreak, the snowball sampling strategy was chosen instead of random samples. In this cross-sectional study, the identified factors are regarded as associated factors, which could be either be the causes or the results of depression or anxiety. Furthermore, due to ethical requirements on anonymity and confidentiality, the contact details of the respondents was not collected. However, the use of validated screening e-questionnaire was considered as a cost-effective approach to explore the situation in general, therefore, used in this study. Since the research methodology could not reach people with medically examined depression and anxiety symptoms, the provision of the results may not fully reflect the severity of depressive and anxiety symptoms among students. Another limitation of this study is not using the tools designed specifically for the COVID-19 pandemic, such as the coronavirus anxiety scale (CAS). Meanwhile, it would be ideal for conducting a prospective study on the same group of participants with tools developed especially for the COVID-19 pandemic after a period to provide a concrete finding and to facilitate the demand for a focused public health initiative.

Despite some limitations, this study gives the first empirical evidence that a large percentage of Bangladeshi university students have been suffering from depression and anxiety symptoms during the ongoing pandemic. In addition to academic and professional uncertainty, financial insecurity is contributing to the rise of depression and anxiety among university students. To minimize the growing mental health problems, the government, along with the universities, should work together to deliver promptly and accurately economy-oriented psychological support to the university students. To ensure the continuous involvement of students in educational processes, the universities should initiate all-inclusive online-based educational programs to reach out the students living in remote areas with or without devices in association with internet-service providers by providing scholarship or student loan. Furthermore, parents should be encouraged, by providing pandemic response and recovery support from the government, to create a friendly and positive family environment for university students without imposing pressure on the future academic and working career.

Supporting information

https://doi.org/10.1371/journal.pone.0238162.s001

Acknowledgments

We are grateful to the participants, as well as thankful to the editors and anonymous reviewers.

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Family and Academic Stress and Their Impact on Students' Depression Level and Academic Performance

1 School of Mechatronics Engineering, Daqing Normal University, Daqing, China

2 School of Marxism, Heilongjiang University, Harbin, China

Jacob Cherian

3 College of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates

Noor Un Nisa Khan

4 Faculty of Business Administration, Iqra University Karachi Pakistan, Karachi, Pakistan

Kalpina Kumari

5 Faculty of Department of Business Administration, Greenwich University Karachi, Karachi, Pakistan

Muhammad Safdar Sial

6 Department of Management Sciences, COMSATS University Islamabad (CUI), Islamabad, Pakistan

Ubaldo Comite

7 Department of Business Sciences, University Giustino Fortunato, Benevento, Italy

Beata Gavurova

8 Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Kosice, Kosice, Slovakia

József Popp

9 Hungarian National Bank–Research Center, John von Neumann University, Kecskemét, Hungary

10 College of Business and Economics, University of Johannesburg, Johannesburg, South Africa

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Current research examines the impact of academic and familial stress on students' depression levels and the subsequent impact on their academic performance based on Lazarus' cognitive appraisal theory of stress. The non-probability convenience sampling technique has been used to collect data from undergraduate and postgraduate students using a modified questionnaire with a five-point Likert scale. This study used the SEM method to examine the link between stress, depression, and academic performance. It was confirmed that academic and family stress leads to depression among students, negatively affecting their academic performance and learning outcomes. This research provides valuable information to parents, educators, and other stakeholders concerned about their childrens' education and performance.

Introduction

Higher education institutions (HEIs) are believed to be one of the strongest pillars in the growth of any nation ( 1 ). Being the principal stakeholder, the performance of HEIs mainly relies on the success of its students ( 2 ). To successfully compete in the prevailing dynamic industrial environment, students are not only supposed to develop their knowledge but are also expected to have imperative skills and abilities ( 3 ). In the current highly competitive academic environment, students' performance is largely affected by several factors, such as social media, academic quality, family and social bonding, etc. ( 4 ). Aafreen et al. ( 2 ) stated that students continuously experience pressure from different sources during academic life, which ultimately causes stress among students.

Stress is a common factor that largely diminishes individual morale ( 5 ). It develops when a person cannot handle their inner and outer feelings. When the stress becomes chronic or exceeds a certain level, it affects an individual's mental health and may lead to different psychological disorders, such as depression ( 6 ). Depression is a worldwide illness marked by feelings of sadness and the inability to feel happy or satisfied ( 7 ). Nowadays, it is a common disorder, increasing day by day. According to the World Health Organization ( 8 , 9 ), depression was ranked third among the global burden of disease and predicted to take over first place by 2030.

Depression leads to decreased energy, difficulty thinking, concentrating, and making career decisions ( 6 ). Students are a pillar of the future in building an educated society. For them, academic achievement is a big goal of life and can severely be affected if the students fall prey to depression ( 10 , 11 ). There can be several reasons for this: family issues, exposure to a new lifestyle in colleges and universities, poor academic grades, favoritism by teachers, etc. Never-ending stress or academic pressure of studies can also be a chief reason leading to depression in students ( 12 ). There is a high occurrence of depression in emerging countries, and low mental health literacy has been theorized as one of the key causes of escalating rates of mental illness ( 13 ).

