Impact of COVID-19 First Wave on Psychological and Psychosocial Dimensions: A Systematic Review

Abstract

A systematic review of the literature investigating the effects of the COVID-19 pandemic on psychological and psychosocial factors was completed. Published literature was examined using electronic databases to search psychosocial factors such as beliefs and media persuasion, social support, coping, risk perception, and compliance and social distancing; and psychological factors as anxiety, stress, depression, and other consequences of COVID-19 that impacted mental health among the pandemic. A total of 294 papers referring to the first wave of the COVID-19 pandemic (December 2019–June 2020) were selected for the review. The findings suggested a general deterioration of mental health, delineating a sort of “psychological COVID-19 syndrome”, characterized by increased anxiety, stress, and depression, and decreased well-being and sleep quality. The COVID-19 effect on the psychological dimensions of interest was not the same for everyone. Indeed, some socio-demographic variables exacerbated mental health repercussions that occurred due to the pandemic. In particular, healthcare workers and young women (especially those in postpartum condition) with low income and low levels of education have been shown to be the least resilient to the consequences of the pandemic.

1. Introduction

As of 21 February 2022, SARS-CoV-2 has infected more than 423 million individuals worldwide and caused more than 5.8 million deaths [1]. During the first months of 2020, the globe was under lockdown: the confinement measures which constituted an emergency protocol imposing restrictions on the free movement of persons [2]. These restrictive measures had various effects on people’s daily life, and physical and mental health. This review arose from the need to summarize and schematize the many psychological publications related to the first wave of COVID-19, as they appear to be very large. The authors aim to provide a complete review of the literature on the psychological impact that the first wave (December 2019–June 2020) of COVID-19 had on a global level, to identify which factors related to the pandemic were most impacting people’s mental health.

The results of the review regard the first wave of the pandemic (2019–2020). Therefore, this impact is unrepeatable in the future, also regarding resilience and transformative resilience. The findings could indicate a specific structure that could be attributed to a “psychological COVID-19 syndrome”, characterized by symptoms relatable to anxiety, depression, stress, less wellbeing and more sleep problems.

We chose to analyze different variables of mental health studied in previous pandemics (Ebola, SARS, MERS, Novel influenza A, Equine influenza, etc.), in addition to psychosocial dimensions which proved to be essential to mitigate the spread of Coronavirus [3,4,5,6,7,8,9,10]. The authors decided to build on the dimensions considered in previous pandemics to observe whether these variables were also present in the current COVID-19 pandemic. For easy reading of the review, we divided the work into two sections: (1) Psychosocial Variables, and (2) Observables related with Mental Health.

For this systematic review, 294 articles published between December 2019 and June 2020 were analyzed, resulting in a large, heterogeneous literature, with a total sample of 732,852 subjects from more than 30 countries. Although the collected works are often based on non-representative samples, their large number and the possibility of cross-referencing the results common to these studies allows on the one hand to overcome the possible problem of unrepresentativeness, and on the other to grasp the existence of an effect, assuming the heterogeneity of the samples used by these works.

The authors hypothesize that from this systematic review can be derived the psychosocial and psychopathological areas most affected by the COVID-19 pandemic, and likewise, that can be delineated a profile of the most vulnerable individual to these types of issues. So, in this perspective, this review can be useful both for future research and for the current management of the pandemic emergency.

2. Review Methodology

In this section, details about the systematic review approach are provided. We relied on an adapted version of the systematic qualitative review approach by Higgins and colleagues [11]. The authors searched all the papers that included the relationship between COVID-19 and psychological variables of interest.

As a first step, we asked academic information specialists to search for COVID-19 scientific papers that fulfilled our inclusion criteria. These were: written in English language; published between December, 2019 and June, 2020; being an empirical study, project report or review; published in a scholarly peer-reviewed journal and/or conference proceedings; those related to psychological dimensions and mental health (i.e., psychological disorders, risk perception, beliefs, coping, compliance, social support).

The specialists completed their task consulting the databases of PsycInfo, PsycArticles, PubMed, Science Direct, PsyArXiv, NCBI, medRxiv, and Elsevier repository. The authors on their part contributed to the search by consulting Google and Google Scholar to increase the chances of identifying the widest range of sources possible. The consultation took place between April and May 2020. A total of 7381 sources were considered. Subsequently, the authors’ results were compared with those of the experts, and duplicates were removed. To select the papers, search terms such as “COVID-19”, “Psychology”, “Psychological”, “Psychological effect”, “Mental health” were included in the research. For a more complete list of all the search terms, see Appendix A. Based on the inclusion criteria, only 480 sources were accessed as full-text: 303 papers were eligible since they met the inclusion criteria.

