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The prevalence of insomnia in different COVID-19 policy phases: Longitudinal evidence from ITA.LI – Italian Lives



This study investigated changes in the prevalence of insomnia in Italy during COVID-19, starting from the first lockdown period (8 March 2020). We hypothesized that lockdown precipitated increased prevalence of insomnia symptoms relative to the pre-pandemic period; b) the gradual relaxation of containment measures – post-lockdown period (Phase 2 and Phase 3) – reduced insomnia severity, leading to a relative recovery of pre-pandemic levels; and c) we tested age-related heterogeneity in sleep responses, with an expected higher increase in insomnia in younger and middle-age groups.


Analyses drew on a subsample (N = 883) of respondents to ITA.LI – Italian Lives, a recently established panel study on a probability sample of individuals aged 16 + living in Italy. To estimate patterns of change in insomnia, we first fitted a random-effects ordered logistic model on the whole sample. We then added an interaction term between policy phases and the respondent age to test whether the relationship between insomnia and policy phases differed across age groups. Analyses accounted for survey non-response weights.


The fraction of respondents reporting moderate (“somewhat” + 0.159, S.E. 0.017) or severe (“very much” + 0.142, S.E. 0.030) sleep disturbances significantly increased during Phase 1. The prevalence of insomnia followed an inverted U-shaped curve across policy phases, with further increases from baseline levels (“somewhat” + 0.168, S.E. 0.015; “very much” + 0.187, S.E. 0.030) during Phase 2, followed by a relative reduction in Phase 3, although it remained significantly higher than in the pre-pandemic period (“somewhat”, + 0.084, S.E. 0.016; “very much”, + 0.045, S.E. 0.010). There were significant age-related differences in insomnia patterns, as the discrete change from pre-pandemic levels in the probability of not suffering from insomnia was negative and significant for the younger age group (− 0.269, S.E. 0.060) and for respondents aged 35–54 (− 0.163, S.E. 0.039).


There is reason to believe that the emergency policy response to the COVID-19 crisis may have had unintended and possibly scarring effects in terms of increased prevalence of insomnia. The hardest hit were young adults and, to a lesser extent, the middle-aged; however, older respondents (55 +) remained resilient, and their insomnia trajectory bounced back to pre-pandemic levels.

Peer Review reports


According to the World Health Organization epidemiological update, as of early February 2022, about 400 million people have been diagnosed with the SARS-CoV-2 infection (COVID-19) and over 5.6 million people died from it throughout the world [1]. In addition to those who have been directly infected, many others have been affected by the psycho-social and economic consequences of COVID-19. From early in 2020, governments introduced measures to reduce the number of infections, including physical distancing, limits to freedom of movement both intra-and internationally and the closure of social spaces [2]. In addition to measuring the effects of these public health precautions against infection and mortality, which were central to society’s response to COVID-19, it is increasingly relevant to consider other costs associated with their widespread adoption [3]. Indeed, there is abundant evidence, produced internationally, of COVID-19-related psychological disorders (e.g. anxiety, depression and stress); reduced life satisfaction and domains satisfaction; and diminished happiness and flourishing [4,5,6]. Sleep has also been impaired [7,8,9].

There are a few mechanisms through which the pandemic could have affected sleep. Containment measures – such as lockdowns, quarantines, and mobility restrictions – have protected against the spread of the COVID-19 infection [10]; however, they have resulted in immediate and unintended detrimental effects. Workplace closures and changes in working times and rhythms dramatically altered daily schedules and routines, thus contributing to changes in sleep habits and patterns (in terms of wake-up times, sleep timing and sleep duration), which are associated with the circadian rhythms that regulate sleep [11,12,13]. Lack of regular exercise or, more generally, lower levels of physical activity following stay-at-home requirements reduced the need for sleep and led to difficulty falling asleep or to early wake-ups [14]. Physical distancing and confinement increased loneliness, raising perceived anxiety, and COVID-19 related worries, which in turn resulted in increased sleep disturbances [15,16,17]. Moreover, fear of illness has been widely documented in past research, especially the fear of contagion from infectious diseases [18,19,20,21]. During the COVID-19 pandemic, risk perception about own and others’ health and well-being increased, including the fear of falling ill or that a family member or friend would fall ill, the probability of contracting the COVID-19 or the fear of not surviving in the event of contracting COVID-19. As several studies have shown, the higher the level of perceived risk is, the worse the mental health and sleep disorders [22, 23]. In the scientific literature, the relationship between risk perception and poor sleep quality is well-documented, especially among healthcare workers [24, 25]. In the general population, Rossi and colleagues [26] highlighted a strong association between COVID-19 risk factors and severe insomnia symptoms in Italy. Blanc et al. [27] confirmed these results in their study analysing the relationship between risk perception and the Pittsburgh Sleep Quality Index (PSQI) in New York (US).

