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Review

Changes in Sleep Duration and Sleep Timing in the General Population from before to during the First COVID-19 Lockdown: A Systematic Review and Meta-Analysis

by
Chiara Ceolin
1,2,†,
Federica Limongi
3,†,
Paola Siviero
3,*,
Caterina Trevisan
1,2,4,
Marianna Noale
3,
Filippo Catalani
1,
Silvia Conti
5,6,
Elisa Di Rosa
7,
Elena Perdixi
6,8,
Francesca Remelli
4,
Federica Prinelli
5 and
Stefania Maggi
3
1
Geriatric Unit, Department of Medicine (DIMED), University of Padova, 35128 Padova, Italy
2
Department of Neurobiology, Care Sciences and Society, Aging Research Center, Karolinska Institutet and Stockholm University, 17165 Solna, Sweden
3
Neuroscience Institute, Aging Branch, National Research Council, 35128 Padova, Italy
4
Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy
5
Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
6
Neuropsychology Lab, Centre for Cognitive Disorders and Dementia IRCCS Mondino Foundation, 27100 Pavia, Italy
7
Department of General Psychology, University of Padua, 35131 Padova, Italy
8
Department of Neurology, IRCCS Humanitas Research Hospital, 20089 Milan, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work and share first authorship.
Int. J. Environ. Res. Public Health 2024, 21(5), 583; https://doi.org/10.3390/ijerph21050583
Submission received: 18 March 2024 / Revised: 22 April 2024 / Accepted: 26 April 2024 / Published: 2 May 2024
(This article belongs to the Section Global Health)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: The COVID-19 lockdown had a profound effect on everyday life, including sleep health. This systematic review and meta-analysis evaluated changes in quantitative sleep parameters during the first lockdown compared with pre-lockdown in the general population. Methods: A search in scientific databases was performed to identify eligible observational studies from inception to 8 February 2023. We performed a random effects meta-analysis of those studies reporting (a) means of sleep duration, time in bed (TIB), and sleep timing (bedtime and wake-up time); (b) the percentages of atypical sleep duration before and during the lockdown; (c) the percentages of change in sleep duration and sleep timing. Results: A total of 154 studies were included. A small increase in sleep duration (0.25 standardized mean difference, 95% CI 0.180–0.315) was found, with 55.0% of the individuals reporting changes, predominantly an increase (35.2%). The pooled relative risk for sleeping more than 8/9 h per night was 3.31 (95% IC 2.60–4.21). There was a moderately significant delay in sleep timing and a surge in napping. Conclusion: An increase in sleep duration and napping, and delayed sleep timing were observed. High-quality studies should evaluate whether these parameters have now become chronic or have returned to pre-lockdown values.

1. Introduction

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has brought about profound changes globally, impacting public health, society, economy, and daily life for billions. The rapid spread of cases prompted a global health emergency declaration by the World Health Organization (WHO) on 30 January 2020 [1]. Measures such as hand hygiene, face masks, isolation, quarantine, and lockdowns were implemented in order to limit the diffusion of the virus and to mitigate the burden on health systems, causing a drastic shift in social dynamics [2]. Physical distancing and isolation affected daily habits, including work schedules, exposure to natural light, and reduced physical exercise opportunities [3]. Stress levels rose due to fear of the unknown disease, health concerns, and economic repercussions [3]. Changes in routines disrupted daily rhythms and energy balance, affecting various biological clock regulators [4].
Specifically, considering the vulnerability of the sleep system to cognitive-physiological stress (also known as sleep reactivity), sleep health was significantly compromised during the COVID-19 lockdown [5]. In fact, it is known that ruminating on stressful factors can activate processes that disrupt sleep, and that sleeping difficulty during stressful periods promotes repetitive thinking as the inability to fall asleep creates an unstructured stressful period in bed [5]. Sleep disturbances can present in various forms, including insomnia, disrupted sleep, daytime symptoms such as involuntary drowsiness, difficulty falling or staying asleep, delayed bedtime, abnormal sleep behaviors, and nightmares [5]. According to several systematic reviews and meta-analyses, sleep disturbances were common in different segments of the population during the COVID-19 pandemic [6,7,8,9,10].
Our previous systematic review and meta-analysis found a worsened sleep quality and increased sleep disturbances in the general population during the COVID-19 lockdown compared with pre-lockdown levels [11]. The COVID-19 pandemic and confinement measures led to changes in other relevant sleep parameters, such as delayed bedtime and increased sleep duration in the general population [12,13,14]. This pattern has been confirmed by two systematic reviews and meta-analyses evaluating the impact of the COVID-19 pandemic on sleep health [15,16]. Al-Ajlouni reported a negative impact on sleep with an increase in the prevalence of short or long sleep duration among different populations residing in the Middle East and North Africa (MENA) [15]. Cui’s systematic review and meta-analysis uncovered an increase in sleep duration among healthy adults during the COVID-19 lockdown [16]. However, both works have limitations: the former focused exclusively on the MENA region, limiting its generalizability [15], while the latter included only six studies that evaluated changes in sleep duration [16]. Moreover, there are no systematic reviews that investigated changes in other sleep dimensions, such as napping habits and atypical sleep duration.
The current systematic review and meta-analysis aims to overcome these limitations by providing a more comprehensive understanding of the changes in several quantitative sleep parameters during the first COVID-19 lockdown in the general population. We chose to concentrate on the first lockdown period, as investigations conducted thereafter may have been susceptible to the mitigation of restrictions, potentially resulting in varied effects on people’s sleep patterns. In particular, we evaluated changes in sleep duration, time in bed (TIB), sleep timing (bedtime and wake-up time), and napping habits during versus before the first COVID-19 lockdown.

2. Materials and Methods

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [17], and it was registered in PROSPERO, CRD42021256378.

2.1. Search Strategy and Selection Criteria

We systematically searched four academic electronic databases (PubMed, Cochrane Library, Ebsco, and Web of Science-WOS), a preprint server (MedRxiv), and a gray literature database (OpenGrey) from inception to 28 May 2021; an updated search through 8 February 2023 was also carried out (PubMed and WOS). The full search strategy and the search terms used for each database are described in Appendix A (Table A1). The reference lists of relevant systematic reviews and articles were manually searched for additional studies. All the references were downloaded into Zotero, and this citation manager software was used for every stage of the selection process, from downloading and removing duplicates to screening the abstract titles and the full-texts. The abstract titles and full-texts were screened independently by three authors (E.P., F.R., and F.L.). Any disagreements were solved by consulting the senior authors (S.M., C.T., F.P., and M.N.).

2.2. Inclusion and Exclusion Criteria

Original observational cross-sectional or longitudinal studies that assessed changes in sleep characteristics (using self-reported or objective measures) during the first COVID-19 lockdown (hereafter lockdown) compared with before the lockdown (hereafter before) in the general adult population (adults ≥ 18 years) were considered eligible for inclusion.
Studies were excluded if they: (1) were not observational; (2) were reported in languages other than English, Italian, or Spanish; (3) evaluated changes only in subjects with specific diseases (e.g., obesity, diabetes, neuromuscular disease, cancer, osteoarthritis, and dementia) or in specific groups of individuals (e.g., healthcare workers, professional athletes); (4) collected data outside the timeframe of the first lockdown.

2.3. Outcomes

The current work is the continuum of a previous systematic review and meta-analysis on sleep quality and sleep disturbances in the general population from before to during the COVID-19 lockdown [11].
In particular, this study focuses on changes in the following quantitative sleep parameters:
  • Sleep duration, i.e., the amount of time that a person sleeps;
  • Sleep timing, which refers to bedtime, the time the person goes to bed, and wake-up time, the time the person awakes in the morning;
  • The total duration spent in bed, namely TIB, encompassing both the time dedicated to sleep and any additional time spent lying in bed, whether awake or in a state of rest;
  • Napping habits that refer to the sleep time beyond the main sleep period (percentage of participants tacking nap, length, and frequency).

2.4. Data Extraction

Data were extracted by four authors (F.R., E.P., F.L., and P.S.) using a pre-designed Excel spreadsheet. For each included study, the following information was recorded: first author’s name, year of publication, country, study design, assessment period, outcome, population, sample size, percentage of women, participants’ age (mean, median, or interval/age range), data collection method, type of recruitment, and type of measures utilized. The corresponding author was contacted whenever any study appeared incomplete or needed clarification on the presented data.

2.5. Risk of Bias Assessment

The Newcastle Ottawa Scale (NOS) for longitudinal [18] and cross-sectional studies [19] was used by two independent authors (F.R. and E.P.) to evaluate the risk of bias. The NOS allows one to judge a study based on three aspects: the selection of the study groups, the comparability of the groups, and the ascertainment of the outcome of interest. Cross-sectional studies can achieve a score of 0–10, and longitudinal studies can achieve a score of 0–9, with higher scores corresponding to a lower risk of bias. Studies whose NOS < 5 are classified as having a low quality and a high risk of bias [20]. A third author (M.N.) was involved in resolving any discrepancies.

2.6. Data Analysis

A meta-analysis was performed for data on outcomes that were sufficiently homogenous in terms of statistical and methodological characteristics. Additionally, a qualitative synthesis was performed to synthesize the findings of the studies that were not included in the meta-analysis. A random-effects meta-analysis was carried out for the sleep duration, TIB, and sleep timing (bedtime and wake-up time) outcomes, using the DerSimonian and Laird method; the studies were weighted according to the inverse of the standard error using the MedCalc Statistical Software version 20.118 [21].
Studies reporting the following data were included in the meta-analysis:
  • Mean sleep duration, TIB, and sleep timing before and during the lockdown;
  • The percentages of change in sleep duration (increased, decreased, no change) or in bedtime, and wake-up time (delayed, earlier, no change) during the lockdown vs. before; the percentage of atypical sleep duration (short sleep duration < 7 h/night and long sleep duration > 8 h/night) before and during the lockdown.
For each data type, the effect was expressed as a standardized mean difference (SMD)—this effect was interpreted using Cohen J. 1988 as 0.2 < small < 0.5, 0.5 ≤ medium < 0.8, large ≥ 0.8 [22], proportions, or relative risks. The between-study heterogeneity was analyzed using the I² statistic, where a value of 0% indicates no observed heterogeneity, and higher values show increasing heterogeneity [23]. The publication bias was assessed using Egger’s test [24], and, in case of possible bias (Egger’s p ≤ 0.05), we conducted one-study-removed sensitivity analyses.

2.7. Subgroup Analysis

Whenever possible, we stratified the meta-analysis by the risk of bias (NOS < 5 vs. NOS ≥ 5) and by country’s area. Countries were grouped into 7 areas: North America (Canada, USA), South America (Argentina, Brazil, Mexico, Peru), Central Asia (Bangladesh, India, Malaysia, Nepal, Pakistan), East Asia (China, Japan, Singapore), West Asia (Iran, Jordan, Kuwait, Lebanon, Saudi Arabia, United Arab Emirates), Europe (France, Germany, Hungary, Netherlands, Poland, Romania, UK), Mediterranean Europe (Cyprus, Greece, Italy, Portugal, Spain, Catalonia, Turkey). T-test and Chi-squared tests were used to compare subgroups.

3. Results

Overall, 6289 records were retrieved via databases and registers and 20 additional studies were identified via citation searches and systematic reviews. After the two-steps screening process, a total of 154 records were included (for the PRISMA flow diagram, please see Figure 1). The characteristics of the included studies are shown in Table 1 [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178]. Studies were conducted in Argentina (N = 3), Australia (N = 3), Austria (N = 1), Bangladesh (N = 2), Brazil (N = 4), Canada (N = 2), China (N = 6), Colombia (N = 1), Croatia (N = 2), Cyprus (N = 1), Egypt (N = 1), France (N = 6), Germany (N = 2), Greece (N = 4), Hungary (N = 1), India (N = 8), Iran (N = 2), Italy (N = 14), Japan (N = 2), Jordan (N = 2), Kuwait (N = 1), Lebanon (N = 1), Libya (N = 1), Malaysia (N = 1), Mexico (N = 6), Morocco (N = 1), Nepal (N = 1), Netherlands (N = 1), New Zealand (N = 1), Pakistan (N = 3), Peru (N = 1), Poland (N = 4), Portugal (N = 1), Romania (N = 1), Russia (N = 2), Saudi Arabia (N = 5), Singapore (N = 2), South Africa (N = 1), Spain (N = 15), Turkey (N = 1), UK (N = 9), Ukraine (N = 1), United Arab Emirates (N = 2), USA (N = 13), and 12 were carried out in multiple countries. Due to the lockdown, most studies collected data via online surveys. One hundred and forty-five studies used self-reported instruments [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,53,54,55,56,57,58,59,60,61,62,63,64,65,66,68,90,91,92,93,94,95,96,97,98,99,100,101,102,104,105,106,107,109,110,111,112,113,114,115,116,117,118,119,120,121,123,124,125,127,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178]; seven studies used objective measures [67,69,103,108,122,128,146]; and two studies used both [52,126]. One hundred and thirty studies were cross-sectional, whereas twenty-four were longitudinal.
The mean NOS score for the cross-sectional studies was 4.4 (SD = 1.3; range 2−8), and it was 6.2 (SD = 1.3; range 3−9) for the longitudinal ones (Appendix A Table A2). Overall, 53.9% had a good quality and had a low risk of bias (NOS ≥ 5), representing 96% of the longitudinal and 46% of the cross-sectional studies.

