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. 2022 Nov 1:e13767. Online ahead of print. doi: 10.1111/jsr.13767

Two years after lockdown: Longitudinal trajectories of sleep disturbances and mental health over the COVID‐19 pandemic, and the effects of age, gender and chronotype

Federico Salfi 1, Giulia Amicucci 1,2, Domenico Corigliano 1,2, Lorenzo Viselli 1, Aurora D'Atri 1, Daniela Tempesta 1, Maurizio Gorgoni 2,3, Serena Scarpelli 2, Valentina Alfonsi 2, Michele Ferrara 1,✉
PMCID: PMC9878065  PMID: 36317491

Summary

Since the first lockdown of Spring 2020, the COVID‐19 contagion waves pervasively disrupted the sleep and mental health of the worldwide population. Notwithstanding the largest vaccination campaign in human history, the pandemic has continued to impact the everyday life of the general population for 2 years now. The present study provides the first evidence of the longitudinal trajectories of sleep disturbances and mental health throughout the pandemic in Italy, also describing the differential time course of age groups, genders and chronotypes. A total of 1062 Italians participated in a three‐time‐point longitudinal study covering two critical stages of the emergency (the first lockdown in April 2020 and the second partial lockdown in December 2020) and providing a long‐term overview 2 years after the pandemic outbreak (April 2022). We administered validated questionnaires to evaluate sleep quality/habits, insomnia, depression, stress and anxiety symptoms. Analyses showed a gradual improvement in sleep disturbances, depression and anxiety. Conversely, sleep duration progressively decreased, particularly in evening‐type and younger people. Participants reported substantial earlier bedtime and get‐up time. Stress levels increased during December 2020 and then stabilised. This effect was stronger in the population groups apparently more resilient during the first lockdown (older people, men and morning‐types). Our results describe a promising scenario 2 years after the pandemic onset. However, the improvements were relatively small, the perceived stress increased, and the re‐establishment of pre‐existing social/working dynamics led to general sleep curtailment. Further long‐term monitoring is required to claim the end of the COVID‐19 emergency on Italians' sleep and mental health.

Keywords: COVID‐19 outbreak, long‐term effect, psychological well‐being, sleep health

1. INTRODUCTION

In the first months of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) started to spread worldwide, giving rise to a pandemic. To deal with the increasing contagion and death rates due to virus propagation, governments around the world started to apply extraordinary containment measures consisting of home confinement, social distancing and the closure of most business activities. The lockdown period was associated with raised sleep disturbances and mental health problems among the general population, as reported by consistent meta‐analytic literature (AlRasheed et al., 2022; Jahrami et al., 2022; Robinson et al., 2022). Italy was the first European country to handle the contagion wave of COVID‐19, implementing a total lockdown lasting 2 months (March–April 2020). Several Italian studies confirmed the pervasive impact of this unprecedented period on sleep quality/habits and psychological well‐being (Casagrande et al., 2020; Cellini et al., 2020; Salfi et al., 2021b). After a phase of alleviated pandemic emergency and loosened restrictions (Summer 2020), a second large contagion wave occurred in Autumn 2020. The Italian government promptly reacted to the new exacerbated scenario by applying partial lockdown measures on a regional basis, weighted according to the local load on the healthcare system and infection rates.

Notwithstanding the adoption of this regional approach consisting of lighter restraining measures than the first lockdown, some longitudinal investigations showed a persistent impact of the emergency period on sleep (Conte et al., 2021; Salfi et al., 2021a) and mental health (Salfi et al., 2021a) among the Italian population during the second contagion wave. These results were consistent with the international literature confirming long‐lasting repercussions on sleep features (Basishvili et al., 2021; Liu et al., 2022; Trakada et al., 2022) and psychological measures (Benke et al., 2022; Chodkiewicz et al., 2021; Daly & Robinson, 2022; Rus Prelog et al., 2022; Wetherall et al., 2022) during the second COVID‐19 wave, suggesting the urgency of large‐scale interventions to preserve the general well‐being. In the last days of 2020, the administration of the first COVID‐19 vaccine to the adult population was authorised by the European and Italian medicines agencies. Notwithstanding the largest vaccination campaign in human history and the consequent lifting of restraining measures in the subsequent months, COVID‐19 has continued to disrupt the everyday life of the worldwide population for 2 years now. However, the long‐term impact of the pandemic on sleep and mental health remains poorly elucidated. In this vein, the first aim of the present study is to identify the longitudinal trajectories of sleep quality, insomnia, depression, stress and anxiety in the general population across the pandemic in Italy. We surveyed a large sample of Italian citizens (N = 1.062) using validated questionnaires at three time points: during the first weeks of lockdown (April 2020), during the second contagion wave (December 2020) and 2 years after the first implementation of the lockdown measures (April 2022).

The current literature consistently showed that age groups (Amicucci et al., 2021; Bottary et al., 2022; Daly et al., 2020; Jahrami et al., 2022; Rossi et al., 2020), genders (Daly et al., 2020; Rossi et al., 2020; Salfi et al., 2020; Salfi et al., 2021b) and chronotypes (Bottary et al., 2022; Merikanto et al., 2022; Salfi et al., 2021b) reacted differently to the first months of restraining measures. Specifically, younger people (Amicucci et al., 2021; Daly et al., 2020; Jahrami et al., 2022; Rossi et al., 2020) and women (Daly et al., 2020; Rossi et al., 2020; Salfi et al., 2020; Salfi et al., 2021b) reported higher rates of sleep disturbances and psychological symptoms. These results were confirmed by Italian (Salfi et al., 2021a) and European studies (Benke et al., 2022; Chodkiewicz et al., 2021; Rus Prelog et al., 2022; Wetherall et al., 2022) addressing the effect of the second wave of COVID‐19. Similarly, the evening chronotype was associated with more evident changes in sleep patterns and increased sleep and mental health problems both during the lockdown (Bottary et al., 2022; Merikanto et al., 2022; Salfi et al., 2021b) and the second contagion wave (Salfi et al., 2021a). Based on this evidence, another objective of the present research is to identify the different time courses of sleep and psychological disturbances between age groups, genders and chronotypes. Our longitudinal investigation is unique in covering two critical stages of the COVID‐19 outbreak (the first lockdown and the second partial lockdown), and providing a long‐term overview of the Italians' general well‐being 2 years after implementing the first measures to counteract the spread of the virus.