Several researchers, such as ( 6 , 14 , 15 ) have studied stress and depression elements from a performance perspective and reported that stress and depression negatively affect the academic performance of students. However, Aafreen et al. ( 2 ) reported contradictory results and stated that stress sharpens the individual's mind and reflexes and enables workers to perform better in taxing situations. Ardalan ( 16 ) conducted a study in the United States (US). They reported that depression is a common issue among students in the US, and 20 percent of them may have a depressive disorder spanning 12 months or more. It affects students' mental and physical health and limits their social relationships and professional career.

However, the current literature provides mixed results on the relationship between stress and performance. Therefore, the current research investigates stress among students from family and academic perspectives using Lazaru's theory which describes stress as a relation between an individual and his environment and examines how it impacts students' depression level, leading to their academic performance. Most of the available studies on stress and depression are from industrial perspectives, and limited attention is paid to stress from family and institutional perspectives and examines its impact on students' depression level, leading to their academic performance, particularly in Pakistan, the place of the study. Besides, the present study follows a multivariate statistical technique, followed by structural equation modeling (SEM) to examine the relationship between stated variables which is also a study's uniqueness.

This paper is divided into five main sections. The current section provided introduction, theoretical perspective, and background of the study. In the second section, a theoretical framework, a detailed literature review and research hypotheses of the underlying relationships are being proposed. In the third and fourth section, methodology and analysis have been discussed. Finally, in the last section, the conclusion, limitations, implications, and recommendations for future research have been proposed.

Theory and Literature

The idea of cognitive appraisal theory was presented in 1966 by psychologist Richard Lazarus in Psychological Stress and Coping Process. According to this theory, appraisal and coping are two concepts that are central to any psychological stress theory. Both are interrelated. According to the theory, stress is the disparity between stipulations placed on the individuals and their coping resources ( 17 ). Since its first introduction as a comprehensive theory ( 18 ), a few modifications have been experienced in theory later. The recent adaptation states that stress is not defined as a specific incitement or psychological, behavioral, or subjective response. Rather, stress is seen as a relation between an individual and his environment ( 19 ). Individuals appraise the environment as significant for their well-being and try to cope with the exceeding demands and challenges.

Cognitive appraisal is a model based on the idea that stress and other emotional processes depend on a person's expectancies regarding the significance and outcome of an event, encounter, or function. This explains why there are differences in intensity, duration, and quality of emotions elicited in people in response to the environment, which objectively, are equal for all ( 18 ). These appraisals may be influenced by various factors, including a person's goals, values, motivations, etc., and are divided into primary and secondary appraisals, specific patterns of which lead to different kinds of stress ( 20 ). On the other hand, coping is defined as the efforts made by a person to minimize, tolerate, or master the internal and external demands placed on them, a concept intimately related to cognitive appraisal and, therefore, to the stress-relevant person-environment transactions.

Individuals experience different mental and physiological changes when encountering pressure, such as stress ( 21 , 22 ). The feelings of stress can be either due to factors in the external environment or subjective emotions of individuals, which can even lead to psychological disorders such as anxiety and depression. Excess stress can cause health problems. A particularly negative impact has been seen in students due to the high level of stress they endure, affecting their learning outcomes. Various methods are used to tackle stress. One of the methods is trying to pinpoint the causes of stress, which leads us to different terms such as family stress and academic stress. The two factors, stress and depression, have greatly impacted the students' academic performances. This research follows the Lazarus theory based on stress to examine the variables. See the conceptual framework of the study in Figure 1 .

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Conceptual framework.

Academic Stress

Academic issues are thought to be the most prevalent source of stress for college students ( 23 ). For example, according to Yang et al. ( 24 ), students claimed that academic-related pressures such as ongoing study, writing papers, preparing for tests, and boring professors were the most important daily problems. Exams and test preparation, grade level competitiveness, and gaining a big quantity of knowledge in a short period of time all contribute to academic pressure. Perceived stress refers to a condition of physical or psychological arousal in reaction to stressors ( 25 , 26 ). When college students face excessive or negative stress, they suffer physical and psychological consequences. Excessive stress can cause health difficulties such as fatigue, loss of appetite, headaches, and gastrointestinal issues. Academic stress has been linked to a variety of negative effects, including ill health, anxiety, depression, and poor academic performance. Travis et al. ( 27 ), in particular, discovered strong links between academic stress and psychological and physical health.

Family Stress

Parental participation and learning effect how parents treat their children, as well as how they handle their children's habits and cognitive processes ( 28 ). This, in turn, shapes their children's performance and behaviors toward them. As a result, the parent-child relationship is dependent on the parents' attitudes, understanding, and perspectives. When parents have positive views, the relationship between them and their children will be considerably better than when they have negative attitudes. Parents respond to unpleasant emotions in a variety of ways, which can be classified as supportive or non-supportive ( 29 ). Parents' supportive reactions encourage children to explore their emotions by encouraging them to express them or by assisting them in understanding and coping with an emotion-eliciting scenario. Non-supportive behaviors, such as downplaying the kid's emotional experience, disciplining the child, or getting concerned by the child's display, transmit the child the message that expressing unpleasant emotions is inappropriate and unacceptable. Supportive parental reactions to unpleasant emotions in children have been linked to dimensions of emotional and social competence, such as emotion comprehension and friendship quality. Non-supportive or repressive parental reactions, on the other hand, have been connected to a child's stored negative affect and disordered behaviors during emotion-evoking events, probably due to an inability or unwillingness to communicate unpleasant sentiments ( 30 , 31 ).