3.1. Sleep Duration

Among the 132 studies that examined sleep duration, 107 were included in the meta-analysis, and 25 were narratively described [25,48,55,75,80,87,88,102,104,106,108,113,123,126,128,132,141,144,145,156,159,162,163,169,171].

3.1.1. Meta-Analytic Changes in Sleep Duration: Means before and during the Lockdown

The changes in sleep duration were evaluated considering 69 outcomes reported in 44 studies [27,32,39,40,44,45,46,49,50,51,52,57,63,66,68,70,74,81,85,93,95,97,103,107,111,112,114,115,119,120,122,129,130,136,137,143,146,153,160,161,173,176,177,178].
The participants reported a small increase in sleep duration (hours) (SMD = 0.25; 95% CI 0.18–0.32; I2 = 97.2%) (Figure 2); the analysis did not show a significant publication bias (Egger’s p = 0.63).
Subgroups analysis by the risk of bias did not find significant differences. The studies with a low risk of bias produced a higher effect on sleep duration than the overall set of studies, while those with a high risk of bias produced a lower effect. Specifically, the 54 outcomes of the 30 studies [27,32,40,44,49,50,51,52,57,68,70,81,85,93,97,103,107,115,120,122,129,130,136,137,146,153,160,173,176,177] with a low risk of bias (NOS ≥ 5) showed a significant increase of 0.26 SMD in sleep duration (95% CI 0.18–0.34; I2 = 97.5%; not significant Egger’s publication bias). Instead, the 15 outcomes of the 14 studies [39,45,46,63,66,74,95,111,112,114,119,143,161,178] with a high risk of bias (NOS < 5) showed a significant increase of 0.22 SMD in sleep duration (95% CI 0.11–0.33; I2 = 95.4%; not significant Egger’s publication bias).

3.1.2. Meta-Analytic Changes: Percentage of Change in Sleep Duration

The percentage change in sleep duration during lockdown with respect to before was evaluated by 51 studies [28,29,31,33,38,41,44,46,47,53,54,60,65,71,73,77,78,79,83,85,89,90,91,94,96,97,98,99,101,105,109,121,124,131,133,135,138,139,140,142,150,154,155,157,164,166,168,172,174,175,178].
As shown in Figure 3, the random effects model showed that 55.0% (95% CI 49.84–60.07; I2 = 99.6%) of the participants reported a change in sleep duration (hours). In particular, 19.7% reported a decrease (95% CI 16.80–22.87; I2 = 99.3%) and 35.2% reported an increase (95% CI 32.09–38.44; I2 = 99.2). Significant Eggers’s publication bias emerged for the above outcomes, but the sensitivity analyses confirmed the main findings.
Subgroup analysis by risk of bias uncovered significant differences. The studies with a low risk of bias produced a lower percentage of changes in sleep duration compared with the overall studies, while those with a high risk of bias produced a higher percentage. Specifically, the 24 studies [28,31,38,44,60,65,71,73,77,78,83,85,90,91,94,97,99,121,135,157,166,168,174,175] with a low risk of bias (NOS ≥ 5) showed that 48.8% of the participants reported a change in sleep duration (95% CI 41.54–56.12, I2 = 99.8%; significant Egger’s publication bias), 30.7% reported an increase (95% CI 26.13–35.41; I2 = 99.5%; not significant Egger’s publication bias), and 19.7% reported a decrease (95% CI 15.29–24.47; I2 = 99.6%; not significant Egger’s publication bias). Instead, the 27 studies [29,33,41,46,47,53,54,79,89,96,98,101,105,109,124,131,133,138,139,140,142,150,154,155,164,172,178] with a high risk of bias (NOS < 5), showed that 60.8% of the participants reported a change in sleep duration (95% CI 56.64–64.85, I2 = 97.7%; not significant Egger’s publication bias), 39.1% reported an increase (95% CI 35.54–42.73; I2 = 98.0%; significant Egger’s publication bias), and 19.8% reported a decrease (95% CI 16.78–23.01; I2 = 97.3%; not significant Egger’s publication bias).
Subgroup analysis by country’s area was only possible for the percentages of change in sleep duration. As we can see from Table 2, the percentage of change in sleep duration was very high in South America (72%), lower in East Asia (45%), and it was around values above 50% in other areas. The change concerned the increase in sleep hours in all areas, especially in Central Asia (44%) and South America (40%), and it was around values above 30% in other areas. Significant differences were observed among all areas except North America (30.5%) vs. Europe (30.8%), and East Asia (33.3%) vs. Mediterranean Europe (33.8%).

3.1.3. Meta-Analytic Changes: Percentage of Atypical Sleep Duration before and during the Lockdown

Changes in the percentage of participants with atypical sleep duration were evaluated in 18 studies [26,30,34,44,45,59,60,61,62,72,86,92,93,134,138,151,158,167].
Compared with the pre-lockdown period, the percentage of participants with atypical sleep duration increased by approximately 17% during the lockdown (pooled relative risk = 1.17, 95% CI 1.04–1.31; I2 = 98.2; not significant publication bias) (see Figure 4).
Data analysis showed that the percentage of those participants who slept more than 8/9 h increased during lockdown; a pooled relative risk of 3.31 was observed (95% CI 2.60–4.21; I2 = 95.2, not significant publication bias) [26,30,34,44,45,59,60,61,62,72,86,92,93,134,138,151,158].
Data analysis also uncovered that, with respect to before, the percentage of participants who slept less than 6/7 h decreased during lockdown; a pooled relative risk of 0.82 was observed (95% CI 0.73–0.91; I2 = 98.7; not significant publication bias) [26,30,32,34,35,44,45,59,60,61,62,72,86,92,93,107,110,112,114,117,134,138,149,158].

3.2. Time in Bed

Among the 15 studies that examined TIB, 12 were included in the meta-analysis and 3 were narratively described [69,88,126].

Meta-Analytic Changes in Time in Bed: Means before and during the Lockdown

The changes in TIB were evaluated considering 20 outcomes derived from 12 studies [39,56,57,64,70,74,115,122,129,136,160,161]. The participants reported a small increase in TIB (SMD = 0.24; 95% CI 0.151–0.32; I2 = 90.6%; not significant Egger’s publication bias) (see Figure 5).
Subgroup analysis by risk of bias showed no significant differences in TIB: the studies with a low risk of bias produced a lower effect compared with the overall studies, while those with a high risk of bias produced a higher effect. Specifically, the 17 outcomes of the 9 studies [56,57,64,70,115,122,129,136,160] with a low risk of bias (NOS ≥ 5) showed a small but significant increase in TIB (SMD 0.26; 95% CI 0.16–036; I2 = 91.8%; not significant Egger’s publication bias); while the three studies [39,74,161] with a high risk of bias (NOS < 5) showed a small but not significant lower increase (SMD 0.15; 95% CI −0.03–0.34; I2 = 87.1%; not significant Egger’s publication bias).

3.3. Sleep Timing

Out of the 45 studies that examined bedtime, 30 were included in the meta-analysis and 15 were narratively described [27,37,42,50,76,88,100,119,126,127,146,156,159,169,171]. Out of the 51 studies that examined wake-up time, 36 were included in the meta-analysis, 14 were narratively described [27,37,42,74,76,88,100,106,119,146,148,156,169,171], and one study provided data both for the meta-analysis and the qualitative synthesis [126].

3.3.1. Meta-Analytic Changes in Bedtime: Means before and during the Lockdown

Changes in bedtime were evaluated considering 45 outcomes reported in 27 studies [36,39,43,49,56,57,63,64,67,68,70,82,85,103,107,115,120,122,125,130,136,137,143,153,160,170,173]. The participants reported a medium significant delay in bedtime (hours) of 0.51 SMD (95%CI 0.38–0.64; I2 = 98.5%; not significant Egger’s publication bias) (see Figure 6).
Subgroup analysis by risk of bias uncovered no significant differences. Specifically, the 32 outcomes of the 23 studies [36,43,49,56,57,64,67,68,70,82,85,103,107,115,120,122,125,130,136,137,153,160,173] with a low risk of bias (NOS ≥ 5) showed a small but significant delay of 0.47 SMD in bedtime (95% CI 0.33–0.62; I2 = 98.6%; not significant Egger’s publication bias). The four studies [39,63,143,170] with a high risk of bias (NOS < 5) showed that there was a large significant delay of 0.82 SMD in bedtime (95% CI 0.42–1.21; I2 = 94.4%; not significant Egger’s publication bias).

3.3.2. Meta-Analytic Changes in Bedtime: Percentage of Change

The percentage of participants who changed or maintained the same bedtime during the lockdown compared with the pre-lockdown period were evaluated by 14 studies [28,36,46,58,84,85,91,116,121,138,144,157,165,172]. As shown in Figure 7, the random effects model showed that 57.6% (95% CI 44.41–70.27, I2 = 99.97; not significant Egger’s publication bias) of the participants reported a change in bedtime. In particular, 42.9% reported a delayed bedtime (95% CI 30.93–55.29; I2 = 99.9; not significant Egger’s publication bias) and 11.9% said they went to bed earlier (95% CI 8.79–15.50; I2 = 99.1; not significant Egger’s publication bias).
Subgroup analysis by risk of bias revealed significant differences. Specifically, the seven studies [28,36,84,85,91,121,157] with a low risk of bias (NOS ≥ 5) showed that 54.9% of the participants reported a change in bedtime (95% CI 33.32–75.56, I2 = 99.9%; not significant Egger’s publication bias), 36.1% said they had a delayed bedtime (95% CI 16.02–59.14; I2 = 99.9%; not significant Egger’s publication bias), whereas 10.7% reported an earlier bedtime (95% CI 8.26–13.45; I2 = 96.8%; not significant Egger’s publication bias). Instead, the seven studies [46,58,116,138,144,165,172] with a high risk of bias (NOS < 5), revealed that 61.4% of the participants reported a change in bedtime (95% CI 56.16–66.47, I2 = 97.8%; significant Egger’s publication bias), 48.8% a delayed bedtime (95% CI 40.51–57.14; I2 = 99.5%; not significant Egger’s publication bias), and 13.3% an earlier one (95% CI 6.62–21.81; I2 = 99.5%; not significant Egger’s publication bias).

3.3.3. Meta-Analytic Changes in Wake-Up Time: Percentage of Change

Changes in the wake-up time were evaluated considering 37 outcomes reported in 28 studies [36,39,43,49,50,56,57,63,64,67,68,70,82,85,103,107,115,120,122,125,130,136,137,143,153,160,170,173]. Participants reported a medium significant delay in wake-up time of 0.78 SMD (95% CI 0.64–0.92; I2 = 98.5%; not significant Egger’s publication bias) (see Figure 8).
Subgroup analysis by risk of bias uncovered no significant differences. However, the 33 outcomes of the 21 studies [36,43,49,64,67,68,70,82,85,103,107,115,120,122,125,130,136,137,153,160,173] with a low risk of bias (NOS ≥ 5) showed a medium significant delay in wake-up time of 0.77 SMD (95% CI 0.63–0.92; I2 = 98.7%; not significant Egger’s publication bias). The four studies [39,63,143,170] with a high risk of bias (NOS < 5) showed a large significant delay in wake-up time of 0.87 SMD (95% CI 0.33–1.42; I2 = 97.0%; not significant Egger’s publication bias).

3.3.4. Meta-Analytic Changes in Wake-Up Time: Percentage of Change

The percentage change in wake-up time during the lockdown was evaluated by 11 studies [36,46,58,84,85,91,121,126,138,144,172]. As shown in Figure 9, the random effects model showed that 59.3% of the participants reported a change in wake-up time (95% CI 40.26–77.07, I2 = 99.9%; not significant Egger’s publication bias). In particular, 45.0% reported a delayed wake-up time (95% CI 28.38–62.30; I2 = 99.9%; not significant Egger’s publication bias) and 11.01% reported an earlier one (95% CI 8.35–13.98; I2 = 98.5%; not significant Egger’s publication bias).
Subgroup analysis by risk of bias showed significant differences. The studies with a low risk of bias produced lower percentages of change with respect to the overall studies, but those with a high risk of bias produced higher percentages of change. Specifically, the six studies [36,84,85,91,121,126] with a low risk of bias (NOS ≥ 5) showed that 51.3% of the participants reported a change in wake-up time (95% CI 21.75–80.35, I2 = 99.9%; not significant Egger’s publication bias), 30.4% reported a delayed wake-up time (95% CI 9.16–57.43; I2 = 99.9%; not significant Egger’s publication bias) and 12.0% reported an earlier one (95% 7.26–17.65; I2 = 98.9%; not significant Egger’s publication bias). Instead, the five studies [46,58,138,144,172] with a high risk of bias (NOS < 5) revealed that 69.1% of the participants reported a change in wake-up time (95% CI 62.82–74.96, I2 = 98.2%; not significant Egger’s publication bias) with 60.1% reporting a delayed wake-up time (95% CI 53.82–66.11; I2 = 97.9%; not significant Egger’s publication bias) and 10.2% an earlier one (95% CI 8.07–12.62; I2 = 94.2%; not significant Egger’s publication bias).