2. METHODS

2.1. Participants and procedure

The present study consists of a longitudinal web‐based survey involving three assessment points. Participants responded using an online platform (Google forms). The first survey wave was held during the third and fourth weeks of the lockdown period of Spring 2020 (25 March–7 April 2020), coinciding with the first contagion peak of COVID‐19. Subsequently, respondents were invited to participate in the second survey wave by email during the contagion peak of the second pandemic wave (28 November–11 December 2020). Finally, all respondents were re‐invited to take part in the third survey wave 2 years after the first one (9 April–22 April 2022). A total of 1062 Italian citizens participated in all three assessments. The demographic composition of the sample is reported in Table 1. All the survey waves comprised an evaluation of sleep quality, insomnia symptoms, chronotype, depressive symptomatology, perceived stress and anxiety through the following validated questionnaires: the Pittsburgh Sleep Quality Index (PSQI; Curcio et al., 2013), the Insomnia Severity Index (ISI; Castronovo et al., 2016), the Morningness–Eveningness Questionnaire‐reduced version (MEQr; Natale et al., 2006), the Beck Depression Inventory‐second edition (BDI‐II; Sica & Ghisi, 2007), the 10‐item Perceived Stress Scale (PSS‐10; Mondo et al., 2021) and the state–anxiety subscale of the State–Trait Anxiety Inventory (STAI‐X1; Spielberger et al., 1970). The administration order of mandatory sleep questionnaires was as follows: PSQI, ISI and MEQr. Subsequently, participants could decide whether to continue the compilation of the other three questionnaires (BDI‐II, PSS‐10, STAI‐X1) with the option to stop after each of them to ensure reliable unforced responses due to the burden of testing battery. A total of 71, 43 and 40 subjects did not fill out the BDI‐II in the first, second and third survey waves, respectively. A total of 100, 61 and 83 participants did not complete the PSS‐10 during the three assessment points, respectively. Finally, 103, 61 and 116 respondents did not fill out the STAI‐X1 in the three survey waves. The Internal Review Board of the University of L'Aquila approved the research project (protocol n. 43,066/2020). The study was performed according to the principles established by the Declaration of Helsinki. Online informed consent was obtained from participants.

TABLE 1.

Demographic characteristics of the sample participating in the three survey waves

April 2020 (survey wave 1) December 2020 (survey wave 2) April 2022 (survey wave 3)
N (%) or *mean (SD)
Age *35.325 (12.59) *35.985 (12.59) *37.367 (12.59)
Gender
Male 202 (19.02) 202 (19.02) 202 (19.02)
Female 860 (80.98) 860 (80.98) 860 (80.98)
Education
Middle/High school 318 (29.94) 285 (26.84) 238 (22.41)
Graduate 599 (56.40) 629 (59.23) 633 (59.60)
Postgraduate 145 (13.65) 148 (13.94) 191 (17.99)
Occupation
Unemployed 81 (7.63) 77 (7.25) 95 (8.95)
Student 305 (28.72) 241 (22.69) 167 (15.73)
Healthcare worker 68 (6.40) 90 (8.47) 106 (9.98)
Self‐employed 198 (18.64) 189 (17.80) 212 (19.96)
Employed 379 (35.69) 428 (40.30) 443 (41.71)
Retired 31 (2.92) 37 (3.48) 39 (3.67)
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Note: Numbers preceded by an asterisk (*) are mean values (SD).

2.2. Questionnaires

The PSQI (Curcio et al., 2013) is a widely used questionnaire to evaluate sleep quality. It consists of 19 questions covering seven dimensions (subjective sleep quality, sleep duration, sleep latency, habitual sleep efficiency, sleep disturbances, the use of sleeping medications, and daytime dysfunctions). A higher score in each sub‐component (range, 0–3) indicates greater severity of symptoms on the specific dimension (i.e. higher scores on subjective sleep quality sub‐component corresponds to lower sleep quality; higher scores on sleep duration sub‐component indicates shorter sleep duration; higher scores in sleep latency sub‐component points to longer sleep latency, etc.), giving rise to a higher global score (range, 0–21) that suggests poorer sleep quality. The ISI (Castronovo et al., 2016) is a well‐established 7‐item tool to measure the severity of insomnia symptoms in clinical settings (range, 0–21). A higher score points to a more severe insomnia condition. The MEQr (Natale et al., 2006) is a 5‐item instrument typically used in chrono‐psychological research to estimate circadian typologies within the morningness–eveningness continuum. A lower total score (range, 4–25) suggests a tendency to eveningness and vice versa for morningness. The BDI‐II (Sica & Ghisi, 2007) is a 21‐item self‐report inventory used in clinical practice to evaluate the severity of depression symptoms. A higher score (range, 0–63) is interpreted as a more severe depressive symptomatology. The PSS‐10 (Mondo et al., 2021) is a 10‐item questionnaire to assess thoughts and feelings about stressful events. A higher score (range, 0–40) indicates a higher level of perceived stress. The STAI‐X1 (Spielberger et al., 1970) is a 20‐item anxiety inventory belonging to the Cognitive Behavioural Assessment battery 2.0 (Sanavio et al., 1998). A higher total score points to more severe state anxiety.

2.3. Statistical analysis

The statistical analyses were performed using the lme4 R package (Bates et al., 2015), which provides functions for fitting and analysing mixed models. Models were fitted using REML adopting the Satterthwaite approximation to compute p‐values (Luke, 2017). Mixed‐model analyses included a random intercept per participant to account for the repeated‐measures nature of the data and the variability among respondents' scores. Bonferroni post hoc tests and simple effect contrasts using the “emmeans” R package (Lenth et al., 2022) were computed in the case of significant main effects or interaction effects, respectively. The level of significance was always set at p < 0.05.

Firstly, we ran different models including the scores of each sleep and mental health questionnaire (PSQI, ISI, BDI‐II, PSS‐10 and STAI‐X1) as dependent variables, and the survey wave factor (April 2020, December 2020, April 2022) as three‐level within‐subjects predictor. These analyses aimed to explore the general trajectories of sleep and psychological disturbances among the overall sample along the two pandemic years.

Furthermore, the same analysis was performed for specific items of PSQI (total sleep time [min], bedtime [hh:mm] and get‐up time [hh:mm]) and each sub‐component of PSQI (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleep medications and daytime dysfunction) to describe the time course of specific dimensions of sleep habits/quality across the three assessments. Finally, we ran further mixed models on questionnaire scores (PSQI, ISI, BDI‐II, PSS‐10 and STAI‐X1) including survey wave (April 2020, December 2020, April 2022), age (continuous time‐varying variable), gender (male, female), MEQr scores (continuous time‐varying variable), and the interaction of survey wave factor with age, gender and MEQr scores as predictors. These analyses aimed at describing possible differences in the trajectories of sleep quality, insomnia, depression, perceived stress and anxiety depending on the above‐mentioned demographic and chrono‐psychological factors. Continuous moderators (age and MEQr score) were represented by plotting mean ± standard deviation values to provide a graphical representation of significant interaction effects. As regards the age variable, we coded the mean age −1 standard deviation (23.612 years) as “younger”, the mean age (36.226 years) as “middle‐age”, and the mean age +1 standard deviation (48.839 years) as “older”. As regards MEQr scores, we labelled the mean MEQr score −1 standard deviation (11.803) as “evening‐type”, the mean MEQr score (15.494) as “intermediate‐type”, and the mean +1 standard deviation (19.185) as “morning‐type”.

The analyses involving PSQI scores and one of its sub‐components (habitual sleep efficiency) were carried out excluding 44, 36 and 34 participants of the first, second and third survey waves, respectively, due to compilation errors (respondents reported longer total sleep time than time in bed). Due to the optional nature of BDI‐II, PSS‐10 and STAI‐X1, Little's MCAR test was performed using SPSS version 27.0.1.0 (IBM, Armonk, NY, USA), which showed that missing data occurred completely at random over the three survey waves (all p > 0.241). Harman's single factor test did not identify common method bias in each survey wave.

