Everything Was Disrupted: Chronotype and Mental Health Among Indonesian University Students Before and During the COVID-19 Pandemic

Article information

Sleep Med Res. 2026;17(1):83-89

Publication date (electronic) : 2026 March 31

doi : https://doi.org/10.17241/smr.2025.03237

Richard Palilingan, MErg¹, Melky Pangemanan, MSc¹, Ellen Lomboan, PhD², Christian Pajung, MSc¹, Alva Supit, MD, PhD^,1,3

¹Department of Public Health, Universitas Negeri Manado, Tondano, Indonesia

²Department of Sport Science, Universitas Negeri Manado, Tondano, Indonesia

³Sam Ratulangi General Hospital, Tondano, Indonesia

Corresponding Author Alva Supit, MD, PhD Department of Public Health, Universitas Negeri Manado, Jl. Kampus Unima No. 1, Tondano 95618, Indonesia Tel +62-85175300139 E-mail alva.supit@unima.ac.id

Received 2025 October 25; Revised 2025 November 25; Accepted 2026 January 26.

Abstract

INTRODUCTION

The importance of circadian rhythm in terrestrial biology, including human health, has been well recognized and appreciated [1]. Numerous diseases have been associated with the disruption of circadian rhythm in various populations across different races and age groups. These include metabolic [2], psychoneurological [3], and neoplastic pathologies [4]. Among these, mental health is in particular directly affected by the disrupted sleep-wake cycle, which may form positive feedback leading to the vicious cycle of depression, anxiety, stress, and reciprocal sleep disturbances [5]. In modern society, artificial light from electronic devices has desynchronized the human circadian rhythm from the sunlight as a natural cue of the day-night cycle. Global society had just begun to be more aware of the threat of circadian-related diseases when the COVID-19 pandemic struck the planet, where poor sleep behaviour is associated with the severity of COVID-19 [6,7]. In addition to its consequential pathogenic-related morbidity and mortality, the COVID-19 pandemic has also brought significant collateral impact on mental health worldwide. The implementation of total or partial lockdown and work/study from home, in particular, has affected the well-being of workers and students worldwide [8]. Consequently, numerous studies have stated the high incidence of COVID-19-related mental health disturbances such as depression, anxiety, and stress among working communities and students [9-12].

Previously, before the pandemic, we [13] and others have shown that chronotype is associated with depression across different populations and latitudes [14-17]. During normal conditions, those who have a later chronotype tend to show higher depression symptoms. However, in pathological sleep conditions, such as during the COVID-19 restriction, it is unclear whether the correlation between chronotype and depression persisted. In this current research, we aim to compare the sleep patterns and mental health profile of students who experienced online studying due to the COVID-19 lockdown with their predecessor student group, who did not experience COVID-19 but were enrolled in the same program. Specifically, we measured their chronotype using the Munich Chronotype Questionnaire (MCTQ) and their emotional negative state by using the Depression, Anxiety, and Stress Scale (DASS) questionnaires. The comparison group was retrieved from our 2019 archival data, which measured the identical parameters in the students enrolled in the same program before the COVID-19 pandemic. We hypothesized that students affected by COVID-19 would have higher mental health negative symptoms accompanied by a shift toward the later chronotype compared to the unaffected previous batch. Furthermore, we anticipated that the depression-chronotype correlation pattern observed pre-pandemic could be disrupted during the pandemic.

METHODS

RESULTS

From the pandemic (Pand) group, 257 subjects filled the questionnaire and were included in the study. As a comparison, we retrieved 325 valid entries from our archival data collected in 2019 (pre-pandemic, PreP). Since females and males exhibit different characteristics in terms of chronotype and sleep duration, we analyzed the data separately for each sex to faithfully capture the true phenomenon. In both PreP and Pand groups, females predominated (69.84% and 84.82% in PreP and Pand, respectively). This is due to the demographic nature of our student population. Variations between male and female subjects during PreP to Pand are presented in Table 1. When combined, only grade point average (GPA) and smoking habit differed pre-and during pandemic, while coffee/alcohol consumption, BMI, and cumulative GPA did not differ significantly. Males in the pandemic group were significantly older than pre-pandemic (p=0.008), but this is caused by the nature of the batches. The GPA, but not the cumulative GPA significantly increased during the pandemic, perhaps due to the changes and compromises during the online learning period. Daily coffee consumption significantly increased in males (p<0.001), while smoking behaviour interestingly declined during the pandemic. Other variables, including BMI and alcohol consumption, showed no significant differences. These indicate potential behavioral and academic shifts during the pandemic, particularly regarding GPA, caffeine intake, and smoking.

Table 1.