Academic Stress and Students' Depression Levels

Generally, it is believed that mental health improves as we enter into adulthood, and depression disorder starts to decline between the age of 18 and 25. On the other hand, excessive depression rates are the highest pervasiveness during this evolution ( 15 ), and many university students in the particular screen above clinical cut-off scores for huge depression ( 14 , 32 ). Afreen et al. ( 2 ) stated that 30% of high school students experience depression from different perspectives. This means a major chunk of fresh high school graduates are more likely to confront depression or are more vulnerable to encountering depression while enrolling in the university. As the students promote to a higher level of education, there are many factors while calculating the stress like, for example, the syllabus is tough to comprehend, assignments are quite challenging with unrealistic deadlines, and accommodation problems for the students who are shifted from other cities, etc. ( 33 ). Experiences related to university can also contribute while studying depression. The important thing to consider is depression symptoms vary from time to time throughout the academic years ( 34 ); subjective and objective experiences are directly connected to the depression disorder ( 6 ), stress inherent in the university situation likely donates to the difference in university students' depressing experiences.

Stress negatively impacts students' mental peace, and 42.3% of students of Canadian university respondents testified devastating levels of anxiety and stress ( 35 , 36 ). Moreover, there were (58.1%) students who stated academic projects are too tough to handle for them. In Germany, Bulgaria, and Poland, a huge sample of respondents consider assignments a burden on their lives that cannot stand compared to relationships or any other concern in life ( 14 ).

In several countries, university students were studied concerning stress, and results show that depression disorder and apparent anxiety are correlated to educational needs and demands ( 37 ). In their cross-sectional study conducted on a sample of 900 Canadian students, Lörz et al. ( 38 ) concluded that strain confronted due to academic workload relatively has high bleak symptoms even after controlling 13 different risk affecting factors for depression (e.g., demographic features, abusive past, intellectual way, and personality, currently experienced stressful trials in life, societal support). Few have exhibited that students who are tired of educational workload or the students who name them traumatic tend to have more depressing disorders ( 15 ).

These relations can be described by examining the stress and coping behaviors that highlight the role of positive judgments in the stress times ( 39 ), containing the Pancer and colleagues' university modification framework ( 40 , 41 ). The evaluation concept includes examining the circumstances against the available resources, for instance, the effectiveness of coping behavior and societal support. As per these frameworks, if demand is considered unapproachable and resources are lacking, confronted stress and interrelated adverse effects will be high, conceivably giving birth to difficulties in an adjustment like mental instability. Stress triggering situations and the resources in the educational area led to excessive workload, abilities, and study and enhanced time managing skills.

Sketching the overall evaluation frameworks, Pancer et al. ( 40 ) established their framework to exhibit the constructive and damaging adjustment results for the university students dealing with the academic challenges. They stated that while students enroll in the university, they evaluate all the stress-related factors that students confront. They consider them manageable as long as they have sufficient resources. On the other hand, if the available resources do not match the stress factors, it will surely result in a negative relationship, which will lead students to experience depression for sure. Based on the given arguments, the researcher formulates the following hypothesis:

  • H1: Increased academic stress results in increased depression levels in students.

Family Stress and Students' Depression Levels

According to Topuzoglu et al. ( 42 ), 3% to 16.9% of individuals are affected by depression worldwide. There are fewer chances for general people to confront depression than university students ( 43 , 44 ). In Mirza et al.'s ( 45 ) study, 1/3 of students encounter stress and depression (a subjective mean occurrence of 30.6%) of all participant students, which suggests students have a 9% higher rate of experiencing depression than general people. Depression can destroy life; it greatly impacts living a balanced life. It can impact students' personal and social relationships, educational efficiency, quality of life, affecting their social and family relationships, academic productivity, and bodily operations ( 46 , 47 ). This declines their abilities, and they get demotivated to learn new things, resulting in unsatisfactory performances, and it can even result in university dropouts ( 48 ). Depression is a continuous substantial risk aspect for committing suicide for university students ( 49 ); thus, it is obliged to discover the factors that can give rise to students' depression.

Seventy-five percentage of students in China of an intermediate school are lucky enough to enroll in higher education. The more students pursue higher education, the more they upsurge for depression (in 2002, the depression rate was 5 to 10%, 2011 it rises 24 to 38%) ( 5 ). Generally, University students' age range is late teens to early twenties, i.e., 18–23 years. Abbas ( 50 ) named the era of university students as “post-adolescence. Risk factors for teenage depression have several and complicated problems of individual characteristics and family and educational life ( 51 ). Amongst the huge depression factors, relationship building with family demands a major chunk of attention and time since factors like parenting and family building play an important role in children's development ( 52 , 53 ). Halonen et al. ( 54 ) concluded that factors like family binding play a major role in development, preservation, and driving adolescent depression. Generally speaking, depressed teenagers tend to have a weaker family relationship with their parents than non-depressed teenagers.

There are two types of family risk factors, soft and hard. Hard factors are encountered in families with a weak family building structure, parents are little to no educated at all, and of course, the family status (economically). Several studies have proved that students of hard risk factors are more likely to encounter depression. Firstly, students from broken families have low confidence in every aspect of life, and they are weak at handling emotional breakdowns compared to students from complete and happy families ( 55 – 57 ). Secondly, the university students born in educated families, especially mothers (at least a college degree or higher degree), are less likely to confront depression than the university students born in families with little to no educated families. Secondly, children born with educated mothers or mothers who at least have a college degree tend to be less depressive than the children of less-educated mothers ( 58 ). However, Parker et al. and Mahmood et al. ( 59 , 60 ) stated a strong relationship between depression and mothers with low literacy levels.