3. RESULTS

3.1. Sleep variables

The PSQI overall score did not differ between the three survey waves (F 2,2032.77 = 1.637, p = 0.195), indicating that sleep quality was stable over the pandemic. On the other hand, the survey wave factor was significant in the analyses on ISI score (F 2,2122 = 28.699, p < 0.001) and total sleep time (F 2,2122 = 47.751, p < 0.001). Bonferroni post hoc comparisons (Figure 1) revealed a progressive improvement of insomnia symptomatology and decreased sleep duration. Specifically, the second and third survey waves were characterised by decreased ISI scores (difference of estimated marginal means: −0.617, p < 0.001; −1.076, p < 0.001; respectively) and total sleep time (−15.395 min, p < 0.001; −20.706 min, p < 0.001; respectively) than the first one. The third assessment was associated with reduced ISI scores (−0.460, p = 0.004) and sleep duration (−5.311 min, p = 0.048) compared with the second one.

FIGURE 1.

FIGURE 1

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Estimated marginal mean ± standard error of PSQI scores (sleep quality), ISI scores (insomnia) and total sleep time (min) during the three survey waves. Significant Bonferroni post hoc comparisons are indicated with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001). ISI, Insomnia Severity Index; PSQI, Pittsburgh Sleep Quality Index

Analyses also showed a significant effect of the survey wave factor on bedtime (F 2,2122 = 264.513, p < 0.001) and get‐up time (F 2,2122 = 430.555, p < 0.001). As shown in Figure 2, post hoc comparisons indicated the progressive advance of bedtime and get‐up time over the two pandemic years. Specifically, participants reported earlier bedtime and get‐up time during the second (−34.567 min, p < 0.001; −50.400 min, p < 0.001; respectively) and third survey waves (−50.400 min, p < 0.001; −70.567 min, p < 0.001; respectively) than the first one. Bedtime and get‐up time were also significantly advanced in the third assessment compared with the second one (−15.833 min, p < 0.001; −20.167 min, p < 0.001; respectively). Descriptive statistics (means and standard deviations) of all sleep variables in the three time points are reported in the “Supporting Information” section (Table S1).

FIGURE 2.

FIGURE 2

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Estimated marginal mean ± standard error of bedtime and get‐up time (hh:mm) during the three survey waves. Asterisks indicate significant Bonferroni post hoc comparisons (***p < 0.001)

Results of the analyses on PSQI sub‐components are reported in Table 2. We highlighted significant differences between survey waves on subjective sleep quality, sleep latency, sleep duration, sleep disturbances and daytime dysfunction. Participants reported better subjective sleep quality and less severe sleep disturbances during the third assessment time compared with the first one (p < 0.001, p = 0.001, respectively) and the second one (p = 0.005, p = 0.046, respectively). Sleep‐onset latency was reduced over time (all p < 0.001). Conversely, post hoc comparisons showed reduced sleep duration during the second and third survey waves compared with the first one (both p < 0.001), and increased daytime dysfunctions over time (all p < 0.028).

TABLE 2.

Descriptive statistics (mean [SD]) of PSQI sub‐component scores during the three survey waves (survey wave 1: April 2020; survey wave 2: December 2020; survey wave 3: April 2022) and the corresponding statistical comparisons (F, p, Bonferroni post hoc)

April 2020 December 2020 April 2022
PSQI sub‐component Mean (SD) F p Post hoc
Subjective sleep quality 1.36 (0.76) 1.30 (0.71) 1.23 (0.66) 14.72 < 0.001

SW1 > SW3***

SW2 > SW3**
Sleep latency 1.36 (1.02) 1.20 (1.01) 1.07 (0.96) 43.61 < 0.001 SW1 > SW2 > SW3***
Sleep duration 0.70 (0.79) 0.81 (0.78) 0.85 (0.77) 21.01 < 0.001

SW1 < SW2***

SW1 < SW3***
Habitual sleep efficiency 0.77 (0.99) 0.79 (0.98) 0.80 (0.97) 0.52 0.59
Sleep disturbances 1.39 (0.58) 1.37 (0.56) 1.32 (0.55) 6.39 0.002

SW1 > SW3**

SW2 > SW3*
Sleep medications 0.26 (0.77) 0.32 (0.85) 0.29 (0.82) 2.18 0.11
Daytime dysfunction 0.83 (0.71) 0.90 (0.67) 0.97 (0.71) 15.00 < 0.001 SW1 < SW2 < SW3*
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Note: A higher score in each sub‐component indicates greater severity of the respective problem (see Section 2.2 “Questionnaires” section for a detailed description of the sub‐component score interpretation).

Significant values are in bold, while significant Bonferroni post hoc comparisons are indicated with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001).

Abbreviations: PSQI, Pittsburgh Sleep Quality Index; SD, standard deviation; SW, survey wave.

3.2. Age, gender and chronotype effect on sleep variables

As reported in Table 3, older age was associated with poorer sleep quality, more severe insomnia symptoms, and shorter total sleep time. The interaction between survey wave and age factors was significant in the analyses of all the sleep variables (PSQI score, ISI score, total sleep time), indicating an age‐dependent time course of sleep quality, insomnia symptoms and sleep duration.

TABLE 3.

Results (F and p) of the mixed model analyses on PSQI score (sleep quality), ISI score (insomnia) and total sleep time (min)

PSQI score ISI score Total sleep time
Predictor F p F p F p
Survey wave 1.109 0.330 1.287 0.276 17.363 < 0.001
Age 17.215 < 0.001 4.506 0.034 95.126 < 0.001
Gender 19.427 < 0.001 9.638 0.002 0.468 0.494
MEQr score 59.424 < 0.001 77.820 < 0.001 8.204 0.004
Survey wave × Age 3.984 0.019 3.843 0.022 4.816 0.008
Survey wave × Gender 2.145 0.117 0.599 0.549 0.101 0.904
Survey wave × MEQr score 0.001 0.999 0.433 0.649 3.463 0.031
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Note: The models comprised the following predictors: survey wave (April 2020, December 2020, April 2022), age, gender (male, female), MEQr score (chronotype), and the interaction between survey wave and age, gender and MEQr score.

Note: Significant values are in bold.

Abbreviations: ISI, Insomnia Severity Index; PSQI, Pittsburgh Sleep Quality Index; MEQr, Morningness–Eveningness Questionnaire‐reduced version.

Simple effect contrasts (Figure 3) highlighted significant differences between the survey waves among the older respondents, as shown by increased PSQI scores in December 2020 (+0.336, p = 0.041), and a subsequent significant improvement in sleep quality during the last assessment period (−0.369, p = 0.020). No differences were reported by the middle‐age (+0.096, p = 0.452) and the younger participants (−0.144, p = 0.385) during the second survey compared with the first one, and from the second to the third assessment (−0.104, p = 0.417;  +0.162, p = 0.338; respectively).

FIGURE 3.