3.4. Qualitative Synthesis

3.4.1. Synthesis of Sleep Duration

Twenty-five studies not included in the meta-analysis evaluated changes in sleep duration (see Table 3). Fifteen reported an increase [25,48,75,102,106,108,126,128,141,145,156,159,162,163], four reported no changes [55,88,132,169], and three reported a decrease in sleep duration [80,87,123]. One study found an increase in sleep duration among students but a decrease among office workers [113]; another study reported an increased duration in the young and middle-aged participants but not in the older ones [104], and one study found longer sleep duration in the evening-type chronotype compared to the neither-type and morning-type groups [144]. These findings are consistent with the overall results of our meta-analysis.

3.4.2. Synthesis of Time in Bed

The three studies not included in the meta-analysis regarding changes in TIB found an increase during the lockdown compared to the pre-lockdown period [69,88,126] (see Table 3).

3.4.3. Synthesis of Sleep Timing

Fifteen studies not included in the meta-analysis evaluated changes in the bedtime (see Table 3). Ten reported delayed bedtime [27,37,42,50,76,88,119,159,169,171] and two earlier one [146,156]; two of the studies were unable to detect a clear change [100,127]. Finally, one study found no change in the overall sample but a greater delay in the evening chronotype group compared to the morning one [126].
Sixteen studies not included in the meta-analysis evaluated changes in the wake-up time (see Table 3). Eleven reported a delayed wake-up time [27,37,42,76,88,119,146,148,156,169,171] and one an earlier wake-up time [74]. Two of the studies were unable to identify a clear change [100,147] and two reported no change [106,126].
These findings are in line with the results of the meta-analysis, which found a delayed bedtime and wake-up time.

3.4.4. Synthesis of Napping Habits

The napping habits, evaluated in twenty-three studies, were only narratively described (see Table 3) [33,36,42,63,64,81,84,86,91,100,107,113,118,119,120,129,136,144,145,147,152,165,169].
Most studies found an increase in the frequency and length of naps as well as in the percentage of participants taking naps. According to Franceschini, while the good sleepers did not change or reduce the length of naps, the poor sleepers increased it [84]. Finally, Salfi found that a significantly higher proportion of the evening-type chronotype subjects reported changes in their napping habits with respect to the morning-type and neither-type chronotype subjects [144].

4. Discussion

This systematic review and meta-analysis uncovered significant changes in several quantitative sleep parameters of the general population from before to during the first COVID-19 lockdown. An increase in both sleep duration and the percentage of individuals with atypical sleep duration was detected. Moreover, the frequency of individuals reporting long sleep duration increased (≥8/9 h), as well as the time in bed and napping habits. Subgroup analysis by country’s area showed an increased sleep duration in all areas considered (North America, South America, Central Asia, East Asia, West Asia, Europe, Mediterranean Europe), prevalently in Central Asia and South America. In addition, we detected significant differences among all areas except for North America vs. Europe and East Asia vs. Mediterranean Europe. The most striking change that emerged from this study regards the sleep–wake cycle. Indeed, more than 40% of the participants reported a significant delay in bedtime and wake-up time.
When reading these data, it is necessary to consider that most of the studies are cross-sectional and have a high risk of bias that can lead to unreliable interpretations. In addition, due to the lockdown restrictions, most of the studies used self-reported data, which tend to be subject to recall and social desirability biases. This issue may have led to a selection bias by excluding non-regular Internet users, such as older people.
The shift to remote work and virtual classes due to the lockdown forced the majority to go slower and extend their timelines, allowing more time for sleep [179,180]. Considering the worldwide prevalence of insufficient sleep, this increased time to sleep could be seen as a beneficial effect of restrictive measures [181]. However, it is unclear whether the increase in sleep duration corresponded to better sleep quality, as some studies reported a worse sleep quality during the lockdown compared with the pre-lockdown levels [11,56]. Moreover, the finding of an increased time in bed should be interpreted with caution. In fact, several works reported a decrease in sleep efficiency, leading to the hypothesis that an increase in the time spent in bed did not necessarily correspond to a longer sleep duration [11,57].
Our findings align with previous systematic reviews and meta-analyses [15,16], as well as certain original studies [12,13,14], which have documented extended sleep duration and delayed bedtime patterns within the general population. However, comparing our results across geographical regions poses challenges [13]. For instance, Robbins et al. observed significant variations in sleep duration both before and during the COVID-19 pandemic, noting a lesser increase in Seoul compared to New York City, Los Angeles, London, and Stockholm [13]. When juxtaposed with the evolution of sleep parameters across the different phases of the COVID-19 pandemic, our study reveals several differences. For instance, a longitudinal investigation unveiled a shift in bedtime during the initial lockdown compared to the pre-lockdown period, followed by a reversal during subsequent lockdowns, eventually reverting to pre-pandemic norms. Similarly, the duration of time spent in bed mirrored this trend. Intriguingly, afternoon napping habits remained largely unaffected during these periods of lockdown [64]. These trends suggest an adaptation to the progression of restrictive measures, with researchers hypothesizing that this adaptation stems from the normalization of lifestyle routines. In contrast, delays in both bedtime and wake-up times were noted during both lockdowns compared to the pre-lockdown scenario in the Belgian population, especially among individuals under 24 years old [182]. Another longitudinal study conducted in Italy highlighted a reduction in sleep duration during both the first and second waves of the pandemic [183].
Below, we analyze the potential primary effects on overall health and seek to explore the possible causes of alterations in quantitative sleep parameters.

4.1. The Consequences of Quantitative Sleep Parameters Alterations on General Health

Sleep represents an essential biological process for life and optimal health and it is well known that healthy sleep patterns are characterized by adequate duration, good quality, appropriate timing, and regularity, and the absence of sleep disturbances and problems [184]. The changes in quantitative sleep parameters that emerged from this work are of pivotal relevance, given their potentially harmful impact on the immune system, and other health outcomes. Sleep plays a critical role in maintaining the balance of the immune system, and any alterations in its duration and quality can impact its susceptibility to infections and the effectiveness of vaccinations [185,186]. In the context of SARS-CoV-2 vaccination, studies have indicated a positive association between regular sleep duration and antibody levels [185,187]. Specifically, ensuring sufficient sleep, especially within the first week after booster vaccination, is essential for optimal antibody production [188]. However, it is important to consider that while a higher frequency of daytime napping per week initially showed a negative correlation with antibody levels in one study, this association vanished after adjusting for confounding factors, underscoring the intricate nature of the relationship between sleep and post-vaccination immune response [184,187].
Alterations in sleep timing may have important consequences on multiple health variables. Later sleep timing in adults has been associated with depression, adverse brain health outcomes, cognitive impairment, obesity, higher cardiometabolic risk, osteopenia and osteoporosis [189]. An earlier bedtime was associated with a higher risk of diabetes, stroke, obesity, hypertension, and cardiovascular diseases (CVDs) [190].
A sleep duration of 7–8 h has a beneficial impact on general health with both shorter and longer sleep durations than 7–8 h are associated with poor outcomes such as mortality, diabetes, CVD, coronary heart disease, obesity, and stroke [191,192]. A prolonged time spent in bed appears to be linked to a greater decline in physical function and a higher risk of motor disability in the older population [193].
The impact of napping on health is still a debated topic. Indeed, napping seems to have both positive and negative effects on health and its effects appear to depend not only on the duration of the nap itself but also on other factors such as sex and age. Overall, naps showed significant beneficial effects on several cognitive functions [194], but in older people, only short or moderate duration of naps compared with both non-napping and long napping are associated with cognitive benefits in older adults [195]. Depression seems to be associated with long afternoon naps (≥90 min) in middle-aged women, and with short naps (<30 min) in older men [196]. Daytime napping restricted to 30 min/d for adults (aged < 60 years) has no negative effect on cardiovascular health, while it seems not to be beneficial for older adults [197].

4.2. Exploring the Causes of Quantitative Sleep Parameters Alterations

Several variables may have contributed to the sleep changes uncovered by this work. One of the most striking involves the modification to the circadian rhythm. These biological rhythms, operating in 24 h cycles, are strongly influenced by external factors such as social patterns, daily routines, and exposure to sunlight [198]. These stimulating signals were markedly altered during the COVID-19 pandemic due to home confinement and profound lifestyle modification, such as remote work and distance learning, which both contributed to bedtime postponement [12,107,173,199]. The rise in stress and anxiety levels during the lockdown, fueled by psychological distress associated with social isolation, financial challenges, and unemployment, along with growing concerns for personal and loved ones’ well-being, may also have had an impact on sleep health [200]. Finally, although digital platforms such as social media helped to keep people connected during the pandemic and lockdown, the sharp increase in social media usage during the pandemic may also have had an important impact on sleep health [201]. Indeed, according to a systematic review by Drumheller and colleagues, later bedtime and wake-up times and a decrease in sleep duration were found as the screen time use increased [202].

4.3. Strengths and Limitations

Our study has several strengths. Firstly, to the best of our knowledge, this is the first systematic review and meta-analysis that has sought to provide a comprehensive synthesis of changes in quantitative sleep parameters in the general population during the first lockdown compared to pre-lockdown levels. Despite the abundance of research on the topic, existing systematic reviews or meta-analyses do not offer as comprehensive and detailed coverage of these phenomena as provided by our work. Moreover, our research stands out for its thorough analysis of changes associated with atypical sleep duration and napping habits, areas often overlooked or not fully examined in previous studies. A meticulous methodological approach and an extensive literature review were utilized to explore sleep changes. Additionally, using a random-effect model with a more conservative estimation allowed us to at least partially address the heterogeneity between studies, given the differences in socio-cultural realities, methodological aspects (study design and outcome measures), and the severity of the lockdown measures. The study also has many limitations. The heterogeneity of the collected data limited the subgroup analysis exclusively to the risk of bias and to the countries’ areas. The comparison of the areas is also limited to sleep duration. The results of this study refer exclusively to the first lockdown. Finally, the fact that the lockdown measures differed widely across countries could have affected the study’s outcomes.

4.4. Implications of the Results

While the implementation of lockdown measures allowed governments to control the transmission of the SARS-CoV-2 infection, it also had adverse effects on the sleep health of the general population, in particular, on circadian rhythms and sleep–wake cycles. Given the health implications of these changes, and to prepare for possible future pandemics, these findings underline the importance of promoting healthy sleep hygiene, implementing screening programs, and treating sleep disturbances appropriately. However, extending beyond the initial waves of infections, sleep-related issues have persisted in capturing the attention of the scientific community, particularly concerning patients classified as experiencing long COVID. As defined by the WHO, long COVID encompasses a condition emerging in individuals with a history of probable or confirmed SARS-CoV-2 infection. It typically manifests around three months after symptom onset, enduring for at least two months and evading alternative diagnoses. Symptoms may emerge following the initial recovery from acute SARS-CoV-2 infection or persist from the original illness, exhibiting variations and even recurrences over time, often significantly associated with daily functioning. Notably, sleep disturbances are among these symptoms. Consequently, the implications of our findings acquire heightened significance and broad relevance when considered within the broader scope of investigating the intricate complexities of long COVID and its relationship with individuals’ health and well-being. Finally, as there have been other pandemic waves and related lockdowns since that time, it would be essential to uncover whether sleep parameters have returned to pre-pandemic levels or they have become chronic. High-quality, preferably longitudinal, studies are needed to answer these questions.

5. Conclusions

This systematic review and meta-analysis uncovered noteworthy changes in quantitative sleep parameters in the general population during the first COVID-19 lockdown. In particular, an increase in sleep duration and a significant delay in sleep timing were uncovered. COVID-19 pandemic affected several basic life aspects of the general population, influencing overall health with implications for both the immune system efficacy and successful vaccination. High-quality research based on longitudinal studies is needed to evaluate the prolonged effects of lockdown on these sleep parameters. At the same time, health authorities and professionals are called upon to address the problem of poor sleep hygiene and to implement intervention strategies as an integral part of overall health management.

Author Contributions

Conceptualization, F.L., C.T., F.P. and S.M.; methodology, F.L., P.S., C.T., F.P. and S.M.; formal analysis, P.S.; data screening and data extraction F.L., P.S., E.P. and F.R.; investigation, C.C., F.L., P.S. and C.T.; data curation, F.L. and P.S.; writing—original draft preparation, C.C., F.L. and P.S.; writing—review and editing, C.C., F.L., P.S., C.T., M.N., F.C., S.C., E.D.R., E.P., F.R., F.P. and S.M.; visualization, P:S.; supervision, F.L. and M.N. All authors have read and agreed to the published version of the manuscript.

Funding

S.C. and E.P.’s research fellowship was funded by the Ministero della Salute (Bando di Ricerca Finalizzata Giovani Ricercatori 2016, Grant number GR-2016-02361730).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created in this study. Data sharing is not applicable to this article.