Respondent characteristics

Since males and females displayed different chronotype and mental health symptoms, we also assessed the MCTQ and DASS separately between these two sexes. Table 2 displays the significant increase in depression, anxiety, and stress levels among students before and during the pandemic, categorized by sex and combined altogether. In all DASS, there were notable increases in severe cases for both males and females (all p<0.01), reflecting the overall negative impact of the pandemic on mental health. This increase persisted when both sexes were pooled together and the raw scores of DASS were used for numerical analyses (Fig. 1). Between sexes, females in the pre-pandemic group showed a higher stress level compared to males, but with similar depression and anxiety levels. During the pandemic, there is no sex difference in all depression, anxiety, and stress levels (Supplementary Table 1 in the online-only Data Supplement).

Table 2.

Depression, anxiety, and stress score based on sex and pandemic status

Fig. 1.

Comparison of depression, anxiety, and stress raw score pre-pandemic (n=325) and during pandemic (n=257, showing a greater level of all three parameters during the pandemic (***p<0.005; ****p<0.001 by two-tailed t-tests).

Changes in chronotype parameters based on sex and pandemic status are presented in Table 3, comparing pre-pandemic and pandemic periods. SD_ave increased significantly during the pandemic for both males (6:40 to 7:13, p=0.029) and females (6:46 to 7:13, p=0.001), with the combined data showing a highly significant rise (p<0.001). Sleep duration on weekends (SD_weekends) also increased notably, particularly for females (7:25 to 9:15, p<0.001) and the combined group (7:34 to 9:14, p<0.001). Mid-sleep times on weekdays (MS_weekdays) and weekends (MS_weekends) shifted later during the pandemic, with significant changes observed for females and the combined group (p<0.001), indicating a delay in sleep patterns. Interestingly, we observed that the MSF_sc—although has been considered as the main reading of MCTQ—did not show any significant change during the pandemic (p>0.1) regardless of whether the data were analyzed in combination or separately between sexes (Table 3 and Supplementary Fig. 1 [in the online-only Data Supplement]), suggesting that during pathological community-wide chronotype shift, MSF_sc may not be the most suitable parameter for reporting the chronotype, at least in our current samples.

Table 3.

Chronotype parameters based on sex and pandemic status

Previously, we and others reported a correlation between chronotype and depression symptoms. We then asked if this correlation persisted during the pandemic. Since the MSF_sc alone could not reflect the chronotype change, we also calculated the correlation between depression (D), anxiety (A), and stress (S) scores with other chronotype parameters, comparing pre-pandemic and pandemic periods. Pre-pandemic, significant negative correlations were observed between D and SD_ave (R=-0.149, p=0.007) and sleep duration on weekdays (SD_weekdays, R=-0.167, p=0.003). Additionally, significant positive correlations were found between D and MSF_sc (R=0.177, p=0.001), MS_weekdays (R=0.157, p=0.005), MS_weekends (R=0.187, p=0.001), all confirming previous observations in non-pandemic situations. During the pandemic, these significant correlations between D and chronotype parameters largely disappeared, with no significant correlations observed for sleep duration or mid-sleep times. Instead, A showed new small but significant positive correlations with MS_weekdays (R=0.140, p=0.025) and MS_weekends (R=0.132, p=0.035), indicating that later sleep timings were associated with higher A levels during the pandemic. This suggests a shift in the relationship between mental health and sleep patterns during the pandemic, where anxiety became more related to chronotype. However, the observed correlations between A and midsleep times were statistically significant but weak (R≈0.13–0.14), thus limiting clinical relevance (Table 4).

Table 4.

Correlation matrix between Depression, Anxiety, and Stress Scale and chronotype pre- and during pandemic

DISCUSSION

Our results showed that during the pandemic lockdown, students experienced significantly higher depression, anxiety, and stress symptoms compared to the control batch of students before the pandemic. Moreover, students undergoing the pandemic lockdown also had a significantly longer sleep duration per week and later mid-sleep time compared to before the pandemic. Interestingly, the subject’s sleeping schedule had been altered so much that the conventional analysis of MCTQ (calculating the MSF_sc) could not yield a reliable result. Instead, the middle sleep time and sleep duration should be calculated separately for working and free days to fully reflect the sleep pattern change. Finally, we also found that the previously recognized correlation between depression and chronotype diminished during COVID-19 restrictions, while a new anxiety-chronotype correlation emerged.

Multiple studies have confirmed the rise of mental health symptoms during the COVID-19 pandemic, with a meta-analysis reporting a seven-fold increase in depression [18]. This can be attributed to the disease itself, or the lockdown implemented by the local governments to prevent COVID-19 spread. Interestingly, while females exhibited higher stress levels in non-pandemic situations compared to males (as shown in our results [13] and others [19-21]), no significant differences were observed during the pandemic across all mental health scales. This suggests that both sexes experienced similar adverse effects on their mental well-being due to shared challenges caused by COVID-19 pandemic and restrictions. Thus, this current study conforms with previous studies while adding to the existing body of evidence the cohort sample from a group of Indonesian college students [22].