At the title review stage, sources were most commonly rejected because they fulfilled two or more of the following exclusion criteria: were only citations, commentary, or books; were papers published before 2019; those not related to the psychological dimensions and mental health (i.e., psychological disorders, risk perception, beliefs, coping, compliance, social support); articles not in English language. At the abstract and full paper review stages, papers were most commonly rejected for: demonstrating no inclusion of a psychological variable, having incomplete results, using qualitative measures and articles where data analysis was not suitable for the systematic review process (e.g., lack of descriptive statistics, no correlation coefficients provided for the variables of interest).

A flow diagram of the systematic search is shown in Figure 1.

This review is divided into two main sections: (1) Psychosocial Variables, and (2) Observables related to Mental Health. The first section is in turn divided into different subsections: Beliefs and Media Persuasion, Social Support, Coping, Risk Perception, and Compliance and Social Distancing. The second section is in turn divided into different subsections: Anxiety, Stress, Depression, and Other consequences of COVID-19 on Mental Health. These are the dimensions that emerged most during the analysis of the literature about COVID-19. The subdivision in these paragraphs was formulated to make reading easier. Every subsection is divided into “Introduction ”, “Measures”, and “Results”.

Postponed to the “Discussion” paragraph is the possibility of connecting in a single design all the results emerging from the various sections, towards a definition of a psychological COVID-19 syndrome. For an easier reading, you can consult

Table A1. Main characteristics of the studies reviewed: reference, country, sample size, impacted dimension, and main findings (n = 294). Main characteristics of the studies reviewed.

Ref	Country	Sample Size	Impacted Dimension	Main Findings
[136]	Germany	419	Compliance	People use protective measures primarily to protect themselves and only secondarily to protect others. Social distancing and washing hands are the most implemented.
[135]	USA	895	Compliance	Those with higher levels of boredom perceived social distancing as more difficult and adhered less.
[158]	Ireland	500	Compliance	Evidence to support communications that inform people about recommended behaviors.
[19]	USA	1022	Beliefs/	Up-to-date and accurate specific health information Media Persuasion and special precautionary measures have been associated with a lower psychological impact.
[178]	MTurk	324	Compliance	“COVID-19 fear” is a predictor of a positive change in behavior (social distancing, better hand hygiene).
[67]	China	144	Anxiety, Depression,	Presence of depressive and anxiety symptoms Social Support in patients with COVID-19. Lower perceived social support appears to be related to an increase of the symptoms.
[137]	Brazil, Colombia,	2285	Compliance	Preventive measures are perceived as Germany, Israel, more effective when control levels are Norway, USA higher and the perceived risk is lower.
[21]	MTurk	2176	Beliefs/	self-interested framing isn’t more Media Persuasion effective than prosocial framing.
[105]	USA	1591	Risk	Subjects demonstrated a growing awareness Perception of the risk and reported engaging in protective behaviors with increasing frequency.
[76]	China	4607	Risk	People with low self-control are more Perception vulnerable and more in need of psychological help to maintain mental health.
[34]	China	6910	Beliefs/	Although attitudes towards COVID-19 were optimistic, Media Persuasion most residents took precautions to prevent infection.
[134]	MTurk	525	Compliance	Those who perceive COVID-19 as a serious threat and those who have greater faith in science are more likely to act in accordance with guidelines.
[145]	Italy	894	Compliance	Respondents were more likely to reduce their self-isolation if the quarantine extension turned out to be longer than they expected.
[282]	China	2091	Stress	The prevalence of post-traumatic stress symptoms in China one month after the outbreak was 4.6%.
[43]	Poland, UK	652	Beliefs/	The prejudice towards foreign nationalities was Media Persuasion sensitive to the epidemiological situation.
[155]	China	4607	Compliance	Emotional and behavioral reactions were slightly influenced by the outbreak of COVID-19.
[163]	Germany,	2192	Compliance	Inducing empathy for those most vulnerable to the virus UK, USA promotes motivation to adhere to social distancing.
[304]	China	52,730	Other Consequences	Over time, levels of distress have significantly decreased. It can be partly attributed to the effective prevention and control measures taken by the Chinese government.
[42]	USA	1709	Beliefs/	Those who are least likely to rely on their Media Persuasion intuitions and who have lower basic scientific knowledge were the worst at discerning fake news.
[367]	Holland	439	Other Consequences	Four predictors of "COVID-19 fear" were found: intolerance to uncertainty, health anxiety, increased media exposure, and risks to loved ones.
[130]	Italy	1573	Risk perception	In line with international literature, for the Italian population, too, experimental data confirmed a decrease in risk propensity of around 17.4% during the lockdown.

(placed in the Appendix A) that contains the main information of the papers analyzed.

3. Psychosocial Variables: Beliefs and Media Persuasion

3.1. Introduction

Probably the most important factor determining the resilience of a human community exposed to a pandemic is the readiness to change habits in order to face the diffusion of the virus [12,13,14,15]. Of course, the role of information spreading about the pandemic was a fundamental factor to promote such a change, even if the impact of the communication style, source of information and other elements should also be taken into consideration in order to maximize the impact of the media on the people’s beliefs and behaviours [12,13,14,15].