On the other hand, Chang et al. ( 46 ) couldn't prove the authentication of this relationship in university students. Thirdly, university students who belong to lower class families tend to have more unstable mental states and are more likely to witness depression than middle or upper-class families ( 61 ). Jadoon et al. and Abbas et al. ( 62 , 63 ) said that there is no link between depression and economic status. Their irrelevance can be because medical students often come from educated and wealthy families and know their jobs are guaranteed as soon as they graduate. Therefore, the relationship between the hard family environment and depression can be known by targeting a huge audience, and there are several factors to consider while gauging this relationship.

The soft family environment is divided into clear factors (parenting style example, family guidelines, rules, the parent with academic knowledge, etc.) and implied factors (family norm, parent-child relationship, communication within the family, etc.). The soft factor is the key factor within the family that cannot be neglected while studying the teenagers' mental state or depression. Families make microsystems within the families, and families are the reason to build and maintain dysfunctional behavior by multiple functional procedures ( 64 ). Amongst the soft family environmental factors, consistency and struggles can be helpful while forecasting the mental health of teenagers. The youth of broken families, family conflict, weak family relationships, and marital issues, especially unhappy married life, are major factors for youth depression ( 65 ). Ruchkin et al. ( 66 ) stated that African Americans usually have weak family bonding, and their teenagers suffer from depression even when controlling for source bias. Whereas, few researchers have stated, family unity is the most serious factor while foreseeing teenagers' depression. Eaton noted that extreme broken family expressions might hurt emotionality and emotional regulation ( 67 , 68 ).

Social circle is also considered while studying depression in teenagers ( 69 – 71 ). The traditional Pakistani culture emphasizes collectivism and peace and focuses on blood relations and sensitive sentiments. Adolescents with this type of culture opt to get inspired by family, but students who live in hostels or share the room with other students lose this family inspiration. This transformation can be a big risk to encounter depression ( 72 ). Furthermore, in Pakistan securing employment is a big concern for university students. If they want a good job in the future, they have to score good grades and maintain GPA from the beginning. They have to face different challenges all at once, like aggressive educational competition, relationships with peers and family, and of course the biggest employment stress all alone. The only source for coping with these pressures is the family that can be helpful for fundings. If the students do not get ample support the chances are of extreme depression. The following hypothesis is suggested:

  • H2: Increased family stress level results in increased depression levels in students.

Students' Depression Levels and Students' Academic Performance

University students denote many people experiencing a crucial conversion from teenagers to adulthood: a time that is generally considered the most traumatic time in one's ( 73 ). This then gets accumulated with other challenges like changes in social circle and exams tension, which possibly puts students' mental health at stake. It has been concluded that one-third of students experience moderate to severe depression in their entire student life ( 74 ). This is the rate that can be increased compared to the general people ( 75 , 76 ). Students with limited social-class resources tend to be more helpless. Additionally, depressed students in attainable-focused environments (for instance, higher academic institutes) are likely to score lower grades with a sense of failure and more insufficient self-assurance because they consider themselves failures, find the world unfair, and have future uncertainties. Furthermore, students with low self-esteem are rigid to take on challenging assignments and projects, hence they are damaging their educational career ( 77 ).

Depression can be defined as a blend of physical, mental, bodily processes, and benightedness which can make themselves obvious by symptoms like, for example, poor sleep schedule, lack of concentration, ill thoughts, and state of remorse ( 78 , 79 ). But, even after such a huge number of depressions in students and the poor academic system, research has not explored the effect of depression on educational performance. A study has shown that the relationship between emotional stability and academic performance in university students and financial status directly results in poor exam performance. As the study further concluded, it was verified depression is an independent factor ( 80 ). Likewise, students suffering from depression score poor grades, but this relationship vanished if their depression got treated. Apart from confidence breaking, depression is a big failure for their academic life. Students with depression symptoms bunk more classes, assessments, and assignments. They drop courses if they find them challenging than non-depressed peers, and they are more likely to drop out of university completely ( 81 ). Students suffering from depression can become ruthless, ultimately affecting their educational performance and making them moody ( 82 ).

However, it has been stated that the association between anxiety and educational performance is even worse and ambiguous. At the same time, some comprehensive research has noted that the greater the anxiousness, the greater the student's performance. On the other hand, few types of research have shown results where there is no apparent relationship between anxiety and poorer academic grades ( 83 ). Ironically, few studies have proposed that a higher anxiety level may improve academic performance ( 84 , 85 ). Current research by Khan et al. ( 86 ) on the undergraduate medical students stated that even though the high occurrence of huge depression between the students, the students GPA is unharmed. Therefore, based on given differences in various research findings, this research is supposed to find a more specific and clear answer to the shared relationship between students' depression levels and academic performance. Based on the given arguments, the researcher formulates the following hypothesis:

  • H3: Students' depression level has a significant negative effect on their academic performance.

Methodology

Target population and sampling procedure.