Acknowledgments

We would like to thank Linda Inverso Moretti for reviewing the English version of this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Search strategy: changes in sleep characteristics.
Table A1. Search strategy: changes in sleep characteristics.
Databases and RegistersSearch Terms
PubMed(sleep[MeSH Terms] OR bedtime * OR “wake time” OR waketime * OR Circadian * OR sleep OR sleeping OR insomnia OR snore OR parasomnia* OR “Life Style”[Mesh] OR lifestyle * OR “life style *”) AND (“COVID 19” OR COVID-19 OR Coronavirus OR 2019-nCoV OR “2019 nCoV” OR “SARS CoV 2” OR SARS-CoV-2 OR “COVID-19”[Mesh] OR “COVID-19 pandemic *” OR “COVID 19 pandemic *”) AND (isolation OR lock-down OR lockdown OR self-isolation OR Confinement OR Containment OR Quarantine OR “Quarantine”[Mesh])
Web of Science(bedtime * OR “wake time” OR waketime * OR Circadian * OR sleep OR sleeping OR insomnia OR snore OR parasomnia * OR lifestyle * OR “life style*”) AND (“COVID 19” OR COVID-19 OR Coronavirus OR 2019-nCoV OR “2019 nCoV” OR “SARS CoV-2” OR SARS-CoV-2 OR “COVID-19 pandemic *” OR “COVID 19 pandemic *”) AND (isolation OR lock-down OR lockdown OR self-isolation OR Confinement OR Containment OR Quarantine)
Cochrane Library(bedtime * OR “wake time” OR waketime * OR Circadian * OR sleep OR sleeping OR insomnia OR snore OR parasomnia* OR lifestyle * OR “life style *”) AND (“COVID 19” OR “COVID-19” OR Coronavirus OR “2019 nCoV” OR “SARS CoV-2” OR “SARS-CoV-2” OR “COVID-19 pandemic *” OR “COVID 19 pandemic *”) AND (isolation OR lock-down OR lockdown OR self-isolation OR Confinement OR Containment OR Quarantine)
EBSCOhost(bedtime * OR “wake time” OR waketime * OR Circadian * OR sleep OR sleeping OR insomnia OR snore OR parasomnia * OR lifestyle * OR “life style *”) AND (“COVID 19” OR COVID-19 OR Coronavirus OR 2019-nCoV OR “2019 nCoV” OR “SARS CoV 2” OR SARS-CoV-2 OR “COVID-19 pandemic *” OR “COVID 19 pandemic *”) AND (isolation OR lock-down OR lockdown OR self-isolation OR Confinement OR Containment OR Quarantine)
MedRxiv *bedtime bedtimes “wake time” waketime waketimes Circadian Circadians sleep sleeping insomnia snore parasomnia lifestyle lifestyles “life style” “life styles”
AND COVID-19 AND lockdown
OpenGreybedtime bedtimes “wake time” waketime waketimes Circadian Circadians sleep sleeping insomnia snore parasomnia lifestyle lifestyles “life style” “life styles”
AND coronavirus AND lockdown
* In Abstract or Title.
Table A2. Risk of bias assessment.
Table A2. Risk of bias assessment.
Author, YearStudy DesignSelectionComparabilityOutcomeRisk of Bias Score
Abouzid M., 2021 [25]cross-sectionalc)b)c)b) c)a)3
Ahmed S., 2021 [26]cross-sectionalc)a)c)a) c)a)4
Aishworiya R., 2021 [27]cross-sectionalc)b)c)a)a)b)c)a)6
Akbari H.A., 2021 [28]cross-sectionalc)a)c)a)a)c)a)5
Aldhwayan M., 2022 [29]cross-sectionalc)b)c)b)a)c)a)4
Alhusseini N., 2022 [30]cross-sectionalc)b)c)b) c)a)3
Ali A., 2021 [31]cross-sectionalc)a)c)a)a)b)c)a)6
Al-Musharaf S., 2021 [32]longitudinalc)a)a)a)a)c)a)6
Alomari M.A., 2021 [33]cross-sectionalc)a)c)b) c)a)4
Alrubaysh M.A., 2021 [34]cross-sectionalc)b)c)b) c)a)3
Amerio A., 2021 [35]cross-sectionala)b)c)a)a)b)c)a)6
AMHSI Research Team, 2021 [36]longitudinalc)a)a)a) c)a)5
Anastasiou E., 2021 [37]cross-sectionalc)a)b)a)a)b)c)a)7
Antunes R., 2020 [38]cross-sectionalc)b)c)a)a)b)c)a)6
Arrona-Palacios A., 2022 [39]cross-sectionalc)b)c)b)a)c)a)4
Asensio-Cuesta S., 2021 [40]cross-sectionalc)b)b)a)a)c)a)5
Aymerich-Franch L., 2020 [41]cross-sectionalc)b)c)b) c)b)2
Azizi A., 2020 [42]cross-sectionalc)b)b)a) c)a)4
Azuma K., 2021 [43]cross-sectionalc)b)b)a)a)b)c)a)6
Bann D., 2021 [44]cross-sectionalb)b)c)b)a)b)c)a)6
Barbouzas A.E., 2022 [45]cross-sectionalc)b)c)b)a)c)a)4
Bertrand L., 2022 [46]cross-sectionalc)b)c)a) c)a)4
Bigalke J.A., 2020 [47]cross-sectionalc)b) c)b)a)b)c)a)5
Blume C., 2021 [48]cross-sectional c)b)c)a)a)b)c)a)6
Borisenkov M.F., 2022 [49]cross-sectionalc)b)b)a)a)c)a)5
Bottary R., 2022 [50]cross-sectionalc)b)c)a)a)c)a)5
Bourdas D.I., 2021 [51]cross-sectionalc)b)c)b)a)b)c)a)5
Buoite Stella A., 2021 [52]cross-sectionalc) b) c)b)a)b) b)a)6
Bushnaq T., 2022 [53]cross-sectionalc)a)c)b) c)a)4
Cancello R., 2020 [54]cross-sectionalc)b) c)c) c)a)2
Casas R., 2022 [55]cross-sectionalc)b)c)b) c)a)3
Cellini N., 2020 [56]cross-sectionalc)b)c)a)a)b)c)b)5
Cellini N., 2021 [57]cross-sectional c) b)c)b)a)b) c)a)5
Celorio-Sardà R., 2021 [58]cross-sectionalc)b)c)a) c)b)3
Cheikh Ismail L., 2020 [60]cross-sectionalc)b)c)b)a)b)c)a)5
Cheikh Ismail L., 2021a [61]cross-sectionalc)b)c)b) c)a)3
Cheikh Ismail L., 2021b [59]cross-sectionalc)b)c)b) c)a)3
Chopra S., 2020 [62]cross-sectionalc)b)c)b) c)a)3
Chouchou F., 2021 [63]cross-sectionalc)b)c)b)a)c)a)4
Conte F., 2021 [64]cross-sectional c)b)c)b)a)b)c)a)5
Cooper J.A., 2021 [65]cross-sectionalc)b)c)a)b)c)a)5
Csépe P., 2021 [66]cross-sectional c)b)b)b)a)c)a)4
Curtis R.G., 2021 [67]longitudinalc)a)a)b) a)a)6
Czeisler M.É., 2021 [69]cross-sectionalc)b)c)a) c)a)4
Czeisler M.E., 2022 [68]longitudinalc)a)a)b)a)a)b)6
Davy J.P., 2021 [70]cross-sectionalc)b)c)a)a)b)c)a)6
Delgado-Ortiz L., 2022 [71]cross-sectionalb)a)a)a)a)c)a)8
Di Renzo L., 2020 [72]cross-sectionalc)b)b)a) c)a)4
Ding X., 2022 [73]cross-sectionalb)b)b)a)a)c)a)6
Diz-Ferreira E., 2021 [74]cross-sectionalc)a)c)a) c)a)4
Dragun R., 2021 [75]cross-sectionalc)b)c)a)b)c)a)5
Elhadi M., 2021 [76]cross-sectionalc)a)c)c) c)a)3
ElHafeez S.A., 2022 [77]cross-sectionalc)a)c)a)a)c)a)5
Enriquez-Martinez O.G., 2021 [78]cross-sectionalc)b)c)a)a)c)a)5
Esht V., 2021 [79]cross-sectionalc)a)c)a) c)a)4
Falkingham J., 2020 [80]longitudinalc)a)a)a)a)b)c)a)7
Felician J., 2022 [81]cross-sectionalc)b)a)a)a)b)c)a)7
Flanagan E.W., 2021 [82]cross-sectionalc)b)c)b)a)b)c)a)5
Flores L.E., 2022 [83]cross-sectionalc)b)c)a)a)c)a)5
Franceschini C., 2020 [84]cross-sectionalc)b)c)a)a)c)a)5
Gao C., 2020 [85]longitudinal c)a)c)a) c)a)5
García-Esquinas E., 2021 [86]longitudinal b)a)a)a) c)a)6
García-Garro P.A., 2022 [87]cross-sectionalc)a)b)a) c)a)5
Gibson R., 2022 [88]cross-sectionalc)b)b)a)a)c)a)5
González-Calderón M.J., 2022 [89]cross-sectionalc)b)c)b)a)c)a)4
Gornicka M., 2020 [90]cross-sectionalc)b)c)a)b)c)a)5
Gupta R., 2020 [91]cross-sectionalc)b)c)b)a)b)c)a)5
Hernández-Nava R.G., 2022 [92]cross-sectionalc)a)c)c)a)b)c)a)5
Hisler G., 2021 [93]longitudinalb)a)b)b)a)b)c)a)7
Huancahuire-Vega S., 2021 [94]cross-sectionalc)b)c)a)a)c)a)5
Husain W., 2020 [95]cross-sectionalc)a)c)a) c)a)4
Islam M.A., 2022 [96]cross-sectionalc)b)c)b)a)c)a)4
Janssen X., 2020 [97]longitudinalc)a)a)a) c)a)5
Jones C., 2021 [98]cross-sectionalc)b)c)b) c)a)3
Joshi D.R., 2023 [99]cross-sectionalc)b)c)a)a)b)c)a)6
Kaizi-Lutu M., 2021 [100]cross-sectionalc)b)c)b) c)a)3
Karahan Yılmaz S., 2020 [101]cross-sectionalc)b)c)c) c)a)2
Khojasteh M.R., 2022 [102]cross-sectionalc)a)a)b) c)a)5
Kholghi M., 2021 [103]longitudinalc)a)a)a)a)a)a)7
Kim A.C.H., 2022 [104]cross-sectionalc)b)c)b)a)c)a)4
Kolokotroni O., 2021 [105]cross-sectionalc)b)c)a) c)a)4
Kontsevaya A.V., 2021 [106]cross-sectionalc)b)c)a)a)c)a)5
Leone M.J., 2020 [107]longitudinalc)a)a)a)a)b)c)a)7
Li J.W., 2021 [108]longitudinalc)a)a)b) a)a)6
Liboredo J.C., 2021 [109]cross-sectionalc)b)c)a) c)a)4
Lopez-Bueno R., 2020 [110]cross-sectional c)b)c)a)a)b)c)a)6
López-Moreno M., 2020 [111]cross-sectionalc)b)c)b) c)a)3
Luciano F., 2020 [112]cross-sectionalc)b)b)a) c)a)4
Majumdar P., 2020 [113]cross-sectionalc)b)c)b)a)c)b)3
Mandelkorn U., 2021 [114]cross-sectionalc)b)c)c) c)a)2
Marelli S., 2021 [115]cross-sectionalc)b)c)a)a)b)c)a)6
Martínez-Vázquez S.E., 2021 [116]cross-sectionalc)a)c)b) c)a)4
Mititelu M., 2021 [117]cross-sectionalc)b)c)b)a)c)a)4
Mohsin A., 2021 [118]cross-sectionala)b)c)b) c)b)3
Mónaco E., 2022 [119]longitudinalc)a)a)a) c)b)3
Morin C.M., 2022 [120]longitudinalc)a)a)b) c)a)5
Nishijima C., 2021 [121]cross-sectionalb)b)c)a)a)c)a)5
Ong J.L., 2021 [122]longitudinala)a)a)b)a)b)a)a)9
Pachocka L., 2022 [123]cross-sectionalc)b)c)b) c)a)3
Panarese P., 2021 [124]cross-sectionalc)b)c)b)a)c)b)3
Pecotić R., 2022 [125]cross-sectionalc)b)c)a)a)b)c)a)6
Pépin J.-L., 2021 [126]longitudinalc)a)a)b)a)b)a)a)8
Perez-Carbonell L., 2020 [127]cross-sectionalc)b)c)c) c)a)2
Peterson M., 2021 [128]longitudinald)a)a)b) a)b)5
Petrov M.E., 2021 [129]cross-sectionalc)b)c)b)a)b)c)a)5
Pisot S., 2020 [130]cross-sectionalc)b)c)a)b)c)a)5
Pitol M.N.S., 2023 [131]cross-sectionalc)a)b)b) c)a)4
Pouget M., 2022 [132]cross-sectionalc)b)c)a) c)a)4
Priego-Parra, 2020 [133]cross-sectionalc)b)c)a)a)b)c)a)6
Rababah T., 2023 [134]cross-sectionalc)b)c)b) c)a)3
Radwan H., 2021 [135]cross-sectionalc)b)b)a)a)b)c)a)6
Ramírez C., 2022 [136]cross-sectionalc)b)c)a)a)c)a)5
Ramos Socarras, 2021 [137]cross-sectionalc)b)c)a)a)b)c)a)6
Reynaud E., 2022 [138]cross-sectionalc)b)c)b) c)a)3
Robinson E., 2020 [139]cross-sectionalc)b)b)a) c)b)3
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Figure 1. PRISMA flow diagram of study selection.
Figure 1. PRISMA flow diagram of study selection.
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Figure 2. Forest plot showing pooled changes in sleep duration (hours) between before and during the lockdown. Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates. Aishworiya R., 2021a: female; Aishworiya R., 2021b: male; Asensio-Cuesta S., 2021a: male; Asensio-Cuesta S., 2021b: female; Bann D., 2021a: 1946; Bann D., 2021b: 1958; Bann D., 2021c: 1970; Bann D., 2021d: 1990; Bann D., 2021e: 2001; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male; Cellini N., 2021i: Italian regular workers, female; Cellini N., 2021j: Italian regular workers, male; Cellini N., 2021k: Italian remote workers, female; Cellini N., 2021l: Italian remote workers, male; Cellini N., 2021m: Italian Stop working, female; Cellini N., 2021n: Italian Stop working, male; Cellini N., 2021o: Italian students, female; Cellini N., 2021p: Italian students, male; Cellini N., 2021q: Italian unemployed/retired, female; Cellini N., 2021r: Italian unemployed/retired, male; Mandelkorn U., 2021a: Study 1; Mandelkorn U., 2021b: Study 2; Ramos Socarras L., 2021a: 18–21-years old; Ramos Socarras L., 2021b: 22–25-years old.
Figure 2. Forest plot showing pooled changes in sleep duration (hours) between before and during the lockdown. Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates. Aishworiya R., 2021a: female; Aishworiya R., 2021b: male; Asensio-Cuesta S., 2021a: male; Asensio-Cuesta S., 2021b: female; Bann D., 2021a: 1946; Bann D., 2021b: 1958; Bann D., 2021c: 1970; Bann D., 2021d: 1990; Bann D., 2021e: 2001; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male; Cellini N., 2021i: Italian regular workers, female; Cellini N., 2021j: Italian regular workers, male; Cellini N., 2021k: Italian remote workers, female; Cellini N., 2021l: Italian remote workers, male; Cellini N., 2021m: Italian Stop working, female; Cellini N., 2021n: Italian Stop working, male; Cellini N., 2021o: Italian students, female; Cellini N., 2021p: Italian students, male; Cellini N., 2021q: Italian unemployed/retired, female; Cellini N., 2021r: Italian unemployed/retired, male; Mandelkorn U., 2021a: Study 1; Mandelkorn U., 2021b: Study 2; Ramos Socarras L., 2021a: 18–21-years old; Ramos Socarras L., 2021b: 22–25-years old.
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Figure 3. Forest plot showing a pooled percentage of changes in sleep duration from before to during the lockdown: changes (a), decrease (b), and increase (c). Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates.
Figure 3. Forest plot showing a pooled percentage of changes in sleep duration from before to during the lockdown: changes (a), decrease (b), and increase (c). Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates.
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Figure 4. Forest plot showing pooled changes in atypical sleep duration from before to during the lockdown: atypical (a), decrease (b), increase (c). Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates.
Figure 4. Forest plot showing pooled changes in atypical sleep duration from before to during the lockdown: atypical (a), decrease (b), increase (c). Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates.
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Figure 5. Forest plot showing pooled changes in TIB from before to during the lockdown: increase, decrease. Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates. Cellini N., 2020a: student; Cellini N., 2020b: worker; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male.
Figure 5. Forest plot showing pooled changes in TIB from before to during the lockdown: increase, decrease. Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates. Cellini N., 2020a: student; Cellini N., 2020b: worker; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male.
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Figure 6. Forest plot showing pooled changes in bedtime from before to during the lockdown. Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates. Cellini N., 2020a: student; Cellini N., 2020b: worker; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male; Ramos Socarras L., 2021a: 18–21-years old; Ramos Socarras L., 2021b: 22–25-years old.
Figure 6. Forest plot showing pooled changes in bedtime from before to during the lockdown. Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates. Cellini N., 2020a: student; Cellini N., 2020b: worker; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male; Ramos Socarras L., 2021a: 18–21-years old; Ramos Socarras L., 2021b: 22–25-years old.
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Figure 7. Forest plot showing pooled changes in bedtime from before to during the lockdown: changes (a), delayed (b), earlier (c). Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates.