In particular, literature reports studies examining why some people believe in conspiracy theories about COVID-19, and why they choose to not follow the rules/laws given by the government to prevent contagion. Uncertainty and ambiguity regarding the information about COVID-19 could lead to a lack of trust in governmental regulations, bringing people to respond with adaptive or non-adaptive coping strategies [16]. Some studies investigated how people were influenced by the media (both traditional and new media), and how disinformation may be enhanced by the possibility offered by the new media to share an opinion about medical topics without the proper knowledge, widely spreading conspiracy theories [17]. Clearly, the distrust in science and in the medical system could increase the risks [18].

3.2. Measures

Table 1. Validated tools to measure “Beliefs” and “Media persuasion”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Knowledge, Attitude and perception questionnaire [25]	0.70	1
Strengths and difficulties questionnaire (SDQ) [26]	0.77	1
Beliefs in conspiracy theory inventory (BCTI) [27]	0.78	1
Generalised conspiracy beliefs scale (GCBS) [28]	0.90	1
Core Beliefs Inventory (CBI) [29]	0.92	1
Cognitive Reflection Test (CRT) [30]	0.69	2
Vaccination intention [31]	0.79 to 0.94	1
Conspiracy Mentality Questionnaire (CMQ) [32]	0.84	2
Attitude toward vaccination [33]	0.83	1

shows the validated measures used to analyze “Beliefs” and “Media persuasion”. About “Media persuasion”, all articles [17,19,20,21,22,23,24] used ad-hoc measures.

3.3. Result for Beliefs and Media Persuasion

The results of the articles reviewed on beliefs and media persuasion were distinguished by the authors into three main categories considering three fundamental moderators: attitudes, scholarship and scientific knowledge, finding news on social networks.

Attitudes: From our bibliographic research, we found that even if attitudes towards COVID-19 were optimistic (e.g., the likelihood of being infected was perceived as low), most of the participants took precautionary measures to prevent infection: 96.4% of the participants avoided crowded places and 98% wore masks [34]. In particular, in young adults an optimistic attitude was associated with less psychological distress; also the diffusion of accurate information and a mental health intervention could lead to better attitudes [35]. On the contrary, higher levels of fatalism toward COVID-19 were associated with lower behavioral intentions to support mitigation efforts [36]; also those who believed that the fear of COVID-19 caused unnecessary absences from work and schools, reported higher levels of anxiety [37]. Opposed to these results, Shiina and colleagues [23] found lower anxiety levels. Interestingly, those whose core beliefs (beliefs that guide individuals in their identity) were less violated by the pandemic reported to engage more in social isolation measures [38]. Other results showed that beliefs in myths about illness prevention (e.g., smokers are not susceptible to COVID-19) were associated with behaviors like sanification, clothes disinfection and handwashing [38,39].

Scholarship and scientific knowledge: The most important predictor of COVID-19 knowledge was the level of education [40]. The knowledge resulted in an important predictor for degree of information search behavior, to discern between true and false information, comply with the preventive measures, show less panic reactions, and adopt more behaviors able to reduce infection risk [41]. Moreover, they also assessed the crisis as the worst epidemic crisis, less adopting “unreasonable” behaviors (e.g., hoarding toilet papers or medical equipment, spreading misinformation), being characterized by lower levels of anxiety, and greater risk perception [25,41,42].

Finding news on social networks: Because of the large use of social media as a source of information, these have had a significant impact on the spread of panic and fear about COVID-19. A research showed that participants found information through electronic media and TV ($85.5\%$), social media ($81.3\%$), family members ($69.0\%$) and friends ($68.7\%$). Media communications influenced attitudes founding a positive relationship between exposure to media and prejudice towards four different nationalities (i.e., Chinese, Italian, Hungarian and Mongolian populations), while at the same, time media exposure has been associated with lower anxiety levels [16,17,24,43].

Despite the research, in general, proving that people tend to adopt preventive behaviors to protect others rather than themselves [21], knowledge and beliefs about COVID-19 and preventive behaviors were also affected by age, gender, education, culture and conspiracy theories.

Age: Age affected both knowledge about COVID-19 and preventive behaviors, with the younger people resulting in more acknowledged and adopting preventive behaviours than elders [24].

Gender: Females showed higher levels of worry, anxiety, fear and sadness, about COVID-19 infection regarding family members and friends, than men. Men, on the other hand, were more concerned about the national and international impact of COVID-19 on money and work [44].

Education: Different effects mediated by social condition were found. In particular, lower levels of education were associated with more fear of dying by COVID-19, perception of susceptibility to the infection and worse overall knowledge about the pandemic. Conspiracy theories were widely believed even among highly educated individuals, predicting science mistrust, unwillingness to adhere to public health measures and increased psychological distress [24,25,45,46,47].