FIGURE 3

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Estimated marginal mean ± standard error of PSQI scores (sleep quality), ISI score (insomnia) and total sleep time (min) during the three survey waves according to age (red: young; yellow: middle‐age; grey: older). Significant simple effect contrasts are indicated with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001). ISI, Insomnia Severity Index; PSQI, Pittsburgh Sleep Quality Index

ISI scores decreased during the second survey wave compared with the first one in the middle‐age (−0.577, p = 0.001) and young people (−0.952, p < 0.001), while insomnia was stable among the older respondents (−0.202, p = 0.384). On the other hand, older participants reported a significant reduction in ISI scores from the second to the third survey wave (−0.703, p = 0.002). A further decline in ISI scores was observed in April 2022 in the middle‐age group compared with December 2020 (−0.386, p = 0.033), while the younger population did not show any change in insomnia symptoms comparing the last two survey waves (−0.068, p = 0.776).

An overall reduction in total sleep time was observed in December 2020 compared with April 2020 (young: −15.551 min; middle‐age: −14.122 min; older: −12.694 min; all p < 0.001), and the young population was marked by a further reduction in sleep time during the last assessment (−9.801 min, p = 0.008). Older and middle‐age respondents reported stable sleep duration in the last two survey waves (+0.635 min, p = 0.855; −4.583 min, p = 0.103).

Female participants reported poorer sleep quality and more severe insomnia symptoms than males (Table 3). No differences in total sleep time between genders emerged. The interaction between survey wave and gender factors on sleep variables did not reveal differences between genders in the time course of sleep quality, insomnia symptoms and sleep duration.

Finally, eveningness was associated with lower sleep quality, more severe insomnia symptomatology and shorter total sleep time (Table 3). The interaction between survey wave and MEQr scores in predicting sleep duration was significant, while no different time course of sleep quality and insomnia symptoms between circadian typologies was highlighted. As shown in Figure 4, simple effect contrasts revealed a larger reduction of total sleep time among the evening‐type population (−17.068 min, p < 0.001) from the first to the second survey wave than among the intermediate‐type (−14.122, p < 0.001) and morning‐type respondents (−11.177, p = 0.002). Late chronotypes are associated with a further significant reduction of sleep time from the second to the third assessment point (−7.774 min, p = 0.028), while no significant differences were reported by intermediate‐type (−4.583 min, p = 0.102) and morning‐type participants (−1.392 min, p = 0.708).

FIGURE 4.

FIGURE 4

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Estimated marginal mean ± standard error of total sleep time (min) during the three survey waves according to chronotype (light blue: morning‐type; blue: intermediate‐type; dark blue: evening‐type). Significant simple effect contrasts are indicated with asterisks (*p < 0.05, ***p < 0.001)

3.3. Psychological variables

The analyses on BDI‐II, PSS‐10 and STAI‐X1 scores showed significant differences between the three survey waves (F 2,2016.04 = 4.142, p = 0.016; F 2,1974.17 = 16.576, p < 0.001; F 2,1911.25 = 43.850, p < 0.001, respectively). As shown in Figure 5, participants reported less severe depressive symptoms during the third survey wave than the first one (−0.661, p = 0.023), while the second assessment did not significantly differ from the first (−0.109, p = 1.000) and the third ones (+0.552, p = 0.074). Perceived stress increased during the second (+1.043, p < 0.001) and third survey waves (+1.011, p < 0.001) compared with the first one. Anxiety scores began to decline in December 2020 (−1.887, p < 0.001), and were further reduced during the last assessment compared with the second one (−0.904, p = 0.008). Descriptive statistics (means and standard deviations) of all psychological variables in the three survey waves are reported in the “Supporting Information” section (Table S1).

FIGURE 5.

FIGURE 5

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Estimated marginal mean ± standard error of BDI‐II scores (depression), PSS‐10 scores (perceived stress) and STAI‐X1 scores (anxiety) during the three survey waves. Significant Bonferroni post hoc comparisons are indicated with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001). BDI‐II, Beck Depression Inventory‐second edition; PSS‐10, Perceived Stress Scale‐10 item; STAI‐X1, state–anxiety subscale of the State–Trait Anxiety Inventory

3.4. Age, gender and chronotype effect on psychological variables

Younger age, female gender and eveningness were associated with more severe depressive symptoms and higher perceived stress. Younger respondents and evening‐type people reported higher anxiety levels, while no relationship between age and anxiety emerged (Table 4). The interaction between survey wave and chrono‐demographic factors (age, gender, MEQr score) in predicting PSS‐10 was significant. No significant interaction effect for BDI‐II and STAI‐X1 scores was highlighted, indicating no differences between age groups, genders and chronotypes in the time course of depressive and anxiety symptoms.

TABLE 4.

Results (F and p) of the mixed model analyses on BDI‐II score (depression), PSS‐10 score (perceived stress) and STAI‐X1 score (anxiety)

BDI‐II score PSS‐10 score STAI‐X1 score
Predictor F p F p F p
Survey wave 0.949 0.387 26.924 < 0.001 27.373 < 0.001
Age 8.137 0.004 15.065 < 0.001 0.134 0.714
Gender 17.034 < 0.001 30.345 < 0.001 14.842 < 0.001
MEQr score 81.224 < 0.001 30.133 < 0.001 25.545 < 0.001
Survey wave × Age 0.404 0.668 7.072 < 0.001 0.061 0.941
Survey wave × Gender 1.330 0.265 12.596 < 0.001 0.644 0.525
Survey wave × MEQr score 0.484 0.616 3.646 0.026 1.182 0.307
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Note: The models comprised the following predictors: survey wave (April 2020, December 2020, April 2022), age, gender (male, female), MEQr score (chronotype), and the interaction between survey wave and age, gender and MEQr score.

Significant values are in bold.

Abbreviations: BDI‐II, Beck Depression Inventory‐second edition; MEQr, Morningness–Eveningness Questionnaire‐reduced version; PSS‐10, Perceived Stress Scale‐10 item; STAI‐X1, state–anxiety subscale of the State–Trait Anxiety Inventory.

Simple effect contrasts (Figure 6) revealed significantly raised stress levels from the first survey wave to the second one in all groups. However, the extent of the effect depended on age, gender and chronotype. Specifically, older participants and morning‐type subjects reported the largest increase in perceived stress (+2.461, p < 0.001; +2.145, p < 0.001; respectively), middle‐age and intermediate‐type respondents showed an intermediate increase (+1.657, p < 0.001; +1.657, p < 0.001; respectively), while younger and evening‐type subjects were associated with the smallest increase (+0.853, p = 0.010; +1.169, p < 0.001; respectively). Men reported a greater increase in perceived stress (+2.497, p < 0.001) than women (+0.817, p < 0.001). Finally, all groups reported unchanged stress levels between the second and the third survey waves (younger: −0.205, p = 0.546; middle‐age: −0.012, p = 0.962; older: +0.180, p = 0.572; male: +0.001, p = 0.999; female: −0.025, p = 0.911; evening‐type: −0.038, p = 0.907; intermediate‐type: −0.012, p = 0.962; morning‐type: +0.013, p = 0.969).

FIGURE 6.