Figure 7. Forest plot showing pooled changes in bedtime from before to during the lockdown: changes (a), delayed (b), earlier (c). Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates.
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Figure 8. Forest plot showing pooled changes in wake-up time from before to during the lockdown. Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates. Cellini N., 2020a: student; Cellini N., 2020b: worker; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male; Ramos Socarras L., 2021a: 18–21-years old; Ramos Socarras L., 2021b: 22–25-years old.
Figure 8. Forest plot showing pooled changes in wake-up time from before to during the lockdown. Caption: error bars = 95% confidence interval; square boxes = individual study point estimates; diamond box = pooled point estimates. Cellini N., 2020a: student; Cellini N., 2020b: worker; Cellini N., 2021a: Belgian regular workers, female; Cellini N., 2021b: Belgian regular workers, male; Cellini N., 2021c: Belgian remote workers, female; Cellini N., 2021d: Belgian remote workers, male; Cellini N., 2021e: Belgian students, female; Cellini N., 2021f: Belgian students, male; Cellini N., 2021g: Belgian unemployed/retired, female; Cellini N., 2021h: Belgian unemployed/retired, male; Ramos Socarras L., 2021a: 18–21-years old; Ramos Socarras L., 2021b: 22–25-years old.
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Figure 9. Forest plot showing pooled changes in wake-up time from before to during the lockdown: changes (a), delayed (b), earlier (c). Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates.
Figure 9. Forest plot showing pooled changes in wake-up time from before to during the lockdown: changes (a), delayed (b), earlier (c). Caption: error bars = 95% confidence interval; square boxes = individual point estimates; diamond box = pooled point estimates.
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Table 1. Descriptive characteristics of the studies included.
Table 1. Descriptive characteristics of the studies included.
Author, YearCountryStudy DesignAssessment PeriodPopulationN (F%)AgeData Collection/ Type of RecruitmentMeasurementRisk of Bias ScoreDTIBBTWTNAP
Abouzid M., 2021 [25]multi-country: Middle East/North Africa countriescross-sectionalAugust–4 September 2020general population5896; F 62.8%≥18 yonline survey/snowball samplingself-report measure3D
Ahmed S., 2021 [26]Bangladeshcross-sectional24 April–25 May 2020general population230; F 20.9%8–60 yonline survey/random samplingself-report measure4D
Aishworiya R., 2021 [27]Singaporecross-sectional7 April–1 June 2020general population593; F 86.0%≥21 yonline survey/convenience samplingself-report measure6D BTWT
Akbari H.A., 2021 [28]Irancross-sectional17 November 2020–13 February 2021general population3323; F 54.3%30 ± 11 yonline survey/snowball samplingself-report measure5D BT
Aldhwayan M., 2022 [29]Saudi Arabiacross-sectional2–23 April 2020general population1860; F 75.1%>18; median 36 y (IQR 18)online survey/convenience samplingself-report measure4D
Alhusseini N., 2022 [30]Saudi Arabiacross-sectional22 May–2 June 2021general population1051; F 71%≥18 yonline survey/convenience samplingself-report measure3D
Ali A., 2021 [31]Pakistancross-sectional24 March–26 April 2020students251; F 70.2%19.4 ± 1.6 yonline survey/convenience samplingself-report measure6D
Al-Musharaf S., 2021 [32]Saudi ArabialongitudinalB: February–April 2019; D: April–May 2020university students297; F 100%19–30 y; 20.7 ± 1.4 ytelephone interview/nrself-report measure6D
Alomari M.A., 2021 [33]Jordancross-sectionalsecond-third quartiles of 2020general population1757; F 69.4%33.8 ±11.1y; ≥18 yonline survey/convenience samplingself-report measure4D NAP
Alrubaysh M.A., 2021 [34]Saudi Arabiacross-sectionalJanuary 2021–February 2021general population2069; F 68.1%≥18 yonline survey/convenience samplingself-report measure3D
Amerio A., 2021 [35]Italycross-sectional27 April–3 May 2020general population6003; F 50,7%18–74 yonline survey/quota sampling methodself-report measure6D
AMHSI Research Team, 2021 [36]multi-country: InternationallongitudinalD: 1 March–15 June 2020adults2645; F 52.5%19–60 yonline and telephone survey/snowball samplingself-report measure5 BTWTNAP
Anastasiou E., 2021 [37]Greececross-sectional31 March–23 April 2020general population4216; F 70,87%36.8 ± 12.0 yonline survey/convenience samplingself-report measure7 BTWT
Antunes R., 2020 [38]Portugalcross-sectional1–15 April 2020general population1404; F 69.6%18–89 y, 36.4 ± 11.7 yonline survey/convenience samplingself-report measure6D
Arrona-Palacios A., 2022 [39]Mexicocross-sectional18 May–10 June 2020faculty members of universities214; F 56.5%42.66 ± 9.17 y; 25–64 yonline survey/snowball samplingself-report measure4DTIBBTWT
Asensio-Cuesta S., 2021 [40]Spaincross-sectionalB: 17 October 2019–17 February 2020; D: 21 April–1 May 2020university community (students, teachers, and staff)B: 341, F 43.1%; D: 398, F 58.3%≥18 ytelegram chatbot/convenience samplingself-report measure5D
Aymerich-Franch L., 2020 [41]Spaincross-sectional15–25 April 2020general population584; F 75.3%18–65 yonline survey/snowball samplingself-report measure2D
Azizi A., 2020 [42]Moroccocross-sectionalB: nr; D: 9–30 May 2020general populationB: 484, F nr; D: 537, F 62.9%D: 33.19 ± 12.14online survey/convenience samplingself-report measure4 BTWTNAP
Azuma K., 2021 [43]Japancross-sectionalB: January 7–28 April 2019; D: 6 January 6–26 April 2020general populationB: 464, F 74.6%; D: 622, F 85.7%≥20 y; B 35 ± 12 y; D: 32 ± 11 yonline survey/convenience samplingself-report measure6 BTWT
Bann D., 2021 [44]UKcross-sectional4–30 May 2020general population by birth cohort13283; F 50.3%≥19 yonline survey/cohortself-report measure6D
Barbouzas A.E., 2022 [45]Greececross-sectional2 September–27 November 2020young adults540; F 62.8%21.2 ± 2.3 y; 18–25 yonline survey/cohortself-report measure4D
Bertrand L., 2022 [46]Francecross-sectional6–11 May 2020general population1627; F 74.3%<18 y 1%–>65 y 7.5%online survey/convenience samplingself-report measure4D BTWT
Bigalke J.A., 2020 [47]USAcross-sectional25 April–18 May 2020general population103; F 59%mean 38 yonline survey/convenience samplingself-report measure5D
Blume C., 2021 [48]multi-country: Austria/Germany/Switzerlandcross-sectional23 March–26 April 2020general population435; F 75.2%≥18 yonline survey/convenience samplingself-report measure6D
Borisenkov M.F., 2022 [49]Russiacross-sectional17 April–14 June 2020university studentsB: 1050, F 71.8; D: 844; F 79.4%B: 18.9 ± 1.9; D: 19.4 ± 1.8 yonline survey/convenience samplingself-report measure5D BTWT
Bottary R., 2022 [50]USAcross-sectionalApril–May 2020general population610; F 82.9%≥18 y; 39.24 ± 17.45 y; 18–89 yonline survey/snowball and convenience samplingself-report measure5D BTWT
Bourdas D.I., 2021 [51]Greececross-sectional4–19 April 2020general population8495; F 61.68%≥18 yonline survey/snowball samplingself-report measure5D
Buoite Stella A., 2021 [52]Italycross-sectional23–29 March 2020general population400; F 69%35 ± 15 ydevice data or online survey/convenience samplingobjective or self-report measures6D
Bushnaq T., 2022 [53]Saudi Arabiacross-sectional10August–9 October 2021general population786; F 88.3%≥18 y; 30.48 ± 11.50 yonline survey/convenience samplingself-report measure4D
Cancello R., 2020 [54]Italycross-sectional15 April–4 May 2020general population490; F 84%≥18 yonline survey/convenience samplingself-report measure2D
Casas R., 2022 [55]Spaincross-sectional23 April–2 June 2020general population945; F 70.8%>18 y; 43.4 ± 13.4 yonline survey/snowball samplingself-report measure3D
Cellini N., 2020 [56]Italycross-sectional24–28 March 2020students/workers1310; F 67.2%23.91 ± 3.6 yonline survey/convenience samplingself-report measure5 TIBBTWT
Cellini N., 2021 [57]multi-country: Italy/Belgiumcross-sectional1 April–19 May 2020general population1622 Italians, F 72.2%; 650 Belgian, F 78.3%34.1 ± 13.6 y; 43.0 ± 16.8 yonline survey/convenience samplingself-report measure5DTIBBTWT
Celorio-Sardà R., 2021 [58]Spaincross-sectional22 May–3 July 2020students/workers321; F 79.8%≥18 yonline survey/convenience samplingself-report measure3 BTWT
Cheikh Ismail L., 2021b [59]Lebanoncross-sectional3–28 June 2020general population2507; F 73%>18 yonline survey/snowball samplingself-report measure3D
Cheikh Ismail L., 2020 [60]United Arab Emiratescross-sectionalApril–May 2020general population1012; F 75.9%≥18 yonline survey/convenience samplingself-report measure5D
Cheikh Ismail L., 2021a [61]multi-country MENA region: Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen.Cross-sectional15–29 April 2020general population2970; F 71·6%≥18 yonline survey/convenience and snowball samplingself-report measure3D
Chopra S., 2020 [62]Indiacross-sectional15–30 August 2020general population995; F 41.4%33.33 ± 14.5 y; 18–85 yonline and telephone survey/quota samplingself-report measure3D
Chouchou F., 2021 [63]Francecross-sectional35–45th days of lookdowngeneral population400; F 58.3%≥18 y; 29.8 ± 11.5 yonline survey/convenience samplingself-report measure4D BTWTNAP
Conte F., 2021 [64]Italycross-sectional1–20 April 2020general population1622; F 72.2%>18 y; 18–79 y; 34.1 ± 13.6 yonline survey/convenience samplingself-report measure5 TIBBTWTNAP
Cooper J.A., 2021 [65]USAcross-sectional24 April–4 May 2020adults1607; F 56.6%38.0 ± 12.9 y, 18–75 yonline survey/convenience samplingself-report measure5D
Csépe P., 2021 [66]Hungarycross-sectional29 June–5 July 2020university students447; F 75,16%25.6 ± 7.2 yonline survey/convenience samplingself-report measure4D
Curtis R.G., 2021 [67]AustralialongitudinalB: 10–23 February 2020; D: 14–27 April 2020parents of children61; F 66%41 ± 6 ydevice data/sample of parentsobjective measure (wrist-worn wearable device, Fitbit Charge 3)6 BTWT
Czeisler M.E., 2022 [68]USAlongitudinalB: 1 January–12 March 2020; D: 13 March–12 April 2020general population4912; F 29.3%≥18 ydevice data/non-probability samplingobjective measure (sleep wearable device, WHOOP)6D BTWT
Czeisler M.É., 2021 [69]Australiacross-sectional15–24 September 2020general population1157; F 53%≥18 yonline survey/demographic quota sampling and survey weighting to Censusself-report measure4 TIB
Davy J.P., 2021 [70]South Africacross-sectional12 May–15 June 2020young adults1048; F 73.2%median 27 (21, 42) yonline survey/convenience and snowball samplingself-report measure6DTIBBTWT
Delgado-Ortiz L., 2022 [71]Cataloniacross-sectionalMarch–August 2020general population10032; F 59%55.3 ± 8 yonline or telephone survey/cohortself-report measure8D
Di Renzo L., 2020 [72]Italycross-sectional5–24 April 2020general population Internet users3533; F 76.1%40.03  ±  13.53 yonline survey/convenience samplingself-report measure4D
Ding X., 2022 [73]UKcross-sectional2–31 May 2020general population8547; F 57%≥17 yonline survey/cohortself-report measure6D
Diz-Ferreira E., 2021 [74]Spaincross-sectional30 March–12 April 2020general population451; F 73.4%≥18 ypaper and online survey/convenience samplingself-report measure4DTIB WT
Dragun R., 2021 [75]Croatiacross-sectionalB: 2018–2019; D: May 2020studentsB: 1326, F 63.8%; D: 531, F 62.3%B/D: 18.0 (IQR 6.0)online survey/convenience samplingself-report measure5D
Elhadi M., 2021 [76]Libyacross-sectional18 July–23 August 2020general population10,296; F 76.6%28.9 ± 8.5 yonline survey/convenience samplingself-report measure3 BTWT
ElHafeez S.A., 2022 [77]Egyptcross-sectional25 April–1 June 2020general population1000; F 66.2%≥18 yonline survey/convenience samplingself-report measure5D
Enriquez-Martinez O.G., 2021 [78]multi-country: Argentina, Brazil, Mexico, Peru, Spaincross-sectional1 April–26 September 2020general population6325; F 68.1%≥18 yonline survey/convenience samplingself-report measure5D
Esht V., 2021 [79]Indiacross-sectionalApril–May 2020general population440; F 52.7%20–40 yonline survey/snowball samplingself-report measure4D
Falkingham J., 2020 [80]UKlongitudinalB: 2019; D: April 2020general population8163; F 53%50.6 ± 17.5 yonline survey/nrself-report measure7D
Felician J., 2022 [81]Francecross-sectional9 April–9 June 2020general population2513; F 77%median 39 (IQR 30–48) yonline survey/convenience samplingself-report measure7D NAP
Flanagan E.W., 2021 [82]multi-country: USA/Australia/Canada/Ireland/UKcross-sectional3 April–3 May 2020general population7753; F 80.0%≥18 y; 51.2 ± 0.17 yonline survey/convenience samplingself-report measure5 BTWT
Flores L.E., 2022 [83]Argentinacross-sectional2–22 December 2020general population1536; F 75.1%≥18 y; 38.8  ±  13.1 y;online survey/convenience samplingself-report measure5D
Franceschini C., 2020 [84]Italycross-sectionalMarch 10–4 May 2020adults6439; F 73.1%33.9 ± 27.6 y; 18–82online survey/convenience samplingself-report measure5 BTWTNAP
Gao C., 2020 [85]USAlongitudinalB: 17 February 2020; D: 25–27 March 2020general population699 (B:199; D: 500; B/D: 86); F 44.78%38.04 ± 11.65 yonline survey/convenience samplingself-report measure5D BTWT
García-Esquinas E., 2021 [86]SpainlongitudinalB: 2019; D: 27 April–22 June 2020general population3041; F 57.7%≥65 ytelephone interview/cohortself-report measure6D NAP
García-Garro P.A., 2022 [87]Colombiacross-sectional26 May–23 June 2020university community (professors, administration staff)354; F 40.96%43.39 ± 10.21 y; 40–64 yonline survey/convenience sampling (calculation of the sample size)self-report measure5D
Gibson R., 2022 [88]New Zealandcross-sectional11 April 2020general population723; F 82.3%median 45 (IQR 22) y; 20–85 yonline survey/convenience samplingself-report measure5DTIBBTWT
González-Calderón M.J., 2022 [89]Spaincross-sectional9–31 May 2020university community (students, professors, administration staff)2834; F 69.3%41.36 ± 10.5 y; 19–76 yonline survey/convenience samplingself-report measure4D