The target audience of this study contains all male and female students studying in the public, private, or semi-government higher education institutions located in Rawalpindi/Islamabad. The researchers collected data from undergraduate and postgraduate students from the management sciences, engineering, and computer science departments. The sampling technique which has been used is the non-probability sampling technique. A questionnaire was given to the students, and they were requested to fill it and give their opinion independently. The questionnaire is based on five points Likert scale.

However, stress and depression are the most common issue among the students, which affects their learning outcomes adversely. A non-probability sampling technique gathered the data from February 2020 to May 2020. The total questionnaires distributed among students were 220, and 186 responses were useful. Of which 119 respondents were females, 66 males, and 1 preferred not to disclose. See Table 1 for detailed demographic information of respondents.

Respondent's demographic profile.

Measurement Scales

We have divided this instrument into two portions. In the first section, there is demographic information of respondents. The second section includes 14 items based on family stress, academic stress, students' depression levels, and students' academic performance. Academic and family stress were measured by 3 item scale for each construct, and students' depression level and academic performance were measured by 4 item scale for each separate construct. The five-point Likert scale is used to measure the items, in which one signifies strongly disagree (S.D), second signifies disagree (D.A), third signifies neither agree nor disagree (N), fourth signifies agree (A.G), and the fifth signifies strongly agree (S.A). The questionnaire has been taken from Gold Berg ( 87 ), which is modified and used in the given questionnaire.

Data Analysis and Results

The researchers used the SEM technique to determine the correlation between stress, depression, and academic performance. According to Prajogo and Cooper ( 88 ), it can remove biased effects triggered by the measurement faults and shape a hierarchy of latent constructs. SPSS v.23 and AMOS v.23 have been used to analyze the collected data. Kaiser-Meyer-Olkin test is used to test the competence of the sample. The value obtained is 0.868, which fulfills the Kaiser et al. ( 89 ), a minimum requirement of 0.6. The multicollinearity factor was analyzed through the variance inflation factor (VIF). It shows the value of 3.648 and meets the requirement of Hair et al. ( 90 ), which is < 4. It also indicates the absence of multicollinearity. According to Schwarz et al. ( 91 ), common method bias (CMB) is quite complex in quantitative studies. Harman's test of a single factor has been used to analyze CMB. The result obtained for the single factor is 38.63%. As stated by Podsakoff et al. ( 92 ), if any of the factors gives value < 50% of the total variance, it is adequate and does not influence the CMB. Therefore, we can say that there is no issue with CMB. Considering the above results are adequate among the measurement and structural model, we ensure that the data is valued enough to analyze the relation.

Assessment of the Measurement and Structural Model

The association between the manifest factors and their elements is examined by measuring model and verified by the Confirmatory Factor Analysis (CFA). CFA guarantees legitimacy and the unidimensional of the measurement model ( 93 ). Peterson ( 94 ) stated that the least required, i.e., 0.8 for the measurement model, fully complies with its Cronbach's alpha value, i.e., 0.802. Therefore, it can confidently be deduced that this measurement model holds satisfactory reliability. As for the psychological legitimacy can be analyzed through factor loading, where the ideal loading is above 0.6 for already established items ( 95 ). Also, according to the recommendation of Molina et al. ( 96 ), the minimum value of the average variance extracted (AVE) for all results is supposed to be >0.5. Table 2 gives detail of the variables and their quantity of things, factor loading, merged consistency, and AVE values.

Instrument reliability and validity.

A discriminant validity test was performed to ensure the empirical difference of all constructs. For this, it was proposed by Fornell and Larcker ( 97 ) that the variance of the results is supposed to be greater than other constructs. The second indicator of discriminant validity is that the square root values of AVE have a greater correlation between the two indicators. Hair et al. ( 90 ) suggested that the correlation between the pair of predictor variables should not be higher than 0.9. Table 3 shows that discriminant validity recommended by Hair et al. ( 90 ) and Fornell and Larcker ( 97 ) was proved clearly that both conditions are fulfilled and indicates that the constructs have adequate discriminant validity.

Discriminant validity analysis.

Acd. Strs, Academic Stress; Fam. Strs, Family Stress; Std. Dep. Lev, Student's Depression Level; Std. Acd. Perf, Student's Academic Performance .

Kaynak ( 98 ) described seven indicators that ensure that the measurement model fits correctly. These indicators include standardized root mean squared residual (SRMR), root means a square error of approximation (RMSEA), comparative fit index (CFI), normative fit index (NFI), adjusted goodness of fit index (AGFI), the goodness of fit index (GFI) and chi-square to a degree of freedom (x 2 /DF). Tucker-Lewis's index (TLI) is also included to ensure the measurement and structural model's fitness. In the measurement model, the obtained result shows that the value of x 2 /DF is 1.898, which should be lower than 2 suggested by Byrne ( 99 ), and this value also meets the requirement of Bagozzi and Yi ( 100 ), i.e., <3. The RMSEA has the value 0.049, which fully meets the requirement of 0.08, as stated by Browne and Cudeck ( 101 ). Furthermore, the SRMR acquired value is 0.0596, which assemble with the required need of < 0.1 by Hu and Bentler ( 102 ). Moreover, according to Bentler and Bonett ( 103 ), McDonald and Marsh ( 104 ), and Bagozzi and Yi ( 100 ), the ideal value is 0.9, and the values obtained from NFI, GFI, AGFI, CFI, and TLI are above the ideal value.