Culture: Culture has proved to be another important variable. Those who claimed to be more religious and more politically rightist were more likely to believe in COVID-19 conspiracies [48]. More specifically, horizontal collectivism (viewing the self as part of a collective that emphasizes equality) was associated with more accurate covid-related beliefs, with respect to vertical individualism (viewing the self as an autonomous individual who accepts inequality) [47,49].

Conspiracy theories: With regard to conspiracy theories, some studies show that subjects who believed that COVID-19 was a hoax complied less with the suggested behaviour to reduce and contain the infection. Moreover, it was seen that these subjects engaged more in self-centred behaviour aimed at personal benefit during the crisis (e.g., hoarding everyday goods, relying on “alternative” sources of information) than in reducing the infection rate (e.g., increased hygiene behavior, keeping physical distance to others) [18]. It has been seen that a high level of collective narcissism (inflated belief in the greatness of one’s ingroup) is related to a greater agreement with conspiracy theories [50]. In addition, those who support conspiracy theories may be less likely to get vaccinated if this will be available [51,52]. With regard to the sharing of conspiracy theories related to COVID-19, it was seen that individuals with high levels of Social Dominance Orientation and low levels of Traditionalism were more likely to spread them, but were less likely to share misinformation about the severity and spread of COVID-19 [53]. There are socio-demographic differences: the more religious and/or politically right-wing subjects were inclined to believe in conspiracy theories [48]. In conclusion, the convictions of conspiracy theories related to COVID-19 were strongly correlated with convictions of broader conspiracy theories and a lower level of education, and weakly correlated with more negative attitudes towards the government [46].

Finally, even the communication style affected the attitudes and behaviours of people around the world. Research about media persuasion found that fear messages create a stronger emotional reaction than the prosocial message, but the prosocial message was more effective in increasing the desire for self-isolation if it produces a strong emotional response. Both fear and prosocial messages were equally effective in stimulating a willingness to engage in preventive behaviors [20]. In conclusion, situational awareness (a construct based on SAT—Situational Awareness Theory; a sort of perceived understanding) influenced social distancing. The sources of information, formal and informal, were found to be significantly correlated with situational awareness [22], in particular public health officials or high-power political figures may be more persuasive [19].

Table 2. Beliefs and Media Persuasion. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. *: “MTurk” is used to indicate a sample extended worldwide.

Beliefs and Media Persuasions
Valid papers	36
Sample size	50,090
Geographical regions	Arabic Emirates, Bangladesh, China, Cyprus, Europe, France, Greece, Japanese, Kurdi, Malaysia, MTurk *, Polonia, Serbia, South America, South Korea, Turkish, UK, USA
Mediator variables	Age, Gender, Education and Culture

shows information about the papers analyzed for the study of Beliefs and Media persuasion.

4. Psychosocial Variables: Social Support

4.1. Introduction

Current stay-at-home orders, and the COVID-19 pandemic in general, had negative social and economic consequences, for example, many social support networks have been disrupted [54], causing psychological morbidities and fatigue [55,56]. According to Skalski and colleagues [57], “Social support refers to the so-called social network and characterizes the functioning of individuals among other people”. In recent studies, social support emerged as able to dampen the pandemic outcomes, due to its negative correlation with anxiety, mental discomfort and psychological pressure [57,58,59]. Moreover, peer and community support groups appeared as a protective factor for psychological distress [59]. Prosocial communication during COVID-19 (i.e., prosocial language) is also important in protecting people’s well-being, since it appeared to promote “positive” emotions, like hope and joy [20,57], and could maintain health status [57].

4.2. Measures

Table 3. Validated tools to measure “Social support”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Perceived Social Support Scale (PSSS) [60]	0.88 to 0.90	9
Social Support Rating Scale (SSRS) [61]	0.81	3
Perceived Social Support Questionnaire (F-SozU K-6) [62]	0.90	1
Social Provisions Scale (SPS) [63]	0.85 to 0.92	1
COVID-19 Prosocial Experiences [64]	0.56 to 0.71	1

shows the validated measures used to analyze “Social support”.

Lastly, a few articles used scales created ad hoc for the research [20,58,65,66].

4.3. Results for Social Support

The results of the articles reviewed regarding social support during COVID-19 emergency were analyzed. From these results, people seemed to benefit during the first wave of the pandemic from real-life and online social support [20,24,54,55,56,57,58,59,64,65,66,67,68,69,70,71]. For example, receiving COVID-19 prosocial acts (e.g., providing support to people concerned about the pandemic, making donations to charities or people in need, grocery shopping for individuals at risk, helping with household chores, and giving gifts) by others reduced anxiety symptoms [64], and increased belongingness and community attachment [64]. Social support also seemed very important for those people involved directly in fighting against the pandemic. For instance, for medical staff, a good level of social support was associated with self-efficacy and sleep quality, and negatively correlated with anxiety, stress and depression [65,70].

Social support appeared able to affect people’s compliance, although with some mixed results [56,66]. Indeed, people actively sought support and social connections to cope with COVID-19 changes and maintain higher levels of compliance with quarantine [56]. However, social support did not seem to affect compliance towards other behavioral recommendations like mask-wearing, handwashing and social distancing [66].