Gornicka M., 2020 [90]Polandcross-sectional30 April–23 May 2020general population2381; F 89.8%≥18 yonline survey/convenience samplingself-report measure5D
Gupta R., 2020 [91]Indiacross-sectional28 April–10 May 2020general population958; F 41.2%37.32 ± 13.09 yonline survey/snowball samplingself-report measure5D BTWTNAP
Hernández-Nava R.G., 2022 [92]Mexicocross-sectional2 June–4 July 2020general population1004; F 69.5%≥18 yonline survey/convenience samplingself-report measure5D
Hisler G., 2021 [93]USAlongitudinalB: 2018; D: 27 April 2020general populationB: 19433, F 51.8%; D: 2059, F 50.7%≥18 y; B: 42.84 ± 14.84 y; D: 43.35 ± 14.88 yonline survey/probability samplingself-report measure7D
Huancahuire-Vega S., 2021 [94]Perucross-sectional16 July–31 August 2020general population1176; F 51.5%≥18 yonline survey/convenience samplingself-report measure5D
Husain W., 2020 [95]Kuwaitcross-sectional30 March–15 April 2020adults415; F 68.7%≥18 y; 38.47 ± 12.73 y, 18–73 yonline survey/snowball samplingself-report measure4D
Islam M.A., 2022 [96]Bangladeshcross-sectional10–17 December 2020general population748; F 41.3%≥18 yonline survey/snowball samplingself-report measure4D
Janssen X., 2020 [97]UK: ScotlandlongitudinalD: 20 May–12 June 2020general population3230; F 79.2%≥18 y, 46.2 ± 15.3 yonline survey/convenience samplingself-report measure5D
Jones C., 2021 [98]USAcross-sectional14 May–24 October 2020general population228; F 79.0%≥18 y; 45.0 ± 17.1 yonline survey/convenience samplingself-report measure3D
Joshi D.R., 2023 [99]Nepalcross-sectional15 April–25 July 2020academicians (school teachers, faculty members, and graduate students of higher education institutions)361; F 18.3%34.17 ± 8.67 yonline survey/snowball samplingself-report measure6D
Kaizi-Lutu M., 2021 [100]USAcross-sectional16 May–11 November 2020general population226; F 77.8%≥18 y; 44.9 ± 17.4 yonline survey/convenience samplingself-report measure3 BTWTNAP
Karahan Yılmaz S., 2020 [101]Turkeycross-sectionalApril–May 2020adults1120; F 63.2%18–65 y; 33.04 ± 11.04 yonline survey/convenience samplingself-report measure2D
Khojasteh M.R., 2022 [102]Irancross-sectionalMarch–April 2020university students283; F 72.4%24.11 ± 2.54online survey/convenience samplingself-report measure5D
Kholghi M., 2021 [103]AustralialongitudinalB: November 2019–February 2020; D: March–May 2020older adults31; 54.8%84 ± 6.8 ydevice data/cohortobjective measure (mattress-based devices, EMFIT QS)7D BTWT
Kim A.C.H., 2022 [104]USAcross-sectionalfirst week of June 2020general population695; F 40%45.85 ± 15.42 yonline survey/convenience samplingself-report measure4D
Kolokotroni O., 2021 [105]Cypruscross-sectional10 April–12 May 2020general population745; F 73.8%≥18 y; median 39 (IQR 13) y; 18–76 yonline survey/convenience samplingself-report measure4D
Kontsevaya A.V., 2021 [106]Russiacross-sectional26 April–6 June 2020general population2432; F 83%≥18 y; 37.6 ± 13.4 yonline survey/convenience samplingself-report measure5D WT
Leone M.J., 2020 [107]ArgentinalongitudinalB: February and May 2018 and 2019/February 2020; D: April 2020general population1021; F 69.64%13–74 y; 37.4 ± 13.21 yonline survey/convenience samplingself-report measure7D BTWTNAP
Li J.W., 2021 [108]ChinalongitudinalB: 26 December 2019-22 January 2020; D: 3–21 January 21 February 2020general population19,960; F 10.1%35.7 ± 11.3 ydevice data/cohortobjective measure (wrist-worn wearable device—acceleration sensor and photoplethysmogram)6D
Liboredo J.C., 2021 [109]Brazilcross-sectional14 August–9 September 2020general population1368; F 80%median 31 y; 18–87 yonline survey/convenience samplingself-report measure4D
Lopez-Bueno R., 2020 [110]Spaincross-sectional22 March–5 April 2020adults2741; F 51.8%≥18 y; 34.2 ± 13.0 yonline survey/convenience samplingself-report measure6D
López-Moreno M., 2020 [111]Spaincross-sectional28 May–21 June 2020general population675; F 30.1%≥18 y; 39.1 ± 12.9 y; 18–85 yonline survey/snowball samplingself-report measure3D
Luciano F., 2020 [112]Italycross-sectionalB: October–November 2019; D: 9 March–3 May 2020university studentsB: 714, F 62%; D: 394, F 73%B/D: 25 ± 2 yonline survey/convenience samplingself-report measure4D
Majumdar P., 2020 [113]Indiacross-sectional14 April–2 May 2020university students/workers325 students, F 60.9%; 203 workers, F 18.2%33.1 ± 7.11 y; 22.1 ± 1.66 yonline survey/convenience samplingself-report measure3D NAP
Mandelkorn U., 2021 [114]multi-country: multi-national/USAcross-sectional26 March–26 April 2020general population2562 study 1, F 68%; 971 study 2, F 52.8%study 1 45.18 ± 14.46 y; study 2 40.36 ± 13.61 yonline survey/convenience samplingself-report measure2D
Marelli S., 2021 [115]Italycross-sectional24 March–2 May 2020university students/workers400; F 75.8%22.84 ± 2.68 yonline survey/convenience samplingself-report measure6DTIBBTWT
Martínez-Vázquez S.E., 2021 [116]Mexicocross-sectional13 April–16 May 2020general population8289; F 80%≥18 y; 18–38online survey/snowball samplingself-report measure4 BT
Mititelu M., 2021 [117]Romaniacross-sectional8–26 July 2020general population805, F 19.7%≥20 yonline survey/convenience samplingself-report measure4D
Mohsin A., 2021 [118]Pakistancross-sectional27 May–1 July 2020general population553; F 63.5%>18 yonline survey/convenience samplingself-report measure3 NAP
Mónaco E., 2022 [119]Spainlongitudinal30 March 2020general populationB: 363, F 69.4%; D: 261, F nr32.59 ± 12.57 y; age range: 18–65 yonline survey/snowball samplingself-report measure3D BTWTNAP
Morin C.M., 2022 [120]CanadalongitudinalB: 2018; D: April–May 2020general population594; F: 64.0%48.3 ± 13.1 y; 18–83 yonline or telephone survey/cohortself-report measure5D BTWTNAP
Nishijima C., 2021 [121]Japancross-sectional9–14 September 2020general population9645; F 52,4%≥20 yonline survey/random sampling by age, sex, and place of residenceself-report measure5D BTWT
Ong J.L., 2021 [122]SingaporelongitudinalB: 2–22 January 2020; D: 7–27 April 2020city-dwelling/young working adults1824; F 51.64%21–40 y; 30.94 ± 4.62 ydevice data/convenience samplingobjective measure (wrist-worn wearable device, Fitbit API)9DTIBBTWT
Pachocka L., 2022 [123]Polandcross-sectionalAugust 2020general population490; F 66.1%18–80 yface to face survey/convenience samplingself-report measure3D
Panarese P., 2021 [124]Italycross-sectional7 April–3 May 2020general population11,452; F nr≥25 yonline survey/snowball samplingself-report measure3D
Pecotić R., 2022 [125]Croatiacross-sectional25 April–5 May 2020general population1173; F 73.7%≥18 y; median 42 (32–52) yonline survey/snowball samplingself-report measure6 BTWT
Pépin J.-L., 2021 [126]FrancelongitudinalB: 16 February–March 2020; D: March 17–11 May 2020regular users of a sleep-monitoring headband599; F 29%median 47 (IQR 36–59) ydevice data or online survey/convenience samplingobjective (dream sleep-monitoring headband) and self-report measures8DTIBBTWT
Perez-Carbonell L., 2020 [127]UKcross-sectional12 May–2 June 2020general population843; F 67.4%≥18 y; median 52 (IQR 40–63) yonline survey/convenience samplingself-report measure2 BT
Peterson M., 2021 [128]USAlongitudinalB: before 15 March 2020; D: after 15 March 2020general population9; F 55.6%22–48 ydevice data/nrobjective measure (wrist-worn actigraph, Actiwatch-2 + non-contact monitoring device, SleepScore Max, SleepScore Labs)5D
Petrov M.E., 2021 [129]multi-country 79 countriescross-sectional21 May 2020–7 July 2020general population991; 72.5%≥18; 37.9 ± 14.6 y; 18–80 yonline survey/convenience samplingself-report measure5DTIB NAP
Pisot S., 2020 [130]multi-country: Bosnia and Herzegovina/Croatia/Greece/Kosovo/Italy/Serbia/Slovakia/Slovenia/Spaincross-sectional15 April–3 May 2020general population4108; F 63.6%15–82 y; 32.0 ± 13.2 yonline survey/snowball samplingself-report measure5D BTWT
Pitol M.N.S., 2023 [131]Malaysiacross-sectionalfirst lockdowngeneral population112; F 68.8%≥18 y; 19–60 yonline survey/convenience samplingself-report measure4D
Pouget M., 2022 [132]Francecross-sectional26 June 2020–2 March 2021general population671; F 74%47 ± 13 yonline survey/convenience samplingself-report measure4D
Priego-Parra, 2020 [133]Mexicocross-sectional23 March–21 April 2020general population561; F 71%30.7 ± 10.6 yonline survey/snowball samplingself-report measure6D
Rababah T., 2023 [134]Jordancross-sectionalMarch–June 2021general population672; F61.9%≥18 yonline survey/convenience samplingself-report measure3D
Radwan H., 2021 [135]United Arab Emiratescross-sectional5–18 May 2020adults residing2060; F 75.1%≥18 yonline survey/convenience samplingself-report measure6D
Ramírez C., 2022 [136]Mexicocross-sectional30 April –23 May 2020general population861; F 74.7%18–69 y; 27.73 ± 11.31 yonline survey/snowball samplingself-report measure5DTIBBTWTNAP
Ramos Socarras, 2021 [137]Canadacross-sectional3 June–3 July 2020.young adults248; F 75.4%18–25 yonline survey/convenience samplingself-report measure6D BTWT
Reynaud E., 2022 [138]Francecross-sectional11 April –20 May 2020general population1652; F 77.1%≥18 y; 35.4 ± 11.4 yonline survey/convenience samplingself-report measure3D BTWT
Robinson E., 2020 [139]UKcross-sectional19–22 April 2020adults723; F 67%18–60 y, 30.7 ± 9.6 yonline survey/convenience samplingself-report measure3D
Rotvold A., 2022 [140]USAcross-sectionalspring of 2020students195; F74.5%18–46 yonline survey/convenience samplingself-report measure3D
Ruiz-Zaldibar C., 2022 [141]Spaincross-sectional11–25 April 2020university students488; F 73.6%median 21 y; 18–54 yonline survey/convenience samplingself-report measure6D
Saalwirth C., 2021 [142]Germanycross-sectional1–19 April 2020general population665; F 53.8%18–73 y; 36 ± 14 yonline survey/convenience samplingself-report measure4D
Salehinejad M.A., 2020 [143]Germanycross-sectional20–28 April 2020general population160; F 85.6%18–60 y; 25.79 ± 7.31 yonline survey/convenience samplingself-report measure4D BTWT
Salfi F., 2021 [144]Italycross-sectional25 March –3 May 2020general population13,989; F 76,96%34.8 ± 12.2 y; 18–86 yonline survey/snowball samplingself-report measure4D BTWTNAP
Santos-Miranda E., 2021 [145]Spaincross-sectional23 March –6 April 2020general population474; F 54.9%31.9 ± 12.1 y; median 29 (IQR 22–41) yonline survey/convenience samplingself-report measure3D NAP
Sañudo B., 2020 [146]SpainlongitudinalB: February 2020; D: 24 March –3 April 2020general population20; F 45%22.6 ± 3.4 ydevice data/convenience samplingobjective measure (wristband accelerometer, Xiaomi Mi Band 2)5D BTWT
Scarpelli S., 2021 [147]Italycross-sectional10 March–4 May 2020general population5988; F 73.3%≥18online survey/convenience samplingself-report measure3 NAP
Shahzadi K., 2021 [148]Pakistancross-sectional1 June–30 July 2020general population100; F 68%18–50 yonline survey/convenience samplingself-report measure2 WT
Sheehan C., 2023 [149]USAcross-sectionalB: March 2018; D: March 2020general population2,203,861; F 51.2%≥18 ytelephone survey/random samplingself-report measure6D
Singh B., 2021 [150]Indiacross-sectional11–20 May 2020adults1008; F 43.4%18–81 y, median 24 yonline survey/convenience samplingself-report measure4D
Singh V., 2021 [151]Indiacross-sectional1–15 June 2020general population1251; F 29.5%31.71 ± 13.5 yonline survey/convenience samplingself-report measure2D
Sinha M., 2020a [152]Indiacross-sectional1 April–6 May 2020general population1511; F 50.9%≥18 y; 18–80 yonline survey/convenience samplingself-report measure4 NAP
Sinha M., 2020b [153]Indiacross-sectional1–7 May 2020general population/university students1511; F 50.9%≥18 yonline survey/convenience samplingself-report measure6D BTWT
Sinisterra Loaiza L.I., 2020 [154]Spain: Galiciacross-sectional2–15 May 2020adults1350; F 70%63.2 ± 8.1 yonline survey/convenience samplingself-report measure3D
Smith M.L., 2022 [155]UKcross-sectional26 May –5 July 2020young adults2710; F nrmean 27.8 yonline survey/cohortself-report measure4D
Souza T.C., 2022 [156]Brazilcross-sectionalAugust–September 2020general population1368; F 80%≥18 y; median 31 (24–39) yonline survey/convenience samplingself-report measure4D BTWT
Storari M., 2021 [157]Italycross-sectional29 April –17 May 2020general population967; F 58.84%≥18 yonline survey/convenience samplingself-report measure6D BT
Szczepańska E., 2022 [158]Polandcross-sectional2 first weeks of May 2020parents of children1098; F nr20–50 yonline survey/convenience samplingself-report measure2D
Tang N.K.Y., 2022 [159]UKcross-sectionalJuly–September 2020university students/young adults1442; 56.2%18–30 yonline survey/convenience samplingself-report measure6D BT
Taporoski T.P., 2022 [160]BrazillongitudinalB: January 2010–September 2014; D: March 30–29 June 2020general population417; F 70%44 ± 15 ytelephone survey/cohortself-report measure7DTIBBTWT
Trabelsi K., 2021 [161]multi-country: Western Asia/North Africa/Europe/Americascross-sectional6 April –28 June 2020general population5056; F 59.4%≥18 yonline survey/convenience samplingself-report measure4DTIB
Trakada A., 2020 [162]multi-country: Greece/Switzerland/Austria/Germany/France/Brazilcross-sectional25 March–6 April 2020 (Europe); 10–14 2020 (Brazil)general population1622; F nrnronline survey/convenience samplingself-report measure5D
Tsigkas G., 2021 [163]Greececross-sectional13–30 April 2020general population1014; F 48.7%≥35 ytelephone survey/representative sampleself-report measure5D
Urquia Y.J.M., 2022 [164]Brazilcross-sectionalJuly–September 2020general population1828; F 70.5%18–83 yonline survey/convenience samplingself-report measure4D
Valiensi S.M., 2022 [165]Argentinacross-sectional13–30 April 2020general population2594; F 69%42 ± 13 y; 18–85 yonline survey/convenience samplingself-report measure4 BT NAP
van der Werf E.T., 2021 [166]Netherlandscross-sectional22–27 May 2020general population1004; F 50.7%18–88 yonline survey/convenience samplingself-report measure5D