Aim and objectives

Against this background, this study employed panel data on a sample of 883 individuals aged 16 + in Italy and investigated the COVID-19 related effects on insomnia, which is a condition of subjectively inadequate or non-restorative sleep [28]. First, we anticipated (Hypothesis 1) that the period of lockdown acted as a precipitating event and, like other stressful life experiences, could lead indirectly to the onset of insomnia [9, 13, 29]. Moreover, we intended to test the severity of sleep disturbances in the longer term. Therefore, we hypothesized (Hypothesis 2) that the gradual relaxation of containment measures, which took place in different policy phases, could possibly reduce insomnia and lead to a relative recovery towards baseline levels [30, 31]. Finally, we tested age-related heterogeneity in sleep responses to the health emergency and associated government restrictions. In greater detail, building on previous research which showed, even if not unequivocally [8, 32], that the severest psycho-social impact of the pandemic and policy measures to mitigate its impact have been produced on young people and adults [13, 33], we expected (Hypothesis 3) a significant increase in insomnia in the younger and middle-aged groups relative to their older counterparts.



Analyses drew on a subsample of respondents to ITA.LI – Italian Lives (hereafter ITA.LI), which is a recently established panel study based on a probability sample of 8,967 individuals aged 16 or older living in 4,900 households in Italy. From June 2019, it has collected crucial information on a wide set of topics, including family structures and dynamics, housing, education and employment, income and wealth and health and well-being. From April to September 2020, all panel members who had already taken part in ITA.LI wave 1 were invited to participate in a COVID-19 ad hoc survey (hereafter ITA.LI COVID-19). The ITA.LI COVID-19 survey, which was conducted using computer-aided web interviewing (CAWI) and computer-assisted telephone interviewing (CATI) methods [34], gathered information on the psychological, social and economic consequences of COVID-19 and mainly used items and measures already employed in ITA.LI wave 1. Of the 2,415 eligible people with direct contacts, 950 participated in the ITA.LI COVID-19 survey and completed the questionnaire. When merging data collected from ITA.LI wave 1 with the records of the respondents to ITA.LI COVID-19, data on 67 participants were dropped and excluded from the analysis because these were not matched to the previous wave of data collection or because of missing information on selected variables. Hence, 883 respondents were included in the regression analysis. The flowchart of the study design is shown in Fig. 1, and the sociodemographic characteristics of the sample are given in Table 1.

Fig. 1
figure 1

Flowchart of study design

Table 1 Sample (N = 886)


Insomnia was assessed on a 3-point scale (0 “Not at all”; 1 “Somewhat”; 2 “Very much”), using the following question: “Have you experienced any difficulties in falling asleep or insomnia?”.

Policy phases were categorized as follows, based on the date of data collection in the ITA.LI COVID-19 survey and following key policy responses implemented at the national level: pre-pandemic (reference period), until 8 March 2020; first nation-wide lockdown (Phase 1), from 9 March until 3 May 2020, during which severe restrictions were in place to help stop the spread of COVID-19; Phase 2, from 4 May to 2 June 2020, in which several containment measures were relaxed, and retail shops, bar and restaurants and bars and personal services were reopened under COVID-19 safety guidelines; summer reopening (Phase 3), from 3 June 2020, onwards, in which most mandatory COVID-19 regulations, such as mobility restrictions across regions and restrictions to and from other EU countries, were lifted or removed.

Building on the relevant literature, gathered during COVID-19 and beyond [7, 29, 35], the following covariates were included in the models: socio-demographic variables: age (recoded in the following categories: 16–34; 35–54; 55 and older); gender (male; female); educational level (recoded in the following categories: primary, secondary, tertiary); cohabiting with a partner or spouse (no; yes); children living in the household by age group (recoded in the following categories: no children aged 0–14 years; pre-schoolers; children aged 7–14 years); and personality traits, measured with an Italian adaptation of the Big Five Inventory – Short version (BFI–S) [36]: agreeableness, extraversion, conscientiousness, neuroticism and openness.