Afterward, the structural model was analyzed and achieved the findings, which give the value of x 2 /DF 1.986. According to Browne and Cudeck ( 101 ), the RMSEA value should not be greater than 0.08, and the obtained value of RMSEA is 0.052, which meets the requirement perfectly. The minimum requirement of Hu and Bentler ( 102 ) should be <0.1, for the structural model fully complies with the SRMR value 0.0616. According to a recommendation of McDonald and Marsh ( 104 ) and Bagozzi and Yi ( 100 ), the ideal value must be up to 0.9, and Table 4 also shows that the values of NFI, GFI, AGFI, CFI, and TLI, which are above than the ideal value and meets the requirement. The above results show that both the measurement and structural models are ideally satisfied with the requirements and the collected data fits correctly.

Analysis of measurement and structural model.

Testing of Hypotheses

The SEM technique is used to examine the hypotheses. Each structural parameter goes along with the hypothesis. The academic stress (Acd. Strs) with the value β = 0.293 while the p -value is 0.003. These outcomes show a significant positive relationship between academic stress (Acd. Strs) and students' depression levels (Std. Dep. Lev). With the β = 0.358 and p = 0.001 values, the data analysis discloses that the family stress (Fam. Strs) has a significant positive effect on the students' depression level (Std. Dep. Lev). However, the student's depression level (Std. Dep. Lev) also has a significant negative effect on their academic performance (Std. Acd. Perf) with the values of β = −0.319 and p = 0.001. Therefore, the results supported the following hypotheses H 1 , H 2 , and H 3 . The sub-hypotheses analysis shows that the results are statistically significant and accepted. In Table 5 , the details of the sub-hypotheses and the principals are explained precisely. Please see Table 6 to review items with their mean and standard deviation values. Moreover, Figure 2 represents the structural model.

Examining the hypotheses.

Description of items, mean, and standard deviation.

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Structural model.

Discussion and Conclusion

These findings add to our knowledge of how teenage depression is predicted by academic and familial stress, leading to poor academic performance, and they have practical implications for preventative and intervention programs to safeguard adolescents' mental health in the school context. The outcomes imply that extended academic stress positively impacts students' depression levels with a β of 0.293 and a p -value sof 0.003. However, according to Wang et al. ( 5 ), a higher level of academic stress is linked to a larger level of school burnout, which leads to a higher degree of depression. Satinsky et al. ( 105 ) also claimed that university officials and mental health specialists have expressed worry about depression and anxiety among Ph.D. students, and that his research indicated that depression and anxiety are quite common among Ph.D. students. Deb et al. ( 106 ) found the same results and concluded that depression, anxiety, behavioral difficulties, irritability, and other issues are common among students who are under a lot of academic stress. Similarly, Kokou-Kpolou et al. ( 107 ) revealed that depressive symptoms are common among university students in France. They also demonstrate that socioeconomic and demographic characteristics have a role.

However, Wang et al. ( 5 ) asserted that a higher level of academic stress is associated with a higher level of school burnout, which in return, leads to a higher level of depression. Furthermore, Satinsky et al. ( 105 ) also reported that university administrators and mental health clinicians have raised concerns about depression and anxiety and concluded in his research that depression and anxiety are highly prevalent among Ph.D. students. Deb et al. ( 106 ) also reported the same results and concluded that Depression, anxiety, behavioral problems, irritability, etc. are few of the many problems reported in students with high academic stress. Similary, Kokou-Kpolou et al. ( 107 ) confirmed that university students in France have a high prevalence of depressive symptoms. They also confirm that socio-demographic factors and perceived stress play a predictive role in depressive symptoms among university students. As a result, academic stress has spread across all countries, civilizations, and ethnic groups. Academic stress continues to be a serious problem impacting a student's mental health and well-being, according to the findings of this study.

With the β= 0.358 and p = 0.001 values, the data analysis discloses that the family stress (Fam. Strs) has a significant positive effect on the students' depression level (Std. Dep. Lev). Aleksic ( 108 ) observed similar findings and concluded that many and complicated concerns of personal traits, as well as both home and school contexts, are risk factors for teenage depression. Similarly, Wang et al. ( 109 ) indicated that, among the possible risk factors for depression, family relationships need special consideration since elements like parenting styles and family dynamics influence how children grow. Family variables influence the onset, maintenance, and course of juvenile depression, according to another study ( 110 ). Depressed adolescents are more likely than normal teenagers to have bad family and parent–child connections.

Conversely, students' depression level has a significantly negative impact on their academic performance with β and p -values of −0.319 and 0.001. According ( 111 ), anxiety and melancholy have a negative influence on a student's academic performance. Adolescents and young adults suffer from depression, which is a common and dangerous mental illness. It's linked to an increase in family issues, school failure, especially among teenagers, suicide, drug addiction, and absenteeism. While the transition to adulthood is a high-risk period for depression in general ( 5 ), young people starting college may face extra social and intellectual challenges that increase their risk of melancholy, anxiety, and stress ( 112 ). Students' high rates of depression, anxiety, and stress have serious consequences. Not only may psychological morbidity have a negative impact on a student's academic performance and quality of life, but it may also disturb family and institutional life ( 107 ). Therefore, long-term untreated depression, anxiety, or stress can have a negative influence on people's ability to operate and produce, posing a public health risk ( 113 ).