FIGURE 6

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Estimated marginal mean ± standard error of PSS‐10 scores (perceived stress) during the three survey waves according to age (red: young; yellow: middle‐age; grey: older), gender (light green: female; dark green: male) and chronotype (light blue: morning‐type; blue: intermediate‐type; dark blue: evening‐type). Significant simple effect contrasts are indicated with asterisks (*p < 0.05, ***p < 0.001). PSS‐10, Perceived Stress Scale‐10 item

4. DISCUSSION

The present study demonstrated different trajectories of sleep quality, insomnia, depression, stress and anxiety during the two pandemic years.

As indicated by the PSQI total score, the overall sleep quality was stable across the three assessments. However, an overview of PSQI sub‐components suggested a more articulated pattern of results, revealing a clear dissociation of the effects on sleep features depending on the specific domain considered. On the one hand, subjective sleep quality improved, and sleep disturbances and sleep‐onset latency decreased 2 years after the lockdown of Spring 2020. These findings are consistent with the progressive reduction of insomnia symptoms reported by the overall sample. Conversely, we demonstrated a gradual decrease in sleep duration and an increase in sleep‐related daytime dysfunctions with the loosening of the restraining measures. Similar results were obtained by our previous investigation (Salfi et al., 2021a) comparing the lockdown scenario and the second contagion wave of COVID‐19, and by other studies carried out in the post‐lockdown period (Alfonsi et al., 2021; Massar et al., 2021). These results could be ascribable to the peculiar nature of the lockdown. The stressful situation led to worsened sleep disturbances worldwide (AlRasheed et al., 2022; Jahrami et al., 2022). Meanwhile, the strict confinement regimen was associated with unlocked time for sleep and reduced social jetlag for most of the population (Kantermann, 2020; Korman et al., 2020; Leone et al., 2020), with a consequent improvement in sleep‐related daytime dysfunctions. However, social and working obligations returned to exert their negative effect on sleep during the second survey wave, and even more 2 years after the lockdown. Consistently, participants reported substantial earlier get‐up times (~70 min) and a smaller but still important advanced bedtime (~50 min) in April 2022 compared with the first lockdown. The discrepancy between the above‐mentioned variations led to an overall reduction in total sleep time, as earlier awakenings were not adequately compensated by a comparable advance in bedtimes.

The improvement in sleep disturbances was accompanied by reduced depressive and anxiety symptoms. However, as far as depression is concerned, we observed a marginal decrease in the BDI‐II score, whose effect reached significance only comparing the first and the third assessments. The time course of self‐perceived stress was in stark contrast to the other examined psychological dimensions. The stress level increased from the lockdown period, reaching its plateau during the second contagion wave, and then stabilised. As suggested by other investigations (Conte et al., 2021; Salfi et al., 2021a), this outcome could be ascribable to the prolonged nature of the emergency, which was accompanied by a societal and economic crisis. Moreover, after lifting restrictions, the infection risk raised, presumably affecting the perceived stress in the general population.

Demographic factors such as age also influenced the observed effects. Older participants reported poorer sleep quality, more severe insomnia symptoms and shorter sleep duration than younger respondents. This outcome is consistent with the pre‐pandemic (Kamel & Gammack, 2006; Madrid‐Valero et al., 2017) literature and is related to the typical sleep changes occurring across the lifespan (Li et al., 2018; Patel et al., 2018). Moreover, in line with studies performed during the current period (Amicucci et al., 2021; Manchia et al., 2022; Rossi et al., 2020), the younger population showed more severe depressive symptoms and no difference in anxiety. Similar trajectories of depression and anxiety were observed over the three assessment points among the different age groups. However, our investigation highlighted an age‐dependent time course of sleep quality, insomnia, total sleep time and perceived stress. Younger and middle‐age groups reported unchanged overall sleep quality over the two pandemic years. On the other hand, we observed reduced sleep quality during the second assessment in older participants and a subsequent improvement during the third survey wave. Moreover, the three age groups seemed to report a similar extent of insomnia relief 2 years after the first lockdown, but this effect was reached at different time points. Younger people reported the maximum improvement already during Winter 2020, middle‐age participants gradually improved across the three survey waves, while insomnia symptoms of older respondents were stable until December 2020 and then improved. Finally, we confirmed higher stress levels among young people during the lockdown (Amicucci et al., 2021; Rossi et al., 2020; Ueda et al., 2020), while we reported a general increase in perceived stress among all age groups, which led to similar stress levels in the overall sample during the second and third survey waves.

The specific characteristics of the pandemic scenario during the three data collections could explain our pattern of results. During the second contagion wave of Winter 2020, the confinement measures were reduced, several business and school activities reopened, and freedom of movement was partially ensured. Therefore, infection risk increased while no vaccine against COVID‐19 was still available. Considering that older age is associated with the highest morbidity and mortality rates due to SARS‐CoV‐2 infection, it is unsurprising that older respondents experienced a substantial increase in stress levels with a concurrent decline in sleep quality and unchanged insomnia severity in December 2020 compared with the first lockdown period. However, the massive vaccination campaign of the subsequent months and the return to an almost normal life after 2 years could have allowed older participants to experience improved sleep disturbances. On the other hand, several studies suggested that the younger population suffered particularly from the collapse of social interactions during the confinement regimens (Elmer et al., 2020; Sampogna et al., 2021; Viselli et al., 2021; Weissbourd et al., 2020), with well‐documented repercussions on sleep and mental health (Amicucci et al., 2021; Daly et al., 2020; Jahrami et al., 2022; Rossi et al., 2020). Consequently, the loosening of confinement measures and the partial resumption of social interactions could explain the small increase in stress and the concomitant improvement of insomnia symptomatology among the younger people already during Winter 2020.

The present investigation confirmed the vulnerability of women to experience sleep and psychological problems during the pandemic (Daly et al., 2020; Rossi et al., 2020; Salfi et al., 2020; Salfi et al., 2021b). However, we did not detect differences between genders in the trajectories of sleep quality, insomnia, depression and anxiety symptoms across the three survey waves. Furthermore, our investigation showed that men reported definitely less stress than women during the first weeks of lockdown, although this difference was largely reduced since Winter 2020. This finding is consistent with the interpretation that females might have already reached the peak of psychological distress during the first weeks of home confinement (Salfi et al., 2020). Conversely, it seems that men reacted better to the lockdown, but the prolonged emergency led them to experience higher distress in the long run.

Finally, our study confirmed the well‐known vulnerability to sleep disturbances and psychological problems of evening‐type people (Adan et al., 2012). This evidence was also reported by investigations carried out during the first stage of the pandemic (Merikanto et al., 2022; Salfi et al., 2021b). All chronotypes were associated with a similar time course of sleep quality, insomnia, depression, stress and anxiety across the three periods covered by our study. Meanwhile, the changes in sleep duration differed between circadian typologies. During the lockdown, the typical discrepancy in sleep duration between chronotypes disappeared. However, the progression of the pandemic and the gradual resumptions of social and working obligations reinstated the well‐documented misalignment between the evening‐oriented biological clock and the morning‐oriented social schedule of the so‐called “night‐owls” (Roenneberg et al., 2019). Consequently, this situation led the evening‐type participants to sleep less and less as the COVID‐19 crisis improved and people resumed their pre‐existing daily routine (Salfi et al., 2022).