Statistical analysis

To estimate patterns of change in insomnia at different policy phases, we first fitted a random-effects ordered logistic model on the whole sample of respondents. This choice was justified by the ordered categorical nature of the outcome variable, which was measured at two time points for each respondent, one before the onset of the pandemic and one in the first six months (April–November 2020) of the pandemic. The model was specified as follows:

$${y}_{it}^{*}={\beta }^{\mathrm{^{\prime}}}{x}_{it}+{\alpha }_{i}+{\epsilon }_{it}\left(i=1,\dots , N;t=1,\dots T\right)$$

under the assumption:

$${\alpha }_{i}\sim N\left(0,{\sigma }^{2}\right); {\epsilon }_{it}\sim logistic\left(0,{\pi }^{2}/3\right);cov\left({\alpha }_{i, }{\epsilon }_{it}\right)$$

where \({y}_{it}^{*}\) denotes the latent continuous outcome of individual i on occasion t; a threshold model determines the observed response \({y}_{it}\) as follows:

$${y}_{it}=1 if {y}_{it}^{*}\le {k}_{1}; {y}_{it}=2 if {{k}_{1}<y}_{it}^{*}\le {k}_{2}; {y}_{it}=3 if { y}_{it}^{*}>{k}_{2};$$

to the \({k}_{th}\) category if \({\alpha }_{k-1}<{y}_{it}<{\alpha }_{k}\) where \({\alpha }_{k-1}\) and \({\alpha }_{k}\) denote the lower and upper boundaries of the \({k}_{th}\) category; \({x}_{it}\) is a set of observed covariates (and interaction terms) associated with the outcome; \({\alpha }_{i}\) is the individual-specific and time-invariant unobserved heterogeneity; and \({\epsilon }_{it}\) is the idiosyncratic error term.

We then added an interaction term between policy phases and the age of respondents to test whether the relationship between insomnia and the policy phases differed across individuals of different ages. Analyses accounted for ITA.LI COVID-19 survey nonresponse weights.


The analysis was based on 883 respondents out 2,415 people eligible for the ITA.LI COVID-19 survey (36.6%). A total of 109 respondents from ITA.LI wave 1 (12.3%) were re-interviewed during Phase 1; 130 (14.7%) during Phase 2; and finally, 647 (73.0%) in Phase 3. Of the respondents analysed, 6.3% were 16–34 years old, 38.6% aged 35–54 years and 45.1% 55 years or more; 60.0% were female, and 31.6% had a primary, 50.9% secondary and 17.5% tertiary education level. In terms of household composition, 57.2% were cohabiting and 82.5% had no children aged 0–14 years (Table 1). Summary statistics show that before the pandemic outbreak 64.3% of the total sample did not report any sleep disturbance. However, the same proportion reduced to 29.9% during Phase 1, i.e. confinement; it further reduced in Phase 2 (25.6%) and reached 51.9% in Phase 3. As for age groups, the proportion of individuals aged 16–34 reporting moderate or severe insomnia increased from 17.4% before the onset of COVID-19 to 58.3% in Phase 2 and to 64.5% in Phase 3, and it got to 41.4% in Phase 3. The prevalence of sleep disturbance among adults aged 35–54, which was 33.3% before the pandemic, augmented to 77.1% in Phase 1, 73.2% in Phase 2 and 47.5% in Phase 3. Finally, the fraction of adults aged 55 + suffering from moderate or severe insomnia raised from 44.4%, i.e. pre-pandemic levels, to 71.0% in Phase 1, to 85.3% in Phase 2, and it got to 50.3% in Phase 3.

Table A1 in the Annex reports the parameter estimates, the estimated cut-off points and the estimated panel-level variance component of the random-effects ordered logistic models. To better and more easily interpret the results, we ran the “margins” command in STATA 17 and obtained the predicted probability for each of the three possible outcomes or categories of the outcome variable, for each level of the predictors (i.e. policy phase and the interaction between policy phase and age). We also computed the marginal change in the probability of each outcome. The predicted probabilities for the three categories of insomnia, which were plotted using the “marginsplot” command, are displayed in Fig. 2 (see also Table A2 in Annex for predicted probabilities and pairwise comparisons). Discrete changes in the probability of each outcome are reported in Table 2.

Fig. 2
figure 2

Predictive margins of policy phases on insomnia, by age groups

Table 2 Marginal effects of policy phases

On average, the fraction of respondents reporting moderate (“somewhat” + 0.159, S.E. 0.017) or severe (“very much” + 0.142, S.E. 0.030) sleep disturbance significantly increased during the lockdown period (Phase 1). Therefore Hypothesis 1 was supported. The gradual reopening following the lockdown period in Phase 2 did not bring about any significant immediate reduction in the proportion of sampled individuals having difficulties in falling asleep or staying asleep, which in fact further increased from pre-pandemic levels (“somewhat” + 0.168, S.E. 0.015; “very much” + 0.187, S.E. 0.030). However, as we can see from the curve illustrated in Fig. 2, in Phase 3 the prevalence of insomnia fell compared to that reported in previous policy phases, but it remained significantly higher than during the pre-pandemic period: the increase in the predicted probability of suffering from sleep loss was + 0.084 (S.E. 0.016) for the outcome “somewhat” and + 0.045 (S.E. 0.010) for the outcome “very much”. Thus Hypothesis 2 was partially supported.