Not only was obtaining support from one’s social network important for people’s well-being, but also engaging in COVID-19 prosocial behaviors (i.e., actively providing support to others) was positively associated with mental health, social responsibility values, and burdensomeness (i.e., unwelcome burdensome difficulty) [64].

In the selected works we found several factors able to affect people’s social support during the pandemic that will be described briefly below, while cultural differences in social support levels were not supported [66].

Age: According to Alvis and colleagues [64], adolescents most frequently endorsed giving support to friends, neighbors, or relatives who were worried about COVID-19 [64].

Gender: In accordance with Ma and colleagues [54], males perceived significantly less social support than females [54]. Meanwhile, other studies did not find any significant gender differences [58,59].

Social Condition: According to Tull and colleagues [56], income level was positively related with perceived social support.

Comorbidity: Social support was negatively correlated with anxiety [54,55,57,58,67], coronavirus anxiety [57], depression [24,55,67], distress [71], sleep problems [69], and stress [68].

Table 4. Social support. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Social Support
Valid papers	16
Sample size	69,769
Geographical regions	China, France, New Zealand, Polonia, UK, USA
Mediator variables	Age, Gender, Social Condition and Comorbidity

shows information about the papers analyzed for the study of Social support.

5. Psychosocial Variables: Coping

5.1. Introduction

In the scientific literature, one of the most important factors that can buffer the effects of a stressful situation, such as COVID-19, is the coping that people implement to deal with new difficulties [72,73]. Coping refers to the effort to solve personal and interpersonal problems in order to master, reduce or tolerate stress and conflicts [71,74], or in other words, “the thoughts and actions that individuals use to deal with stressful events” [72]. Two general coping strategies are traditionally identified by the literature: (i) problem-focused coping, which concerns solving the problem or taking action to change the situation (active coping, planning, and use of instrumental support), (ii) emotion-focused coping, which aims to reduce the emotional distress (use of emotional support, acceptance, positive reframing, religion, humor, substance use, self-distraction, self-blame, denial, behavior disengagement, and venting) [72,74]. The scientific community also distinguished between coping with a positive or negative outcome [71]. Negative coping strategies are considered primary risk factors, and are associated with depression, anxiety and stress [55,73,75]. Conversely, positive and adaptive coping styles can protect individuals’ mental health [75,76,77], and enhance their capability to deal with challenges [76,78] such as those related to COVID-19 [78,79].

5.2. Measures

Table 5. Validated tools to measure “Coping”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Values in Action Inventory of Strengths-120 (VIA-IS-120) [80]	0.58 to 0.90	1
Brief-COPE [81]	0.53 to 0.82	6
Coping Orientation to the Problems Experienced (COPE-NVI-25) [82]	0.62 to 0.92	2
Strengths and difficulties questionnaire (SDQ) [26]	0.64 to 0.70	1
Coping Strategy Questionnaire (CSQ) [83]	0.91 to 0.96	1
Simplified Coping Style Questionnaire (SCSQ) [84]	0.84	4
Brief Self-Control Scale (BSCS) [85]	0.84	1

shows the validated measures used to analyze “Coping”.

Several other research used ad hoc questionnaires to analyze coping [86,87,88,89,90,91].

5.3. Results for Coping

In general, positive coping strategies (e.g., active coping, use of emotional support, humor, mindfulness) appeared related to lower levels of psychological distress in the pandemic scenario [71,72,79] and higher well-being [77,91]. Specifically, high self-control perception, acceptance, behavioral activation, and values-based action strategies resulted in lower levels of fear, restlessness, trouble relaxing, and general vulnerability [76,88]. Moreover, Meaning in Negative Experiences (i.e., the general positive beliefs about negative experiences and the tendency to actively reflect on their meaning or value), was associated with a higher risk perception that may help people in dealing with anti-covid behaviors more successfully [92].

Nonetheless, the outcomes of problem-oriented coping strategies appeared non-definitive. Indeed, problem-oriented coping strategies were found to be associated with higher levels of fear, anger, and psychological distress [72,79]. Negative (emotional-focused) coping was associated with high psychological distress [71], anger [72], and perceived stress [55,74]. Negative coping strategies, like excessive cleaning, reassurance seeking, and excessive checking, were associated with higher levels of irritability and fear [88].

In two recent works [8,93], the most commonly used strategies for coping were: use of emotional support, planning, accepting the situation, learning to start living again, and religious/spiritual strategies. On the contrary, the most rarely used strategies were: substance use, denial of the fact of a difficult situation, behavioral disengagement, and self-blaming for the situation [93]. Those with suspected infection rarely used any coping style to deal with stress, but spent more time searching for information about COVID-19 and received less social support [71].

Coping strategies used during the pandemic were associated with age, gender, socio-demographic, and psychological variables. The results are shown below.