Villadsen A., 2020 [167]UKlongitudinalD: May 2020general population by birth cohort10666; F 60.4%19–62 yonline survey/cohortself-report measure8D
Villasenor Lopez K., 2021 [168]Mexicocross-sectional27 April–17 May 2020general population1084; F 66.5%35.5 ± 13.9 y, 18–86 yonline survey/convenience samplingself-report measure5D
Vinogradov O.O., 2022 [169]Ukrainecross-sectional10–12 May 2020university students86; F 58.1%22.9 ± 0.56 yonline survey/convenience samplingself-report measure2D BTWTNAP
Viselli L., 2021 [170]Italycross-sectionalB: 6–11 October 2016; D: 25–31 March 2020university studentsB: 240, F 80.42%; D: 240B/D: 20.39 ± 1.42 y; 18–25 ynr/non-probability samplingself-report measure4 BTWT
Vollmer C., 2022 [171]Austriacross-sectional24 April –8 May 2020teachers2314; F 72.9%45.3 ± 10.9 yonline survey/convenience samplingself-report measure6D BTWT
Wang X., 2020 [172]Chinacross-sectional23 March –26 April 2020general population2289; F 48.6%27.5 ± 12.0 y; 18–81 yonline survey/convenience samplingself-report measure4D BTWT
Wright K.P., 2020 [173]USAlongitudinalB: 29 January–4 February 2020; D: 22–29 April 2020university students139; F 70.5%22.2 ± 1.7 yonline survey/convenience samplingself-report measure6D BTWT
Yang G., 2021 [174]Chinacross-sectional23 February –4 March 2020general population2702; F 70.7%≥18 y, 37.3 ± 12.0 yonline survey/convenience samplingself-report measure5D
Yang S., 2020 [175]ChinalongitudinalB:23 December 2019–23 January 2020; D: 24–23 February 2020students10082; F 71.7%19.8 ± 2.3 yonline survey/snowball samplingself-report measure6D
Zalech M., 2021 [176]PolandlongitudinalB: 2019; D: 20209university studentsB: 86, F nr; D: 88, F nrB: 23.13 ± 0.86 y; D: 23.10 ± 1.04online survey/nrself-report measure8D
Zheng C., 2020 [177]ChinalongitudinalB: 2019; D: 15–26 April 2020general population631 (B/D: 70); F 61.2%18–35 y; 21.1 ± 2.9 yonline survey/convenience samplingself-report measure5D
Zhu Q., 2021 [178]Chinacross-sectional29 March–5 April 2020general population889; F 61%16–70 y; 31.8 ± 11.4 yonline survey/convenience samplingself-report measure3D
Abbreviations: B: before; D: during; F: female; Y: years; IQR: interquartile range; NR: not reported; D: sleep duration; BT: bedtime; WT: wake-up time; TIB: time in bed; outcomes not included in meta-analysis
Table 2. Percentages of change in sleep duration from before to during the lockdown by country’s area.
Table 2. Percentages of change in sleep duration from before to during the lockdown by country’s area.
Country’s AreaStudiesPercentages of Change in Sleep Duration
ChangeDecreaseIncrease
North America
(Canada, USA)		[47,65,85,98,140]51.4% (95% CI 42.38–60.35; I2 = 93.9%; not significant Eggers´s publication bias)19.8% (95% CI 16.21–23.62; I2 = 75.5%; not significant Eggers´s publication bias)30.5% (95% CI 22.17–39.43; I2 = 94.4%: not significant Eggers´s publication bias)
South America (Argentina, Brazil, Mexico, Peru)[94,109,164,168]71.8% (95% CI 68.76–74.78; I2 = 84.0%; not significant Eggers´s publication bias)27.6% (95% CI 22.20–33.26; I2 = 96.6%; not significant Eggers´s publication bias)40.3% (95% CI 35.40–45.24; I2 = 92.9%; significant Eggers´s publication bias)
Central Asia (Bangladesh, India, Malaysia, Nepal, Pakistan)[31,79,91,96,99,131,150]58.0% (95% CI 44.45–71.01; I2 = 98.5%; not significant Eggers´s publication bias)13.4% (95% CI 9.51–17.86; I2 = 92.2%; not significant Eggers´s publication bias)44.1% (95% CI 32.03–56.63; I2 = 98.3%; not significant Eggers´s publication bias)
East Asia
(China, Japan, Singapore)		[121,172,174,175,178]45.3% (95% CI 25.13–66.21; I2 = 99.9%)11.2% (95% CI 4.40–20.62; I2 = 99.7%)33.3% (95% CI 19.84–48.33; I2 = 99.8%)
West Asia
(Iran, Jordan, Kuwait, Lebanon, Saudi Arabia, United Arab Emirates)		[28,29,33,53,60]55.9% (95% CI 45.40–66.05; I2 = 99.1%; not significant Eggers´s publication bias)18.6% (95% CI 15.43–22.09; I2 = 94.6%; not significant Eggers´s publication bias)36.4% (95% CI 26.10–47.40; I2 = 99.2%; not significant Eggers´s publication bias)
Europe
(France, Germany, Hungary, Netherlands, Poland, Romania, UK)		[44,46,73,90,97,138,139,142,155,166]50.0% (95% CI 43.71–56.23; I2 = 99.2%; not significant Eggers´s publication bias)18.2% (95% CI 12.83–24.31; I2 = 99.4%; not significant Eggers´s publication bias)30.8% (95% CI 27.10–34.56; I2 = 98.0%; not significant Eggers´s publication bias)
Mediterranean Europe (Catalonia, Cyprus, Greece, Italy, Portugal, Spain, Turkey)[38,41,54,71,89,101,105,124,154,157]56.0% (95% CI 41.68–69.78; I2 = 99.6%; significant Eggers´s publication bias)23.6% (95% CI 17.30–30.64; I2 = 98.1%; not significant Eggers´s publication bias)33.8% (95% CI 27.46–40.34; I2 = 99.1%; significant Eggers´s publication bias)
Table 3. Synthesis of changes in outcomes from before to during the lockdown.
Table 3. Synthesis of changes in outcomes from before to during the lockdown.
Author, YearOutcomeDirection of Change a
Duration
Abouzid M., 2021 [25]Significant increase in sleep hours for 53.2% (p < 0.001).↑
Blume C., 2021 [48]Significant increase in sleep duration by about 13 min (p < 0.001).↑
Casas R., 2022 [55]Nearly half of the participants reported no change in sleep duration.≈
Dragun R., 2020 [75]Significant increase in the median length of sleep duration by 1.5 h (p < 0.001).↑
Falkingham J., 2022 [80]Increase in the prevalence of sleep loss compared to 2019 (22% vs. 13.9%) particularly marked among the women and the Black, Asian, and the individuals of other minorities (p < 0.01). ↓
García-Garro P.A., 2022 [87]Significant decrease in sleep duration (p < 0.001).↓
Gibson R., 2022 [88]No significant change in the weighted 24 h sleep duration (p = 0.161).≈
Khojasteh M.R., 2022 [102]Significant increase in sleep duration (p < 0.001).↑
Kim A.C.H., 2022 [104]Significant increase in sleep time in the young (18–39 y) and middle-aged participants (40–59 y) (p < 0.001). No significant decrease in the older participants (60 ≥ y). ↑Y ↑M ↓O
Kontsevaya A.V., 2021 [106]Significant decrease in the number of days per week that participants reported not getting enough sleep (from 3.21 ± 2.44 to 2.86 ± 2.57, p < 0.001).↑
Li J.W., 2021 [108]Significant increase in sleep duration by 0.5 h (p < 0.001).↑
Majumdar P., 2020 [113]Significant decrease in sleep duration in office workers (p < 0.001) and significant increase in sleep duration in students (p < 0.001). ↓W ↑S
Pachocka L., 2022 [123]Decrease in sleep hours. ↓
Pépin J.-L., 2021 [126]Significant increase in objectively measured total sleep time (p < 0.01).↑
Peterson M., 2021 [128]Significant increase in sleep duration (p = 0.016).↑
Pouget M., 2022 [132]No significant change in hours of sleep per night.≈
Ruiz-Zaldibar C., 2022 [141]Significant increase in adequate nighttime sleep (7 to 9 h per night) in both the males (p = 0.011) and females (p < 0.001).↑
Salfi F., 2021 [144]Significant difference between the three chronotype groups (evening-type/neither-type/morning type) for the reported sleep duration (p  <  0.001): the evening-type slept more than the neither-type and morning-type groups.↑ET
Santos-Miranda E., 2021 [145]Significant increase in sleep hours (p < 0.001).↑
Souza T.C., 2022 [156]Significant increase in sleep hours (p <0.001).↑
Tang N.K.Y., 2022 [159]More participants reported an increase in sleep duration.↑
Trakada A., 2020 [162]Significant increase in sleep duration (p < 0.001).↑
Tsigkas G., 2021 [163]Significant increase in the percentage of people sleeping > 7 h (p < 0.001) mainly in the younger persons and in those with a higher income (p < 0.001).↑
Vinogradov O.O., 2022 [169]No change in sleep duration.≈
Vollmer C., 2022 [171]Increase in sleep duration on workdays but not on weekends (p < 0.001).↑
Time in bed
Czeisler M.É., 2021 [69]Increased time in bed.↑
Gibson R., 2022 [88]Significant increase in time in bed both on workdays and on weekends (p < 0.0001).↑
Pépin J.-L., 2021 [126]Significant increase in time in bed (p < 0.01).↑
Sleep timing
Bedtime
Aishworiya R., 2021 [27]Delay in bedtime. →
Anastasiou E., 2021 [37]Delay in bedtime.→
Azizi A., 2020 [42]Significant delay in bedtime (p < 0.0001).→
Bottary R., 2022 [50]Significant delay in bedtime (p < 0.001).→
Elhadi M., 2021 [76]Significant delay in bedtime (p < 0.001).→
Gibson R., 2022 [88]Significant delay in bedtime both on workdays and on weekends (p < 0.001).→
Kaizi-Lutu M., 2021 [100]Among the participants, 36.3% reported an earlier bedtime.NA
Mónaco E., 2022 [119]Delay in bedtime.→
Pépin J.-L., 2021 [126]No significant delay in bedtime. Greater delay in eveningness compared to morningness chronotypes (p < 0.01).≈
Perez-Carbonell L., 2020 [127]Among the participants, 30% reported a delay in bedtime.NA
Sañudo B., 2020 [146]Earlier bedtime.←
Souza T.C., 2022 [156]Significantly earlier bedtime (p < 0.0001).←
Tang N.K.Y., 2022 [159]Delay in bedtime both in the students and in the young adults.→
Vinogradov O.O., 2022 [169]Delay in bedtime.→
Vollmer C., 2022 [171]Significant delay in bedtime on workdays but not on weekends, especially in the youngest teachers (p < 0.001).→
Wake-up time
Aishworiya R., 2021 [27]Delay in wake-up time both in the mothers and fathers.→
Anastasiou E., 2021 [37]Delay in wake-up time.→
Azizi A., 2020 [42]Significant delay in wake-up time (p < 0.0001).→
Diz-Ferreira E., 2021 [74]Significantly earlier wake-up time (p < 0.001).←
Elhadi M., 2021 [76]Significant delay in wake-up time (p < 0.001).→
Gibson R., 2022 [88]Significant delay in wake-up time both on workdays (p < 0.0001) and on weekends (p < 0.001).→
Kaizi-Lutu M., 2021 [100]Among the participants, 36.3% reported an earlier wake-up time.NA
Kontsevaya A.V., 2021 [106]No significant change in the number of days per week the participants reported an earlier wake-up time. ≈
Mónaco E., 2022 [119]Delay in wake-up time.→
Pépin J.-L., 2021 [126]No significant delay in wake-up time. Greater delay in eveningness compared to morningness chronotypes (p < 0.01).≈
Scarpelli S., 2021 [147]The majority of the participants (60.9%) reported changes in wake-up time.NA
Sañudo B., 2020 [146]Delay in wake-up time.→
Shahzadi K., 2021 [148]Delay in wake-up time.→
Souza T.C., 2022 [156]Significant delay in wake-up time (p < 0.0001).→
Vinogradov O.O., 2022 [169]Delay in wake-up time.→
Vollmer C., 2022 [171]Significant delay in wake-up time both on workdays (p < 0.001) and on weekends (p = 0.027).→
Napping habits
Alomari M.A., 2021 [33]Significant decrease in nap hours (p < 0.0001).↓
AMHSI Research Team, 2021 [36]Significant increase in the percentage of participants taking regular naps (p = 0.004).↑
Azizi A., 2020 [42]Significant increase in the length of naps (p < 0.0001).↑
Chouchou F., 2021 [63]Significant increase in the frequency and in the length of naps (p < 0.001).↑
Conte F., 2021 [64]No significant change in the frequency and in the length of naps. ≈
Felician J., 2022 [81]Decrease in the percentage of participants taking naps (from 42% to 36%)↓
Franceschini C., 2020 [84]Most of the good sleepers did not change or reduce the length of naps while the poor sleepers reported an increase (p < 0.001).↓GS↑PS
García-Esquinas E., 2021 [86]Decrease in the percentage of participants taking naps (from 65% to 45%).↓
Gupta R., 2020 [91]Significant increase in the percentage of participants taking naps (p < 0.001).↑
Kaizi-Lutu M., 2021 [100]Increase in the frequency of naps.↑
Leone M.J., 2020 [107]Significant decrease in the percentage of participants taking naps both on weekdays (from 58.1% to 48.1%, p < 0.0001) and on weekends (from 51.3% to 66.3% p < 0.0001).↓
Majumdar P., 2020 [113]Significant increase in the length of naps both in the students and in the office workers (p < 0.05).↑
Mohsin A., 2021 [118]Increase in the frequency (from 40.1% to 50.8%) and the length of naps (from 26.6% to 34.9% of nap exceeding one hour).↑
Mónaco E., 2022 [119]Increase in the frequency of long naps.↑
Morin C.M., 2022 [120]Significant increase in the frequency of naps (almost twice) (p < 0.0001).↑
Petrov M.E., 2021 [129]Significant increase in the frequency of naps (p < 0.001).↑
Ramirez C., 2022 [136]Significant increase in the length of naps both on workdays (p < 0.001) and on weekends (p < 0.01).↑
Salfi F., 2021 [144]The majority of the participants maintained unchanged napping habits (64.7%). A significantly higher percentage of the evening-type subjects reported changes in napping habits compared to the morning-type and neither-type chronotypes (p < 0.01).≈
Santos-Miranda E., 2021 [145]Increase in the frequency of naps (p = 0.051). Significant increase in the length of naps (p = 0.034).↑
Scarpelli S., 2021 [147]The majority of the participants (60.8%) reported changes in napping habits.NA
Sinha M., 2020a [152]The majority of the participants reported an increase in the frequency of naps.↑
Valiensi S.M., 2022 [165]The majority of the participants reported no change in the frequency of naps and a decrease in the length of naps≈F↓L
Vinogradov O.O., 2022 [169]Slight increase in the percentage of the participants taking naps (from 62.8 to 69.8%); no change in the length of naps.↑P≈L
Abbreviations: ET: evening-type chronotype; F, frequency; GS: good sleepers; L, length; M: middle-aged; NA, not applicable; O: older; P: percentage; PS: poor sleepers; W, workers; S, students; Y: young. a ↑ indicates an increase, ↓ indicates a decrease, ≈ indicates no change, → indicates a delay in a sleep timing, ← indicates earlier sleep timing.
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Ceolin, C.; Limongi, F.; Siviero, P.; Trevisan, C.; Noale, M.; Catalani, F.; Conti, S.; Di Rosa, E.; Perdixi, E.; Remelli, F.; et al. Changes in Sleep Duration and Sleep Timing in the General Population from before to during the First COVID-19 Lockdown: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2024, 21, 583. https://doi.org/10.3390/ijerph21050583