Interaction tests indicated that changes in sleep patterns across policy phases were significantly different for respondents in different age groups. Before COVID-19, the predictive probability of an individual aged 16–34 not indicating any problem of insomnia at all was 0.860 (S.E. 0.027); however, it was 0.665 (S.E. 0.027) among respondents aged 35–54 and 0.537 (S.E. 0.025) among those 55 + . The probability of not suffering from insomnia significantly diminished for respondents in each age subgroup in the lockdown policy phase (Phase 1); accordingly, the reduction in the probability of this category was offset by an increase in the other two categories. That said, it is worth mentioning that sleep deterioration during the confinement (Phase 1) was significantly stronger for respondents aged 16–34, for whom the discrete change in the probability of not suffering at all from insomnia was − 0.486 (S.E. 0.103), than for their older counterparts, especially those aged 55 + (− 0.173, S.E. 0.063). In Phase 2, when containment measures were partially lifted, the overall prevalence of sleep disturbances remained substantially unchanged for each age subgroup, compared to that documented in the previous period, and pairwise comparisons showed that the predictive margins of reporting moderate or severe insomnia were not significantly different among the three age subgroups. Finally, during the summer reopening (Phase 3), the proportion of respondents indicating moderate or severe difficulty in initiating or maintaining sleep significantly fell for all age subgroups: the average predicted probability of not having reported sleep disturbances at all was 0.591 (S.E. 0.059) for respondents aged 16–34; 0.502 (S.E. 0.033) for those aged 35–54; and 0.473 (S.E. 0.029) for those aged 55 and over. Pairwise comparisons indicated that, because of these trends, the discrete change from pre-pandemic levels in the probability of not suffering from insomnia was negative and significant for the younger age group (− 0.269, S.E. 0.060) and for respondents aged 35–54 (− 0.163, S.E. 0.039); however, it was slightly negative but not statistically significant, at the 5% level, for the 55 + . Therefore, Hypothesis 3 was fully supported.


This study employed panel data techniques on data collected from a sample of respondents aged 16 + in Italy to investigate whether the COVID-19 pandemic resulted in sleep deterioration. Analyses were also focused on ascertaining if the gradual easing of containment measures was associated with significant improvement in sleep disturbances over a short time span (from April to September 2020) and on assessing possible age-related heterogeneity in pandemic-related effects on sleep.

The results confirmed previous research in the field and showed that, under COVID-19, average sleep quality declined compared to the pre-pandemic baseline, after adjusting for a set of common predisposing risk factors for loss of sleep. We could not test and demonstrate if the relatively higher prevalence of insomnia was a direct result of restrictions related to COVID-19 or if it was rather the consequence of increased worries, social isolation, fear of contagion or infection, stress or even the feeling of not being in control of the events that influence one’s own life, which has been found to negatively affect health and well-being [37]. Nonetheless, analyses fitted on individuals interviewed at different dates, which were indicative of different policy phases, showed that the quality of sleep followed a U-shaped trajectory over the course of the pandemic. In particular, the onset of COVID-19 and the implementation of lockdown (Phase 1) were associated with a sharp increase in the prevalence of moderate or severe insomnia, which did not recede in the so-called Phase 2, when containment measures were initially relaxed. Thus, the evidence collected points to the fact that a possible immediate improvement in the quality of life, resulting from the end of strict stay-at-home requirements, did not bring about an immediate recovery in the sample overall. A plausible explanation for this unexpected pattern is that even if people have the potential for resilience – that is, to report adaptive or better-than-expected outcomes despite significant risk and/or adversity [38, 39] – being able to improve and, particularly, to restore sleep requires time [40]. Recovery from sleep loss did not start until June 2020, but the pattern of recovery that emerged over the summer months (Phase 3) was only partial and ended up in a significant negative divergence from pre-pandemic levels. Hence, this study confirms previous research indicating that average sleep levels deteriorated within the first few months of the COVID-19 pandemic [7, 11]. It also contributes to the existing literature and shows that, despite steady improvements occurring over the summer of 2020, after the lifting of most COVID-19 containment measures, sleep did not bounce back to the pre-pandemic baseline. In other words, the negative effects that developed at the onset of COVID-19 apparently persisted over the course of the pandemic, at least within the short time span under investigation. It is worth noting that, due to the nature of the available data, we could not test whether this positive trajectory continued over the autumn and winter months of 2020 and eventually benefited from seasonal effects, despite an overall resurgence in COVID-19 infections and a second round of pandemic restrictions.