Age: Younger participants reported to use more negative coping strategies like substance use, behavioral disengagement, or “dark” humor [73,75,89].

Gender: Females tended to use more strategies focused on emotions, such as distraction, emotional and religious support and less use of humor [89], but also problem-focused coping strategies [72].

Socio-demographic variables: Students spent more time on social media as a coping strategy [86] and employed more behavioral coping strategies [94]. As for healthcare workers, the most used coping strategies were: religious strategies [8,90], acceptance [8,87], coping planning [8], physical activity [90], talk therapy [90], virtual support groups [90], and positive framing [87].

Comorbidity: Anxiety was negatively associated with total coping strategies [94], especially with problem-focused coping strategies [72].

Table 6. Coping. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Coping
Valid papers	21
Sample size	37,330
Geographical regions	China, Jordan, MTurk, Pakistan, Poland, USA
Mediator variables	Age, Gender, Education and Comorbidity

shows information about the papers analyzed for the study of coping.

6. Psychosocial Variables: Risk Perception

6.1. Introduction

Risk perception is defined as one’s assessment of hazardous objects or activities [95,96,97] and it is composed of perceived susceptibility and severity [98,99]. During the COVID-19 research, the first referred to beliefs about the possibility of contracting the disease, while the second referred to beliefs about the severity of the disease [98,99]. It is important to remember that risk perception is based on personal judgements, and not on real features of the risk [25]; for this reason, risk perception increased with dread, lack of control and unknown risk [100]. Results reported that the greater the perceived risk by an individual, the greater the motivation to adopt protective behaviors (e.g., hand washing, avoiding hand shaking, maintaining social distancing) [40,48,95,98,101,102,103,104,105,106,107]. As claimed by the Extended Parallel Process Model [108], it is not significant to have a high or low level of risk perception, but it is necessary to have an optimal perception (supported by self-efficacy, susceptibility, severity, and response efficacy), such as to trigger compliance with the rules.

Unfortunately, the perception of personal risk and threat appeared as not sufficient to trigger enough protective behavior during the COVID-19 pandemic: if the only determinant factor of protective behavior was the perception of the threat, only a few individuals were persuaded and consequently compliant [102]. The risk perception of being infected by COVID-19 was also affected by the type of information that the individual received by the media [25,109,110], by the lack of trust in official guidelines [16], and by the frequent exposure to information [111]. A good communication of information about the pandemic resulted in better preventive behaviors [101], because the severity and the perceived risk of getting infected by COVID-19 appeared as inducing belief in conspiracy theories [39,48]. These irrational beliefs provided a defense against external and unknown events, like the COVID-19 pandemic, and they were associated with negative health behaviors [39]. Indeed, conspiracy theories could persuade people to not follow preventive measures; in this case, a higher risk perception appeared to remove this effect [103].

Finally, socioeconomic and demographic variables (e.g., living alone, living with children), living in high risk areas, and contact with individuals infected by the virus, appeared to modify people’s risk perception [112,113]. Risk perception during the COVID-19 first wave was also positively associated with psychosocial distress [111], anxiety [37,40], depression [37] and PTSD [37].

6.2. Measures

Risk perception was investigated mostly with ad hoc measures [16,25,37,39,40,48,76,78,79,92,95,96,98,99,100,101,102,103,104,105,106,107,109,110,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129].

Only two studies [111,130] used already validated measures to assess risk perception (

Table 7. Validated tools to measure “Risk perception”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Gainforth’s perceived vulnerability scale [131]	0.95	1
General Risk Propensity Scale (GRiPS) [132]	0.92	1

).

6.3. Results for Risk Perception

Although COVID-19-related risk perception increased from March to April 2020, many people underestimated the risk of the disease [16,39,99,124]. A lower risk perception may lead to a lower level of compliance with health-protective behaviors [37,39,95,102,107,122,124,126], like hand-washing [105,122], social distancing [25,95,105,116,122] and mask-wearing [25,120].

According to Globis and colleagues, and Marotta and colleagues [119,130], during the lockdown period in the USA and Italy, people reported a lower perception of COVID-19 infection risk, probably due to the government’s imposition of social distancing [119,130]. A greater knowledge about the prevention and transmissibility of COVID-19 was associated with lower levels of perceived risk [129]. Conservatorism tended to be associated with lower risk perception as well [128]. Risk perception could also be influenced but in a different direction (i.e., positively) by media accuracy [128], exposure to COVID-19 news [111], using social media [109], and collective orientation [118]. Belief in conspiracy theories was also associated with greater risk perception of contamination [48,103].

Concerning emotional states, risk perception influenced negative affect [96] and emotionality personality trait (fearfulness, anxiety, dependence, sentimentality), measured by HEXACO, revealed a significant positive effect on risk perception [117,123].

Contrary to expectations, risk perception of COVID-19 was not influenced by the existence of previous pathologies [79]. Risk perception levels appeared to play a role in the coping strategy that individuals used to deal with the pandemic. Perceived high threat was linked to a higher level of meaning in negative experiences (MINE) [92], while low risk perception was associated with a higher use of COVID-19 humor [115].