Theoretical Implications

The current study makes various contributions to the existing literature on servant leadership. Firstly, it enriches the limited literature on the role of family and academic stress and their impact on students' depression levels. Although, a few studies have investigated stress and depression and its impact on Students' academic performance ( 14 , 114 ), however, their background i.e., family and institutions are largely ignored.

Secondly, it explains how the depression level impacts students' academic learning, specifically in the Asian developing countries region. Though a substantial body of empirical research has been produced in the last decade on the relationship between students' depression levels and its impact on their academic achievements, however, the studies conducted in the Pakistani context are scarce ( 111 , 115 ). Thus, this study adds further evidence to prior studies conducted in different cultural contexts and validates the assumption that family and academic stress are key sources depression and anxiety among students which can lead toward their low academic grades and their overall performance.

This argument is in line with our proposed theory in the current research i.e., cognitive appraisal theory which was presented in 1966 by psychologist Richard Lazarus. Lazarus's theory is called the appraisal theory of stress, or the transactional theory of stress because the way a person appraises the situation affects how they feel about it and consequently it's going to affect his overall quality of life. In line with the theory, it suggests that events are not good or bad, but the way we think about them is positive or negative, and therefore has an impact on our stress levels.

Practical Implications

According to the findings of this study, high levels of depressive symptoms among college students should be brought to the attention of relevant departments. To prevent college student depression, relevant departments should improve the study and life environment for students, try to reduce the generation of negative life events, provide adequate social support for students, and improve their cognitive and coping capacities to improve their mental qualities.

Stress and depression, on the other hand, may be managed with good therapy, teacher direction, and family support. The outcomes of this study provide an opportunity for academic institutions to address students' psychological well-being and requirements. Emotional well-being support services for students at Pakistan's higher education institutions are lacking in many of these institutions, which place a low priority on the psychological requirements of these students. As a result, initiatives that consistently monitor and enhance kids' mental health are critical. Furthermore, stress-reduction treatments such as biofeedback, yoga, life-skills training, mindfulness meditation, and psychotherapy have been demonstrated to be useful among students. Professionals in the sector would be able to adapt interventions for pupils by understanding the sources from many spheres.

Counseling clinics should be established at colleges to teach students about stress and sadness. Counselors should instill in pupils the importance of positive conduct and decision-making. The administration of the school should work to create a good and safe atmosphere. Furthermore, teachers should assume responsibility for assisting and guiding sad pupils, since this will aid in their learning and performance. Support from family members might also help you get through difficult times.

Furthermore, these findings support the importance of the home environment as a source of depression risk factors among university students, implying that family-based treatments and improvements are critical in reducing depression among university students.

Limitations and Future Research Implications

The current study has a few limitations. The researcher gathered data from the higher education level of university students studying in Islamabad and Rawalpindi institutions. In the future, researchers are required to widen their region and gather information from other cities of Pakistan, for instance, Lahore, Karachi, etc. Another weakness of the study is that it is cross-sectional in nature. We need to do longitudinal research in the future to authoritatively assert the cause-and-effect link between academic and familial stress and their effects on students' academic performance since cross-sectional studies cannot establish significant cause and effect relationships. Finally, the study's relatively small sample size is a significant weakness. Due to time and budget constraints, it appears that the capacity to perform in-depth research of all firms in Pakistan's pharmaceutical business has been limited. Even though the findings are substantial and meaningful, the small sample size is predicted to limit generalizability and statistical power. This problem can be properly solved by increasing the size of the sample by the researchers, in future researches.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

All authors contributed to conceptualization, formal analysis, investigation, methodology, writing and editing of the original draft, and read and agreed to the published version of the manuscript.

This work was funded by the 2020 Heilongjiang Province Philosophy and Social Science Research Planning Project on Civic and Political Science in Universities (Grant No. 20SZB01). This work is supported by the Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic and the Slovak Academy Sciences as part of the research project VEGA 1/0797/20: Quantification of Environmental Burden Impacts of the Slovak Regions on Health, Social and Economic System of the Slovak Republic.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

Authors would like to thank all persons who directly or indirectly participated in the completion of this manuscript.

Anxiety and Depression Among Students Essay

COVID-19 negatively impacted university students in various ways: economically, socially, and psychologically. The study by Islam et al. (2020) aimed at examining the prevalence of anxiety and depression among students. Additionally, the researchers investigated the determinants of anxiety and depression among the students.

The study involved university students in Bangladesh, who were subject to a set of questions. The researchers collected data from a total of 476 university students who responded to online questionnaires. The researchers adopted a correlational model and analyzed the collected data on three consecutive levels: univariate, bivariate, and multivariate. According to the study, university students experienced heightened depression and anxiety during the COVID-19 pandemic. Moreover, older students were experiencing greater depression and anxiety than younger ones.

Although research by Islam et al. (2020) has various strengths, it is limited in several ways. The researchers utilized globally validated standardized tools for quantitative analysis. For instance, the use of frequency tabulation and binary logistics regressions helped in accurately identifying variables that influence anxiety and depression. Additionally, the study was conducted during the COVID-19 period, collecting accurate and real-time data from the respondents. Therefore, the study results were accurate and can be used to back up any other research on the overarching topic. Meanwhile, the research was limited to the number of participants who came from Bangladesh and not any other country. Moreover, the study was conducted during COVID-19 with limited resources. The limitations make it difficult to use the study results in research conducted outside Bangladesh.