AMA Style

Ceolin C, Limongi F, Siviero P, Trevisan C, Noale M, Catalani F, Conti S, Di Rosa E, Perdixi E, Remelli F, et al. Changes in Sleep Duration and Sleep Timing in the General Population from before to during the First COVID-19 Lockdown: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2024; 21(5):583. https://doi.org/10.3390/ijerph21050583

Chicago/Turabian Style

Ceolin, Chiara, Federica Limongi, Paola Siviero, Caterina Trevisan, Marianna Noale, Filippo Catalani, Silvia Conti, Elisa Di Rosa, Elena Perdixi, Francesca Remelli, and et al. 2024. "Changes in Sleep Duration and Sleep Timing in the General Population from before to during the First COVID-19 Lockdown: A Systematic Review and Meta-Analysis" International Journal of Environmental Research and Public Health 21, no. 5: 583. https://doi.org/10.3390/ijerph21050583

APA Style

Ceolin, C., Limongi, F., Siviero, P., Trevisan, C., Noale, M., Catalani, F., Conti, S., Di Rosa, E., Perdixi, E., Remelli, F., Prinelli, F., & Maggi, S. (2024). Changes in Sleep Duration and Sleep Timing in the General Population from before to during the First COVID-19 Lockdown: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 21(5), 583. https://doi.org/10.3390/ijerph21050583

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