Finally, a strong predictor of risk perception was the work: healthcare workers showed higher risk perception than other participants [78,127], especially those who have longer shifts [112].

Risk perception during the pandemic was associated with age, gender, culture, and psychological variables. The results are shown below.

Age: Age was found to be an important predictor of risk perception [40,113]. However, the data showed conflicting results. Some research showed that younger people report higher perceived risk [107,112]. On the contrary, other studies found that the likelihood to feel threatened increased significantly with age, in other words, older adults perceived more risk of COVID-19 than younger people [16,114,117,120].

Gender: Women reported higher levels of risk perception compared to men [104,107,113,114,117,127]. Moreover, by analyzing the interaction effect between gender and age, Iorfa and colleagues [121] found that older men reported a higher risk perception than younger men [121].

Culture: The levels of risk perception reported in different parts of the world, apart from possible cultural factors, appeared to be affected by the levels of contagion. For instance, people residing in Europe reported lower levels of perceived threat than people residing in North-America [117]. Participants that lived in central and northern Vietnam reported a lower risk perception of COVID-19 than those who lived in southern Vietnam (that is also preferred by Chinese tourists) [109]. This trend is also detectable within the same country if the contagion was not homogeneous. For instance, people living in northern Italy reported more preoccupation about risk of infection than those who live in the center and south Italy [78,127].

Comorbidity: The perceived severity of COVID-19 was positively associated with mental health problems [76,125]. Higher levels of risk perception of COVID-19 were associated with depression [96,129,133], anxiety [37,96,106,110,118], and stress [118,127,133], but also with death anxiety [127] and coronavirus fear [133].

Table 8. Risk perception. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Risk Perception
Valid papers	44
Sample size	164,402
Geographical regions	Arabic emirates, Canada, China, Europe, France, Germany, Iran, Italy, Nigeria, Perù, Polonia, Qatar, Serbia, South Africa, South Korea, Turkish, UK, USA, Vietnam
Mediator variables	Age, Gender, Culture and Comorbidity

shows information about the papers analyzed for the study of risk perception.

7. Psychosocial Variables: Compliance and Social Distancing

7.1. Introduction

Governments around the world adopted prevention guidelines (suggested by the World Health Organization, WHO, and the Centers for Disease Control and Prevention, CDC) to contain the spread of COVID-19 [134,135]. The effectiveness of these measures depend mostly on the compliance of the population [135], while the adherence to these relied on how much they were perceived as effective [136,137]. Adopting preventive measures is crucial to control the virus’ spread and limiting its consequences [125,136,138,139]. Some of these procedures were enforced by states (e.g., closure of public places), whereas other procedures were only advised but out of the state’s control (e.g., social distancing, hand washing) [139]. Many of these rules and recommendations can be considered as fairly drastic for citizens [140]. For this reason, some people ignored these instructions, exacerbating the problem [134]. Despite the importance of these rules, such as social distancing, compliance has been sometimes inadequate [141]. Social distancing was defined by CDC as “keeping space between yourself and other people outside of your home by staying at least 6 feet from other people, refraining from gathering in groups, and staying out of crowded places and avoiding mass gatherings” and seems to be the best way to limit the COVID-19’s spread [141]. Nevertheless, social distancing may lead to self-isolation, which could cause severe psychological consequences [125]: for example, increased loneliness is associated with suicidal ideation and parasuicidal behavior, depression, anxiety, etc [138]. Although there are positive consequences for the public good, it is often a burden for the individual to adopt these preventive measures [136], because it is undeniable that COVID-19 restrictions could lead to drastic changes in daily life, including potential mental health problems [137].

7.2. Measures

In this part of the article, the authors chose to analyze compliance with several health behaviors (i.e., social distancing, hygiene, etc.) recommended by WHO and CDC advice for preventing COVID-19’s spread. Most of the articles used ad-hoc measures asking participants to rate their compliance with these behaviors [16,18,38,40,45,48,49,52,66,84,89,90,95,98,101,103,104,115,116,119,125,128,134,135,136,137,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169].

The already validated questionnaires used to measure compliance and social distancing are reported in

Table 9. Validated tools to measure “compliance and social distancing”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Preventive COVID-19 Behaviour Scale (PCV-19BS) [170]	0.91	1
Carrier Scenario (CS) [171]	0.71	1
Future Health Behavior (FHB) [171]	0.76	1
Current Health Behavior (CHB) [171]	0.89	1
UCLA Loneliness Scale-8 (ULS-8) [172]	0.96	2
Revised UCLA Loneliness Scale (R-UCLA) [173]	0.96	1
The COVID-19 Safety Behaviour Checklist (CSBC) [174]	0.84	1
Compliance with COVID-19 measures [175]	0.84 to 0.87	1
COM-B self-evaluation questionnaire [176]	0.59 to 0.85	1
YouGov Behavior Change (YGBC) [177]	0.66	1

.