5. CONCLUSIONS

Since the first months of 2020, COVID‐19 has pervasively affected every area of life of the worldwide population. A massive amount of literature has been developed during the lockdown period, confirming pervasive repercussions on the sleep and mental health of the general population. The second contagion wave of COVID‐19 continued to be characterised by psychological distress and impaired sleep health worldwide.

To the best of our knowledge, the present investigation is the first to longitudinally examine the long‐term trajectories of sleep quality/habits, insomnia, depression, stress and anxiety after 2 years from the lockdown, providing novel insights on the time course of sleep and mental health according to age, gender and circadian typology across three critical stages of the COVID‐19 outbreak. However, some limitations should be acknowledged. We analysed a large sample of Italian participants. Nevertheless, the sample comprised a higher prevalence of women, and participants were recruited by adopting a non‐probabilistic sampling technique. Moreover, the evaluation of sleep features, chronotype and psychological well‐being relied on self‐reported questionnaires. In this view, caution is required in generalising the present findings due to possible selection and response biases that could affect our data. Future studies should include an objective assessment of sleep patterns (e.g. through actigraphy) to confirm our results. Finally, due to the unpredictable nature of the COVID‐19 outbreak, no data that are referred to the pre‐pandemic period were available. Longitudinal studies including a pre‐pandemic assessment could provide a more comprehensive overview of the situation.

In conclusion, our study described a promising scenario after 2 years of the pandemic. We demonstrated decreased sleep disturbances, insomnia, depressive and anxiety symptoms. However, the extent of the improvements was relatively small. Meanwhile, the re‐establishment of pre‐pandemic social and working dynamics configured a negative effect on sleep duration, which was reduced among the overall sample, and more strongly in particular population groups such as younger and evening‐type people. Finally, the persistence of high stress levels and the decreased distress differences between age groups, genders and chronotypes suggest that people transversely continued to feel the burden of this unprecedented and protracted historical period. In this view, further long‐term monitoring of sleep and mental health time course is necessary to claim the end of the COVID‐19 emergency on sleep and psychological status of the general population.

AUTHOR CONTRIBUTIONS

Federico Salfi: conceptualization, methodology, investigation, data curation, formal analysis, writing – original draft, writing – review and editing. Giulia Amicucci: investigation. Domenico Corigliano: investigation. Lorenzo Viselli: investigation. Aurora D'Atri: writing – review and editing. Daniela Tempesta: writing – review and editing. Maurizio Gorgoni: writing – review and editing. Serena Scarpelli: writing – review and editing. Valentina Alfonsi: writing – review and editing. Michele Ferrara: conceptualization, methodology, investigation, supervision, writing – review and editing.

CONFLICT OF INTEREST

None of the authors has potential conflicts of interest to be disclosed. All authors have seen and approved the manuscript.

Supporting information

TABLE S1 Descriptive statistics (mean [SD]) of sleep variables (PSQI, ISI, total sleep time, bedtime, get‐up time) and psychological variables (BDI‐II, PSS‐10, STAI‐X1 score) in the three survey waves (survey wave 1: April 2020; survey wave 2: December 2020; survey wave 3: April 2022)

Click here for additional data file. (70.1KB, pdf)

Salfi, F. , Amicucci, G. , Corigliano, D. , Viselli, L. , D'Atri, A. , Tempesta, D. , Gorgoni, M. , Scarpelli, S. , Alfonsi, V. , & Ferrara, M. (2022). Two years after lockdown: Longitudinal trajectories of sleep disturbances and mental health over the COVID‐19 pandemic, and the effects of age, gender and chronotype. Journal of Sleep Research, e13767. 10.1111/jsr.13767