The impact of COVID-19 on insomnia is consistent with the results of several studies, although few of them highlighted age differences in sleep patterns and quality [40,41,42]. A considerable part of available research in the field has used cross-sectional and/or convenience samples [4], which prevents comparison of the change in the prevalence of sleep disturbances among people of different ages before and after the pandemic. The findings here thus revealed distinct patterns of change in sleep, based on the age of the respondents, over the first months of the pandemic. Those aged 16–34 experienced the sharpest increase in sleep disturbances at the very beginning during the period of national lockdown; adults aged 35–54 followed and reported the second-severest initial disruption of sleep compared to the pre-lockdown baseline. Sleep disturbances persisted and remained at the same levels in the subsequent policy phase. A subsequent recovery followed, which started later than expected and improved sleep. However, this pattern of bouncing back was experienced differently by the age subgroups: the prevalence of insomnia remained higher than the pre-pandemic baseline for younger and adult respondents, whereas it did not show any significant divergence from what was observed before the COVID-19 onset for the oldest adults.

Overall, respondents aged 16–34 were the worst affected, and such an acute deterioration in sleep added up to a marked decline in mental health that has already been recorded in previous research [43]. It must be emphasized that this group of young adults has been facing many normative transitions, such as changes in their educational and professional condition, peer and romantic relationships and other social and emotional experiences such as living alone for the first time, which are stressful [44]. The onset of the pandemic, along with the implementation of containment measures, and the consequent socio-economic outcomes may have complicated or interrupted these normative transitions, increasing stress and worsening sleep for young adults. For this subgroup, the only partial recovery of pre-lockdown sleep quality could be a hint that sleep disruption has long-term consequences. From a life course perspective, an exposure acting during a specific period could have lasting or lifelong effects on the structure or function of organs, tissues and body systems that are not modified in any dramatic way by later experiences. Adopting this “critical period” model [45], insomnia experienced during the pandemic could have a deeper effect on the health and wellbeing of young people over their lifetime than would be acknowledged in considering it as a transitory effect.

Despite significant findings, this study has a few limitations, in addition to those already discussed, which need to be acknowledged. First, we used a self-assessed measure of insomnia, which is therefore subject to recall bias and participants’ perceptions [46]. Second, we used a single-item, three-category self-report measure of sleep quality, which may have lacked sufficient variation and validity to accurately estimate effects, whereas existing questionnaires assessing the quality of sleep such as the Pittsburgh Sleep Quality Index (PSQI), the Athens Sleep Questionnaire (ASQ) or the National Sleep Foundation (NSF, USA) could overcome this limit. Third, we were not able to measure the duration of insomnia; however, disturbed sleep that lasts several nights to a month is considered short-term insomnia and is usually associated with a persistent stressful situation or environmental factors [28]. Fourth, there was a significant attrition rate between data collected in ITA.LI wave 1 and data collected in ITA.LI COVID-19. This means that follow-up data were not available for more than 60% of participants surveyed in wave 1 [47]. Fifth, although the longitudinal design and measurement of sleep disruption symptoms at different timepoints allows for prediction of changes in insomnia symptoms, the lack of manipulation of sleep limits causal interpretations.


To the best of our knowledge, this is among the first studies to use panel data to investigate changes in the prevalence of insomnia under COVID-19 in Italy. Our analyses tracked changes in sleep disturbances before the pandemic outbreak, into the first lockdown period, and in the following six months when bans and restrictions were progressively lifted or removed. The results indicated increased levels of insomnia following the initial strict confinement that declined but persisted in the longer term. Hence, there is reason to believe that the emergency policy response to the COVID-19 crisis may have had scarring effects. In this respect, the hardest hit were the young adults (16–34), and, to a lower extent, adults aged 35–54; however, the older respondents (55 +) remained resilient, and their insomnia trajectory bounced back to pre-pandemic levels. Further research on the nature and extent of pandemic-related outcomes and on their association with mitigation measures is needed to inform and guide decision-makers, mitigate the unintended consequences of policy responses and prevent increasing social inequalities in a complex, highly uncertain and rapidly changing context.

Availability of data and materials

The datasets generated and analysed during the current study are not publicly available due to the necessity to protect data still under embargo. In accordance with EU General Data Protection Regulation (EU 2016/79) and the University of Milano-Bicocca internal regulations (D.R. 6256/2018, prot. 90,980/18), ITA.LI – Italian Lives data are fully encrypted, stored anonymously in the cloud and protected against unauthorized access, disclosure, modification or destruction. ITA.LI – Italian Lives data (specifically the ITA.LI – Italian Lives wave 1 and ITA.LI COVID-19 survey data used for this study) are currently available only to researchers working at the ITA.LI – Italian Lives project who have completed a registration process with the personal data protection officer, in accordance with the above-mentioned regulations. The same data will be made publicly available to researchers from the University of Milano-Bicocca and through Cross-National Equivalent File ( in due time. However, the data release policy (time of public release, details of how to apply for and access the datasets, end user licence, etc.) has not been formally defined yet. Due to this lack of formal policies and procedures, all data underlying the findings may be currently accessed, only for the purpose of reproducing the analyses, through the corresponding author (or any of the remaining authors) and the personal data protection officer at University of Milano-Bicocca. The personal data protection officer can be contacted (at or certified email for all queries concerning personal data processing and the exercise of any rights deriving from General Data Protection Regulation (EU 2016/79). The ITA.LI – Italian Lives Data Controller is the University of Milano-Bicocca, represented by its legal representative, the Rector Giovanna Iannantuoni ( or certified email All relevant materials that may reasonably be requested by others to reproduce the results will made available upon the publication of the study.