The article by Islam et al. (2020) is useful for my research area of interest. The study has relevant data that I can use to compare the prevalence of anxiety and depression between university male students and females. Additionally, the source has accurate results that I can apply to my research in discussing the relationship between COVID-19 and psychological disorders. The researchers utilized quantitative methodology which enhances data objectivity.

The method applies to my research since it would allow me to make my study more accurate by relying on concrete numbers and fewer variables. I can use the quantitative method to analyze data from a large sample size. The study will guide my future research since it provides background information on anxiety and depression among university students. Therefore, my future research will focus on university students in general, not from a specific country.

Islam, Md. A., Barna, S. D., Raihan, H., Khan, Md. N. A., & Hossain, Md. T. (2020). Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey . PLOS ONE , 15 (8), e0238162. Web.

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depression among university students essay

Study shows combining joyful activities with 'savoring' therapy shows positive mental health results among young people

W ith rates of depression rising among young people on university campuses, a team of SMU researchers found that combining two different therapeutic approaches demonstrated effectiveness in improving students' overall mental health. Their findings show that students receiving behavioral activation (BA) therapy augmented with savoring (S) experienced improvements in positive and negative mood.

Behavioral activation is a therapeutic approach that alleviates depression by increasing engagement in meaningful activities, while savoring focuses on increasing one's capacity to savor enjoyable experiences. The research team found favorable results in their participants who used the combination of approaches when compared to another group using emotional awareness (EA), which requires the participants to observe, monitor and reflect upon their positive and negative moods.

The study, published in the journal Behaviour Research and Therapy , involved 60 students who were experiencing a lack of joy or enthusiasm (known as low positive affect or anhedonia). Researchers sought to see if using combined BA + S therapy compared to EA aided participants in achieving positive emotions (or high positive affect). Students participated in two online therapy sessions and completed daily mood surveys on their cell phones.

Those receiving BA + S were asked to choose enjoyable activities from a list and plan to do them daily. They were also given guidance on how to savor those activities and remember what they enjoyed about them.

After carrying out the activities, participants discussed how doing the activities and savoring made them feel and were encouraged to focus on the positive aspects. Students reported feeling happier each day of the study and were also given ways to use BA + S methods in the future, to possibly continue experiencing positive effect.

Those receiving EA were encouraged to notice their feelings and to think about them, both good and bad. The students reported no positive affect improvements.

"Behavioral activation has been around for decades and used to treat depression," said Alicia E Meuret, director of the Anxiety and Depression Research Center at SMU and the senior author.

"What's new is the focus on improving positivity instead of reducing negative feedings. Adding savoring, further pushes people to pay attention to what is in these enjoyable activities that make them feel better. The activity then becomes more salient in their memory and makes it easier for them to feel anticipatory reward or excitement."

Depression in university students is associated with decreased academic performance, a greater likelihood of dropping out and a decrease in quality of life. Additionally, accessing mental health treatments on many college campuses can be challenging due to limited resources, growing waitlists, session limits and the need for outside referrals. One of the benefits of using online BA + S therapy is its ease of accessibility compared to in-person therapy.

"BA + S could help students feel better as a stand-alone strategy or while they wait for traditional treatment," said lead author Divya Kumar, who earned her doctorate in clinical psychology under Meuret at SMU and is now a postdoctoral fellow at Harvard/McLean Hospital.

"Because there are challenges in accessibility to mental health care, finding ways to provide brief and online therapy interventions continues to gain momentum, especially if those methods are targeting positive emotions as well as negative ones."

Additional research team members include SMU doctoral student Sarah Corner and data scientist Richard Kim.

More information: Divya Kumar et al, A randomized controlled trial of brief behavioral activation plus savoring for positive affect dysregulation in university students, Behaviour Research and Therapy (2024). DOI: 10.1016/j.brat.2024.104525

Provided by Southern Methodist University

Credit: Unsplash/CC0 Public Domain

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Spring 2024 Symposium of Student Scholars

How Long Is the Essay Supposed to Be, Professor? A Literature Review of Feasible Writing Expectations for University Students and In-Class Exams

Kris Duah , Kennesaw State University Follow Kenneth White , Kennesaw State University Follow

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Educational Assessment, Evaluation, and Research | Educational Methods | Scholarship of Teaching and Learning

Abstract (300 words maximum)

What is a reasonable amount of writing, in terms of number of paragraphs, that a professor can expect from university students during an hour-long, in-class essay exam? This study is a literature review that attempts to answer this question of how much or how fast a student can be expected to write during an in-class, timed exam. The goal is to summarize previous scholarship on this question using education databases available through Kennesaw State University's library system—particularly, ERIC (Educational Resources Information Center). Search terms include: “written exams,” “essay writing,” “writing speed,” “teacher expectations,” “test expectations,” and “student performance” among others. This study recapitulates existing literature on writing speed and exam expectations to determine a reasonable standard for how much students can be expected to write during an in-class exam. This information could help both students and instructors manage their expectations of writing quantity during timed, in-class exams.

Academic department under which the project should be listed

RCHSS - Sociology & Criminal Justice

Primary Investigator (PI) Name

Kenneth White

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