DATA AVAILABILITY STATEMENT

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

  1. Adan, A. , Archer, S. N. , Hidalgo, M. P. , di Milia, L. , Natale, V. , & Randler, C. (2012). Circadian typology: A comprehensive review. Chronobiology International, 29(9), 1153–1175. 10.3109/07420528.2012.719971 [DOI] [PubMed] [Google Scholar]
  2. Alfonsi, V. , Gorgoni, M. , Scarpelli, S. , Zivi, P. , Sdoia, S. , Mari, E. , Fraschetti, A. , Ferlazzo, F. , Giannini, A. M. , & de Gennaro, L. (2021). COVID‐19 lockdown and poor sleep quality: Not the whole story. Journal of Sleep Research, 30(5), 1–6. 10.1111/jsr.13368 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. AlRasheed, M. M. , Fekih‐Romdhane, F. , Jahrami, H. , Pires, G. N. , Saif, Z. , Alenezi, A. F. , Humood, A. , Chen, W. , Dai, H. , Bragazzi, N. , Pandi‐Perumal, S. R. , BaHammam, A. S. , Vitiello, M. V. , & Comity Investigators . (2022). The prevalence and severity of insomnia symptoms during COVID‐19: A global systematic review and individual participant data meta‐analysis. Sleep Medicine, 100, 7–23. 10.1016/j.sleep.2022.06.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Amicucci, G. , Salfi, F. , D'atri, A. , Viselli, L. , & Ferrara, M. (2021). The differential impact of COVID‐19 lockdown on sleep quality, insomnia, depression, stress, and anxiety among late adolescents and elderly in Italy. Brain Sciences, 11(10), 1336. 10.3390/BRAINSCI11101336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Basishvili, T. , Oniani, N. , Sakhelashvili, I. , Eliozishvili, M. , Khizanashvili, M. , Arabidze, M. , Tsaava, M. , Charekishvili, T. , Tsertsvadze, N. , & Darchia, N. (2021). Insomnia, pre‐sleep arousal, psychosocial factors and changes in sleep pattern during the second wave lockdown of the COVID‐19 pandemic in Georgia. Brain Sciences, 12(1), 17. 10.3390/brainsci12010017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bates, D. , Mächler, M. , Bolker, B. M. , & Walker, S. C. (2015). Fitting linear mixed‐effects models using lme4. Journal of Statistical Software, 67(1), 1–48. 10.18637/JSS.V067.I01 [DOI] [Google Scholar]
  7. Benke, C. , Autenrieth, L. K. , Asselmann, E. , & Pané‐Farré, C. A. (2022). One year after the COVID‐19 outbreak in Germany: Long‐term changes in depression, anxiety, loneliness, distress and life satisfaction. European Archives of Psychiatry and Clinical Neuroscience, 123456789, 20–23. 10.1007/s00406-022-01400-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bottary, R. , Fields, E. C. , Kensinger, E. A. , & Cunningham, T. J. (2022). Age and chronotype influenced sleep timing changes during the first wave of the COVID‐19 pandemic. Journal of Sleep Research, 31(2), 1–7. 10.1111/jsr.13495 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Casagrande, M. , Favieri, F. , Tambelli, R. , & Forte, G. (2020). The enemy who sealed the world: Effects quarantine due to the COVID‐19 on sleep quality, anxiety, and psychological distress in the Italian population. Sleep Medicine, 75, 12–20. 10.1016/J.SLEEP.2020.05.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Castronovo, V. , Galbiati, A. , Marelli, S. , Brombin, C. , Cugnata, F. , Giarolli, L. , Anelli, M. M. , Rinaldi, F. , & Ferini‐Strambi, L. (2016). Validation study of the Italian version of the insomnia severity index (ISI). Neurological Sciences, 37(9), 1517–1524. 10.1007/s10072-016-2620-z [DOI] [PubMed] [Google Scholar]
  11. Cellini, N. , Canale, N. , Mioni, G. , & Costa, S. (2020). Changes in sleep pattern, sense of time and digital media use during COVID‐19 lockdown in Italy. Journal of Sleep Research, 29(4), 1–6. 10.1111/jsr.13074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chodkiewicz, J. , Miniszewska, J. , Krajewska, E. , & Biliński, P. (2021). Mental health during the second wave of the covid‐19 pandemic—Polish studies. International Journal of Environmental Research and Public Health, 18(7), 3423. 10.3390/ijerph18073423 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Conte, F. , Cellini, N. , de Rosa, O. , Rescott, M. L. , Malloggi, S. , Giganti, F. , & Ficca, G. (2021). Dissociated profiles of sleep timing and sleep quality changes across the first and second wave of the COVID‐19 pandemic. Journal of Psychiatric Research, 143(September), 222–229. 10.1016/j.jpsychires.2021.09.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Curcio, G. , Tempesta, D. , Scarlata, S. , Marzano, C. , Moroni, F. , Rossini, P. M. , Ferrara, M. , & de Gennaro, L. (2013). Validity of the Italian version of the Pittsburgh sleep quality index (PSQI). Neurological Sciences, 34(4), 511–519. 10.1007/s10072-012-1085-y [DOI] [PubMed] [Google Scholar]
  15. Daly, M. , & Robinson, E. (2022). Psychological distress associated with the second COVID‐19 wave: Prospective evidence from the UK household longitudinal study. Journal of Affective Disorders, 310(April), 274–278. 10.1016/j.jad.2022.05.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Daly, M. , Sutin, A. , & Robinson, E. (2020). Longitudinal changes in mental health and the COVID‐19 pandemic: Evidence from the UK household longitudinal study. Psychological Medicine, 13, 1–10. 10.1017/S0033291720004432 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Elmer, T. , Mepham, K. , & Stadtfeld, C. (2020). Students under lockdown: Comparisons of students' social networks and mental health before and during the COVID‐19 crisis in Switzerland. PLoS One, 15(7), e0236337. 10.1371/JOURNAL.PONE.0236337 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Jahrami, H. , Alhaj, O. A. , Humood, A. M. , Alenezi, A. F. , Fekih‐Romdhane, F. , AlRasheed, M. M. , Saif, Z. Q. , Bragazzi, N. L. , Pandi‐Perumal, S. R. , BaHammam, A. S. , & Vitiello, M. (2022). Sleep disturbances during the COVID‐19 pandemic: A systematic review, meta‐analysis, and meta‐regression. Sleep Medicine Reviews, 62, 101591. 10.1016/j.smrv.2022.101591 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kamel, N. S. , & Gammack, J. K. (2006). Insomnia in the elderly: Cause, approach, and treatment. The American Journal of Medicine, 119(6), 463–469. 10.1016/J.AMJMED.2005.10.051 [DOI] [PubMed] [Google Scholar]
  20. Kantermann, T. (2020). Behavior: How a global social lockdown unlocks time for sleep. Current Biology, 30(14), R822–R823. 10.1016/j.cub.2020.06.037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Korman, M. , Tkachev, V. , Reis, C. , Komada, Y. , Kitamura, S. , Gubin, D. , Kumar, V. , & Roenneberg, T. (2020). COVID‐19‐mandated social restrictions unveil the impact of social time pressure on sleep and body clock. Scientific Reports, 10(1), 1–10. 10.1038/s41598-020-79299-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lenth, R. V. , Buerkner, P. , Herve, M. , Love, J. , Miguez, F. , Riebl, H. , & Singmann, H. (2022). Package ‘emmeans’: Estimated marginal means, aka least‐squares means. Version, 1(7), 5. https://cran.r-project.org/web/packages/emmeans/emmeans.pdf [Google Scholar]
  23. Leone, M. J. , Sigman, M. , & Golombek, D. A. (2020). Effects of lockdown on human sleep and chronotype during the COVID‐19 pandemic. Current Biology, 30(16), R930–R931. 10.1016/j.cub.2020.07.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li, J. , Vitiello, M. , & Gooneratne, N. S. (2018). Sleep in normal aging. Sleep Medicine Clinics, 13(1), 1–11. 10.1016/J.JSMC.2017.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Liu, Y. , Wang, X. , Sun, P. , Zhang, Q. , Zhang, C. , Shen, Y. , Wang, S. , Ma, J. , & Wang, G. (2022). Sleep disturbance and anxiety symptom among public during the second wave of COVID‐19 in Beijing: A web‐based cross‐sectional survey. Journal of Affective Disorders, 298, 80–85. 10.1016/j.jad.2021.10.068 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Luke, S. G. (2017). Evaluating significance in linear mixed‐effects models in R . Behavior Research Methods, 49(4), 1494–1502. 10.3758/S13428-016-0809-Y [DOI] [PubMed] [Google Scholar]
  27. Madrid‐Valero, J. J. , Martínez‐Selva, J. M. , Ribeiro do Couto, B. , Sánchez‐Romera, J. F. , & Ordoñana, J. R. (2017). Age and gender effects on the prevalence of poor sleep quality in the adult population. Gaceta Sanitaria, 31(1), 18–22. 10.1016/J.GACETA.2016.05.013 [DOI] [PubMed] [Google Scholar]