  1. WHO. Weekly epidemiological update on COVID-19 – 6 July 2022. 2022.

  2. Varma A, Dergaa I, Ashkanani M, Musa S, Zidan M. Analysis of Qatar’s successful public health policy in dealing with the Covid-19 pandemic. Int J Med Rev Case Rep. 2021;5(2):6–11.

    Google Scholar 

  3. Akbari HA, Pourabbas M, Yoosefi M, Briki W, Attaran S, Mansoor H, Moalla W, Damak M, Dergaa I, Teixeira AL, Nauman J. How physical activity behavior affected well-being, anxiety and sleep quality during COVID-19 restrictions in Iran. Eur Rev Med Pharmacol Sci. 2021;25(24):7847–57.

    CAS  PubMed  Google Scholar 

  4. Aknin LB, De Neve JE, Dunn EW, Fancourt DE, Goldberg E, Helliwell JF, et al. Mental Health during the first year of the COVID-19 pandemic: a review and recommendations for moving forward. Perspect Psychol Sci. 2022;17(4):915–36.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Cénat JM, Blais-Rochette C, Kokou-Kpolou CK, Noorishad PG, Mukunzi JN, McIntee SE, et al. Prevalence of symptoms of depression, anxiety, insomnia, posttraumatic stress disorder, and psychological distress among populations affected by the COVID-19 pandemic: a systematic review and meta-analysis. Psychiatry Res. 2021;295:113599.

    Article  PubMed  CAS  Google Scholar 

  6. Wu T, Jia X, Shi H, Niu J, Yin X, Xie J, et al. Prevalence of mental health problems during the COVID-19 pandemic: a systematic review and meta-analysis. J Affect Disord. 2021;281:91–8.

    Article  CAS  PubMed  Google Scholar 

  7. Alimoradi Z, Broström A, Tsang HWH, Griffiths MD, Haghayegh S, Ohayon MM, et al. Sleep problems during COVID-19 pandemic and its association to psychological distress: a systematic review and meta-analysis. EClinicalMedicine. 2021;36:100916.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Halsøy Ø, Johnson SU, Hoffart A, Ebrahimi OV. Insomnia symptoms in the general population during the COVID-19 pandemic. Front Psychiatry. 2021;12:762799.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Morin CM, Vézina-Im LA, Ivers H, Micoulaud-Franchi JA, Philip P, Lamy M, et al. Prevalent, incident, and persistent insomnia in a population-based cohort tested before (2018) and during the first-wave of COVID-19 pandemic (2020). Sleep. 2020;45.1:zsab258.

    Google Scholar 

  10. Talic S, Shah S, Wild H, Gasevic D, Maharaj A, Ademi Z, et al. Effectiveness of public health measures in reducing the incidence of COVID-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis. BMJ. 2021;375:e068302.

    Article  PubMed  Google Scholar 

  11. Gupta R, Grover S, Basu A, Krishnan V, Tripathi A, Subramanyam, et al. Changes in sleep pattern and sleep quality during COVID-19 lockdown. Ind J Psychiatry. 2020;624:370–8.

    Google Scholar 

  12. Ong JL, Lau T, Karsikas M, Kinnunen H, Chee MW. A longitudinal analysis of COVID-19 lockdown stringency on sleep and resting heart rate measures across 20 countries. Sci Rep. 2021;11(1):1–11.