MCTQ has been previously favoured for assessing chronotype across latitudes of the planet, in addition to its easy administration. Classically, MCTQ reports the MSF_sc as its main parameter [23]. In this current research, interestingly, although the sleep pattern is robustly skewed towards a later chronotype and longer duration, the MSF_sc seems to be insensitive in highlighting this phenomenon. Reporting the mid-sleep time during nonphysiological conditions, such as during insomnia, may be unfavourable, as insomnia disorder is characterized by time gaps during the night due to difficulty maintaining sleep; there may be limitations in using mid-sleep time as an accurate indicator of chronotype in insomnia patients [24]. Moreover, during the pandemic, the blurring of boundaries between workdays and free days, along with highly irregular schedules, made MSF_sc less sensitive under socially disrupted conditions such as pandemic lockdowns. MSF_sc relies on predictable contrasts between weekdays and weekends and assumes that sleep debt correction accurately reflects accumulated restriction during workdays. These assumptions hold under stable social schedules but break down when external time cues and obligations are disrupted, as during the pandemic. Lockdowns blurred the distinction between workdays and free days, reducing the compensatory oversleep typically observed on weekends. Consequently, MSF_sc loses sensitivity because its core premise (correcting mid-sleep on free days by subtracting sleep debt) becomes less sensitive when sleep debt itself is inconsistent or negligible. In contrast, raw mid-sleep times for weekdays and weekends, along with derived metrics such as social jetlag and sleep regularity indices, provide direct behavioral markers of circadian timing without relying on assumptions about debt correction (Supplementary Fig. 1 [in the online-only Data Supplement] vs. Table 3). These complementary indicators capture real delays and irregularity in sleep patterns, offering a more nuanced and robust representation of chronotype under socially disrupted conditions. Future research should prioritize these granular measures when evaluating chronotype shifts during periods of widespread schedule instability.

During the pandemic, the previously established correlation between chronotype and depression [13] diminished; instead, a new anxiety-chronotype correlation emerged. The new correlation between later mid-sleep times and increased anxiety during the pandemic may reflect a shift in the relationship between sleep patterns and mental health. As students experienced more flexible schedules and reduced external activities, many of them might have adopted later sleep timings, resulting in the disruption of their circadian rhythms and increased feelings of anxiety. The uncertainty and stress associated with the pandemic likely contributed to this shift, as heightened anxiety can lead to difficulty falling asleep and later bedtimes [15]. Additionally, as discussed above, increased screen time and reduced exposure to natural light during lockdowns may have further delayed sleep onset, creating a feedback loop that worsened anxiety, though this remains speculative [25]. Although statistically significant, the correlations between anxiety and mid-sleep times were weak (R≈0.13–0.14), explaining less than 2% of the variance in anxiety scores. This magnitude is far below thresholds typically considered clinically meaningful and suggests that the association is can be more of a statistical artifact than a robust behavioral link. The significance likely reflects the large sample size rather than a substantive effect. Therefore, these findings should be interpreted as exploratory and hypothesis-generating rather than confirmatory. Future research should prioritize effect size and practical relevance over p-values, and consider longitudinal or experimental designs to clarify whether these small associations persist under different conditions or represent transient patterns specific to pandemic-related disruptions. Therefore, it is interesting to assess whether this trend persists after the pandemic, or would it go back to the original correlation between depression and chronotype, which is currently being studied by our group.

This study has several limitations that should be acknowledged. First, the reliance on self-reported data for sleep patterns and mental health symptoms may introduce biases, such as recall or social desirability bias. To address this, future studies could incorporate objective measures like actigraphy to validate sleep data. Second, the cross-sectional design limits the ability to establish causality between changes in sleep patterns and mental health outcomes. The cross-sectional design comparing two different cohorts may introduce confounding factors. It is noteworthy that the observed differences may partly reflect cohort-specific characteristics such as age and academic stage rather than pandemic effects alone. These confounders limit causal inference and highlight the need for longitudinal designs in future research to provide more robust insights into these relationships. Third, the study focused on a specific student population from the same department but in different batches, which may limit the generalizability of the findings. Expanding the sample to include diverse demographic groups and educational contexts would enhance the applicability of the results. Additionally, the pre-pandemic group filled out a paper-based questionnaire, while the pandemic group completed an online survey, which could introduce methodological inconsistencies. Differences in survey mode (paper vs. online) may have influenced response patterns and variance. Survey mode effects have been previously documented: online surveys often yield higher disclosure of sensitive information but may introduce measurement error and lower completion rates compared to paper surveys [26]. This methodological inconsistency could partly explain observed differences. Future studies should standardize data collection methods. Lastly, the pandemic group may have been more prone to survey or Zoom fatigue, potentially affecting the accuracy and reliability of their responses. Mitigating this issue could involve shorter, more engaging surveys and ensuring participants are well-rested before completing the questionnaire. Including these considerations in future analyses would provide a more comprehensive understanding of the phenomenon.

In conclusion, COVID-19 pandemic increased depression, anxiety, and stress in Indonesian university students, while also altering the chronotype towards a later one. During pandemic-level pathological sleeping conditions, it is essential to adapt the questionnaires rather than relying solely on standard formats to accurately capture the true phenomenon.

Supplementary Materials

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