  28. Manchia, M. , Gathier, A. W. , Yapici‐Eser, H. , Schmidt, M. V. , de Quervain, D. , van Amelsvoort, T. , Bisson, J. I. , Cryan, J. F. , Howes, O. D. , Pinto, L. , van der Wee, N. J. , Domschke, K. , Branchi, I. , & Vinkers, C. H. (2022). The impact of the prolonged COVID‐19 pandemic on stress resilience and mental health: A critical review across waves. European Neuropsychopharmacology, 55, 22–83. 10.1016/J.EURONEURO.2021.10.864 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Massar, S. A. A. , Ng, A. S. C. , Soon, C. S. , Ong, J. L. , Chua, X. Y. , Chee, N. I. Y. N. , Lee, T. S. , & Chee, M. W. L. (2021). Reopening after lockdown: The influence of working‐from‐home and digital device use on sleep, physical activity, and wellbeing following COVID‐19 lockdown and reopening. Sleep, 45(1), zsab250. 10.1093/SLEEP/ZSAB250 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Merikanto, I. , Kortesoja, L. , Benedict, C. , Chung, F. , Cedernaes, J. , Espie, C. A. , Morin, C. M. , Dauvilliers, Y. , Partinen, M. , de Gennaro, L. , Wing, Y. K. , Chan, N. Y. , Inoue, Y. , Matsui, K. , Holzinger, B. , Plazzi, G. , Mota‐Rolim, S. A. , Leger, D. , Penzel, T. , & Bjorvatn, B. (2022). Evening‐types show highest increase of sleep and mental health problems during the COVID‐19 pandemic‐multinational study on 19,267 adults. Sleep, 45(2), 1–13. 10.1093/sleep/zsab216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mondo, M. , Sechi, C. , & Cabras, C. (2021). Psychometric evaluation of three versions of the Italian perceived stress scale. Current Psychology, 40(4), 1884–1892. 10.1007/S12144-019-0132-8/TABLES/5 [DOI] [Google Scholar]
  32. Natale, V. , Esposito, M. J. , Martoni, M. , & Fabbri, M. (2006). Validity of the reduced version of the morningness‐eveningness questionnaire. Sleep and Biological Rhythms, 4(1), 72–74. 10.1111/j.1479-8425.2006.00192.x [DOI] [Google Scholar]
  33. Patel, D. , Steinberg, J. , & Patel, P. (2018). Insomnia in the elderly: A review. Journal of Clinical Sleep Medicine, 14(6), 1017–1024. 10.5664/JCSM.7172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Robinson, E. , Sutin, A. R. , Daly, M. , & Jones, A. (2022). A systematic review and meta‐analysis of longitudinal cohort studies comparing mental health before versus during the COVID‐19 pandemic in 2020. Journal of Affective Disorders, 296, 567–576. 10.1016/j.jad.2021.09.098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Roenneberg, T. , Pilz, L. K. , Zerbini, G. , & Winnebeck, E. C. (2019). Chronotype and social jetlag: A (self‐) critical review. Biology, 8(3), 1–19. 10.3390/biology8030054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Rossi, R. , Socci, V. , Talevi, D. , Mensi, S. , Niolu, C. , Pacitti, F. , di Marco, A. , Rossi, A. , Siracusano, A. , & di Lorenzo, G. (2020). COVID‐19 pandemic and lockdown measures impact on mental health among the general population in Italy. Frontiers in Psychiatry, 11, 790. 10.3389/FPSYT.2020.00790/BIBTEX [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Rus Prelog, P. , Matić, T. , Pregelj, P. , & Sadikov, A. (2022). Risk of depression, anxiety, and stress during the second wave of COVID‐19 in Slovenia. Frontiers in Psychiatry, 12(January), 1–9. 10.3389/fpsyt.2021.788898 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Salfi, F. , D'Atri, A. , Amicucci, G. , Viselli, L. , Gorgoni, M. , Scarpelli, S. , Alfonsi, V. , & Ferrara, M. (2022). The fall of vulnerability to sleep disturbances in evening chronotypes when working remotely and its implications for depression. Scientific Reports, 12, 12249. 10.1038/s41598-022-16256-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Salfi, F. , D'Atri, A. , Tempesta, D. , & Ferrara, M. (2021a). Sleeping under the waves: A longitudinal study across the contagion peaks of the COVID‐19 pandemic in Italy. Journal of Sleep Research, 30(5), e13313. 10.1111/jsr.13313 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Salfi, F. , Lauriola, M. , Amicucci, G. , Corigliano, D. , Viselli, L. , Tempesta, D. , & Ferrara, M. (2020). Gender‐related time course of sleep disturbances and psychological symptoms during the COVID‐19 lockdown: A longitudinal study on the Italian population. Neurobiology of Stress, 13, 100259. 10.1016/J.YNSTR.2020.100259 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Salfi, F. , Lauriola, M. , D'Atri, A. , Amicucci, G. , Viselli, L. , Tempesta, D. , & Ferrara, M. (2021b). Demographic, psychological, chronobiological, and work‐related predictors of sleep disturbances during the COVID‐19 lockdown in Italy. Scientific Reports, 11(1), 1–12. 10.1038/s41598-021-90993-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Sampogna, G. , Giallonardo, V. , del Vecchio, V. , Luciano, M. , Albert, U. , Carmassi, C. , Carrà, G. , Cirulli, F. , Dell'Osso, B. , Menculini, G. , Belvederi Murri, M. , Pompili, M. , Sani, G. , Volpe, U. , Bianchini, V. , & Fiorillo, A. (2021). Loneliness in young adults during the first wave of COVID‐19 lockdown: Results from the multicentric COMET study. Frontiers in Psychiatry, 12, 2226. 10.3389/FPSYT.2021.788139/BIBTEX [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sanavio, E. , Bertolotti, G. , Michelin, P. , Vidotto, G. , & Zotti, A. M. (1998). CBA 2.0–Cognitive Behavioural Assessment 2.0‐Scale Primarie: Manuale. Organizzazioni Speciali.
  44. Sica, C. , & Ghisi, M. (2007). The Italian versions of the Beck anxiety inventory and the Beck depression inventory‐II: Psychometric properties and discriminant power. In Leading‐edge psychological tests and testing research (pp. 27–50). Nova Science Publishers. [Google Scholar]
  45. Spielberger, C. D. , Gorsuch, R. L. , & Lushene, R. E. (1970). The state‐trait anxiety inventory (STAI) test manual for form X. (tr. It.:). Consulting Psychologist Press. [Google Scholar]
  46. Trakada, A. , Nikolaidis, P. T. , Economou, N. T. , Kallianos, A. , Nena, E. , Steiropoulos, P. , Knechtle, B. , & Trakada, G. (2022). Comparison of sleep characteristics during the first and second period of restrictive measures due to COVID‐19 pandemic in Greece. European Review for Medical and Pharmacological Sciences, 26(4), 1382–1387. 10.26355/eurrev_202202_28131 [DOI] [PubMed] [Google Scholar]
  47. Ueda, M. , Stickley, A. , Sueki, H. , & Matsubayashi, T. (2020). Mental health status of the general population in Japan during the COVID‐19 pandemic. Psychiatry and Clinical Neurosciences, 74(9), 505–506. 10.1111/PCN.13105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Viselli, L. , Salfi, F. , D'atri, A. , Amicucci, G. , & Ferrara, M. (2021). Sleep quality, insomnia symptoms, and depressive symptomatology among italian university students before and during the COVID‐19 lockdown. International Journal of Environmental Research and Public Health, 18(24), 13346. 10.3390/ijerph182413346 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Weissbourd, R. , Batanova, M. , Lovison, V. , & Torres, E. (2020). Loneliness in america how the pandemic has deepened an epidemic of loneliness and what we can do about it. Harvard Graduate School of Education: Making Caring Common.
  50. Wetherall, K. , Cleare, S. , McClelland, H. , Melson, A. J. , Niedzwiedz, C. L. , O'Carroll, R. E. , O'Connor, D. B. , Platt, S. , Scowcroft, E. , Watson, B. , Zortea, T. , Ferguson, E. , Robb, K. A. , & O'Connor, R. C. (2022). Mental health and well‐being during the second wave of COVID‐19: Longitudinal analyses of the UK COVID‐19 mental health and wellbeing study (UK COVID‐MH). BJPsych Open, 8(4), 1–10. 10.1192/bjo.2022.58 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

TABLE S1 Descriptive statistics (mean [SD]) of sleep variables (PSQI, ISI, total sleep time, bedtime, get‐up time) and psychological variables (BDI‐II, PSS‐10, STAI‐X1 score) in the three survey waves (survey wave 1: April 2020; survey wave 2: December 2020; survey wave 3: April 2022)

Click here for additional data file. (70.1KB, pdf)

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Articles from Journal of Sleep Research are provided here courtesy of Wiley

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