    Article  CAS  Google Scholar 

  13. Yuksel D, McKee GB, Perrin PB, Alzueta E, Caffarra S, Ramos-Usuga D, et al. Sleeping when the world locks down: correlates of sleep health during the COVID-19 pandemic across 59 countries. Sleep Health. 2021;7(2):134–42.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Diniz TA, Christofaro D, Tebar WR, Cucato GG, Botero JP, Correia MA, et al. Reduction of physical activity levels during the COVID-19 pandemic might negatively disturb sleep pattern. Front Psychol. 2020;11:586157.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Pizzonia KL, Koscinski B, Suhr JA, Accorso C, Allan DM, Allan NP. Insomnia during the COVID-19 pandemic: the role of depression and COVID-19-related risk factors. Cogn Behav Ther. 2021;50(3):246–60.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Saalwirth C, Leipold B. Well-being and sleep in stressful times of the COVID-19 pandemic: Relations to worrying and different coping strategies. Stress Health. 2021;37(5):973–85.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Simonelli G, Petit D, Delage JP, Michaud X, Lavoie MD, Morin CM, et al. Sleep in times of crises: a scoping review in the early days of the COVID-19 crisis. Sleep Med Rev. 2021;60:101545.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Brown RS, Lees-Haley PR. Fear of future illness, chemical AIDS, and cancerphobia: a review. Psychol Rep. 1991;71(1):187–207.

    Article  Google Scholar 

  19. Bish A, Michie S. Demographic and attitudinal determinants of protective behaviours during a pandemic: a review. Br J Health Psycho. 2020;15(4):797–824.

    Article  Google Scholar 

  20. Goodwin R, Gaines SO, Myers L, et al. Initial psychological responses to swine flu. Int J Behav Med. 2011;18:88–92.

    Article  PubMed  Google Scholar 

  21. Cheng C. To be paranoid is the standard? Panic responses to SARS outbreak in the Hong Kong Special Administrative Region. Asian Perspect. 2004;28(1):67–98 (

    Article  Google Scholar 

  22. Sloan MM, Haner M, Graham A, Cullen FT, Pickett J, Jonson CL. Pandemic emotions: the extent, correlates, and mental health consequences of personal and altruistic fear of COVID-19. Sociol Spectr. 2021;41(5):369–86.

    Article  Google Scholar 

  23. Yıldırım M, Arslan G, Özaslan A. Perceived risk and mental health problems among healthcare professionals during COVID-19 pandemic: exploring the mediating effects of resilience and coronavirus fear. Int Mental Health Addict. 2020;20(2):1035–45.

    Article  Google Scholar 

  24. Yin Q, Chen A, Song X, Deng G, Dong W. Risk perception and PTSD symptoms of medical staff combating against COVID-19: a PLS structural equation model. Front Psychiatry. 2021;12:39.

    Google Scholar 

  25. Lee J, Lee HJ, Hong Y, Shin YW, Chung S, Park J. Risk perception, unhealthy behavior, and anxiety due to viral epidemic among healthcare workers: the relationships with depressive and insomnia symptoms during COVID-19. Front Psychiatry. 2021;12:358.

    Google Scholar 

  26. Rossi R, Socci V, Talevi D, Mensi S, Niolu C, Pacitti F, et al. COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. Front Psychiatry. 2020;11:790.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Blanc J, Seixas A, Bubu O, Briggs A, Compas AC Jr, Williams Y, et al. COVID-19 risk perception, sleep health and peritraumatic distress among New Yorkers: The NYU COVID-19 mental health study. Sleep. 2021;44:A278.

    Article  Google Scholar 

  28. Walsh JK, Hartman PG, Kowall JP. Insomnia. In: Chokronerty S, editor. Sleep disorders medicine: basic science, technical considerations, and clinical aspects. Amsterdam: Elsevier Health Sciences; 1994. p. 219–39.

    Chapter  Google Scholar 

  29. Salfi F, Lauriola M, D’Atri A, Amicucci G, Viselli L, Tempesta D, et al. Demographic, psychological, chronobiological, and work-related predictors of sleep disturbances during the COVID-19 lockdown in Italy. Sci Rep. 2021;11:11416.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Abdalla M, Chiuzan C, Shang Y, Ko G, Diaz F, Shaw K, et al. Factors associated with insomnia symptoms in a longitudinal study among New York City healthcare workers during the COVID-19 pandemic. Int J Environ Res Pub Health. 2021;18(17):8970.

    Article  CAS  Google Scholar 

  31. Morin CM, Bjorvatn B, Chung F, Holzinger B, Partinen M, Penzel T, et al. Insomnia, anxiety, and depression during the COVID-19 pandemic: an international collaborative study. Sleep Med. 2021;87:38–45.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pilcher JJ, Dorsey LL, Galloway SM, Erikson DN. Social isolation and sleep: manifestation during COVID-19 quarantines. Front Psychol. 2022;12:810763.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Jahrami HA, Alhaj OA, Humood AM, Alenezi AF, Fekih-Romdhane F, AlRasheed MM, et al. Sleep disturbances during the COVID-19 pandemic: a systematic review, meta-analysis, and meta-regression. Sleep Med Rev. 2022;62:101591.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gerosa T, Respi C. Ad hoc module 1: ITA.LI COVID-19. Brief report. Institute for advanced study of social change (IASSC). 2022. Accessed 09 Feb 2022.

  35. Ohayon MM. Epidemiology of insomnia: what we know and what we still need to learn. Sleep Med Rev. 2002;6(2):97–111.

    Article  PubMed  Google Scholar 

  36. Hahn E, Gottschling J, Spinath FM. Short measurements of personality–validity and reliability of the GSOEP Big Five Inventory (BFI-S). J Res Pers. 2012;46(3):355–9.

    Article  Google Scholar 

  37. Kesavayuth D, Poyago-Theotoky J, Binh Tran D, Zikos V. Locus of control, health and healthcare utilization. Econ Model. 2020;86:227–38.

    Article  Google Scholar 

  38. Masten AS. Ordinary magic: resilience processes in development. Am Psychol. 2001;56(3):227–38.

    Article  CAS  PubMed  Google Scholar 

  39. Werner EE. Risk, resilience, and recovery: perspectives from the Kauai Longitudinal Study. Dev Psychopathol. 1993;5(4):503–15.

    Article  Google Scholar 

  40. Roitblat Y, Burger J, Leit A, Nehuliaieva L, AMHSI Research Team, Milken Research Team, et al. Stay-at-home circumstances do not produce sleep disorders: an international survey during the COVID-19 pandemic. J Psychosom Res. 2020;139:110282.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Salfi F, Lauriola M, Amicucci G, Corigliano D, Viselli L, Tempesta D, et al. Gender-related time course of sleep disturbances and psychological symptoms during the COVID-19 lockdown: a longitudinal study on the Italian population. Neurobiol Stress. 2020;13:1–6.

    Article  CAS  Google Scholar 

  42. Casagrande M, Favieri F, Tambelli R, Forte G. The enemy who sealed the world: effects quarantine due to the COVID-19 on sleep quality, anxiety, and psychological distress in the Italian population. Sleep Med. 2020;75:12–20.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lucchini M, Gerosa T, Pancheva M, Pisati M, Respi C, Riva E. Differential effects of COVID-19 and containment measures on mental health: evidence from ITA.LI-Italian Lives, the Italian household panel. PLoS One. 2021;16.11:e0259989.

    Article  CAS  Google Scholar 

  44. Shanahan L, Steinhoff A, Bechtiger L, Murray AL, Nivette A, Hepp U, et al. Emotional distress in young adults during the COVID-19 pandemic: evidence of risk and resilience from a longitudinal cohort study. Psychol Med. 2020.

  45. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power. Life course epidemiology. J Epidemiol Com Health. 2003;57.10:778–83.

    Article  Google Scholar 

  46. Lauderdale DS, Knutson KL, Yan LL, Liu K, Rathouz PJ. Sleep duration: how well do selfreports reflect objective measures? The CARDIA Sleep Study Epidemiol. 2008;19(6):838–45.

    Google Scholar 

  47. Respi C, Gerosa T, Survey participation and non-response error in a pandemic scenario. Results from the ITA. LI Covid-19 study. Rass Ital Sociol. 2021;62.1:39–65.

    Article  Google Scholar 

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The ITA.LI – Italian Lives project is funded by the Italian Ministry of Education, Universities and Research under the “Departments of Excellence 2018–2022” initiative (Italian Law 232 of 11 December 2016) ( The internal grant number at the Department of Sociology and Social Research of the University of Milan-Bicocca is 2018-NAZ-0116. The award was received by the Department of Sociology and Social Research of the University of Milan-Bicocca. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

This study also benefits from research grant R01AG071649-01 from National Institutes of Health (NIH).

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Conceptualization: Egidio Riva. Data Curation: Mario Lucchini and Tiziano Gerosa. Formal analysis: Mario Lucchini, Tiziano Gerosa and Egidio Riva. Methodology: Mario Lucchini, Tiziano Gerosa and Marco Terraneo. Supervision: Mario Lucchini. Writing – original draft: Egidio Riva and Marco Terraneo. The author(s) read and approved the final manuscript.

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Correspondence to Egidio Riva.

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All procedures and methods performed in this study were in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethics approval for the data collection in the ITA.LI – Italian Lives project was obtained from the Ethics Committee of the University of Milano-Bicocca (0042665/19). Written informed consent and assent were obtained from all family members as mandatory requirements to participate in the ITA.LI – Italian Lives project.

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Supplementary Information

Additional file 1:

Table A1. Random-effects ordered logistic models: parameter estimates, cut points and panel-level variance component. Table A2. Average predicted probabilities for insomnia-related issues at different policy phases and age groups.

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Riva, E., Terraneo, M., Lucchini, M. et al. The prevalence of insomnia in different COVID-19 policy phases: Longitudinal evidence from ITA.LI – Italian Lives. BMC Public Health 22, 1657 (2022).

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