7.3. Results for Compliance and Social Distancing

The results of the articles reviewed on compliance and social distancing were distinguished by the authors into the two main themes.

Compliance: The first aim of the COVID-19 guidelines was to help slow the spread of the virus, prevent infections, and the consequent depletion of medical structures, supplies, and healthcare personnel [144]. The most implemented protective behaviors were: social distancing, washing hands, avoiding contact, avoiding crowded places, mask and gloves wearing, staying at home as much as possible, avoiding public transportation [16,52,119,136,155,157]. Compliance with these behaviors was positively associated with the fear of COVID-19 [169,170,178], trust in the government [154,164], risk perception [48,104,134,137], trust in science [134,179], feeling of safety [137], perspective taking (i.e., the tendency to look at things from other people’s point of view) [90], and disgust towards pathogens [90]. On the contrary, conspiracy theories [18,45,90,103,167], impulsivity [48], self-centered behaviors [18,49,136], outdoor sports [104] and low conscientiousness score [146] were negatively associated with the adherence to the preventive behaviors. Moreover, communication appeared essential to enhance people’s compliance. Specifically, by giving detailed information, underlining the impact of non-adhesion to anti-contagion rules, and adhesion benefits, compliance increased [101,144,147,158].

Finally, people who agreed more with anti-covid restrictions [179], were more motivated [150], and had a positive evaluation about how institutions manage the pandemic crisis [165], complied more.

Compliance during the pandemic was associated with age, gender, social condition and psychological variables. The results are shown below.

Age: Age was positively associated with compliance intentions [154]. In general, older individuals appeared as more willing to accept anti-COVID-19 restrictions [140,142]. Nonetheless, results were contradictory: some research [89,101,119,179] showed that older citizens tended to be more (self-declared) compliant; but others demonstrated that young adults complied more with prevention measures (e.g., cell phone disinfection, maintaining a safe distance) [143,160,162,166]. Finally, one research showed that different age ranges followed different protective behaviors. Younger respondents avoided hugging and kissing with family members, friends and acquaintances, respected more movement restrictions and avoided contact with the elderly; differently, the elderly avoided more shaking hands, they maintained the recommended distance more strictly, disinfected their pets’ paws more often, and made more dietary plans [40].

Gender: Most of the papers that have investigated this topic agreed that women showed more compliance than men to protective behaviors (e.g., wearing mask, do not touch their face, do not shake hands) [40,66,98,101,119,139,152,154,160,161,179]. Females compliance seemed to be influenced by four internal sources: health history, anxiety, feeling responsible for others, feeling responsible for oneself [161].

Social Condition: the results about social conditions were conflicting. Some papers [40,152,179] found that a higher level of education was associated with more compliance to the prescriptions (e.g., avoid hugging and kissing, use disinfectants, avoid shaking hands). Other papers [101,160] instead found the contrary (high levels of education led to less adherence to the rules).

Comorbidity: according to some papers [148,156], anxiety could be a predictor for the adherence to COVID-19 prescriptions, in fact anxiety seemed to lead individuals to more responsible behavior [148]. Interestingly, adhering to anti-COVID-19 prescriptions seemed to lower Coronavirus anxiety levels [38], and decrease suicidal ideation, negative thoughts and sleep disorders [153]. Moreover, the use of personal measures of prevention (e.g., washing hands, mask wearing) were associated with less severe psychiatric symptoms [168]. Finally, people with chronic health conditions implemented more safety behaviors [180].

Social Distancing: Social distancing was one of the most important and effective measures to fight the COVID-19 pandemic worldwide [159] and was always highly recommended by governments and by WHO. The compliance with social distancing was associated with outcomes like boredom and loneliness, and was achieved by people with a higher self-control [135,159]. Loneliness due to social distancing was particularly high in those who were single, who had a psychiatric diagnosis, and those who ruminated and worried more [125,138]. Furthermore, many people perceived “social isolation fatigue”, that was increased by negative surprises (i.e., lockdown measures will be in effect for a longer time than expected) [145]. In fact, according to Briscese et al. [145] and Gollwitzer et al. [151], the intention to social distance weakens with time (due to habituation).

Some of the strongest predictors of social distancing behavior were related to COVID-19 related risk perception, knowledge and beliefs [141]; respondents who were more likely to comply with social distancing included those who believed they could help prevent the spread of COVID-19 [141,151], had higher risk perception [95,116,141,179], perceived their communities were adhering [141,149], knew COVID-19 information [141,151]. Other factors that resulted in a positive association with social distancing compliance were higher financial stability [141,181], negative illness attitude [143], used analytic thinking (deliberative, cognitively-demanding and slow) [167], and had higher FFFS (fight–flight–freeze system) scores (reflecting fear/avoidance behaviors) [143].

According to Charles and colleagues [141], even though 87.5% of the population had high levels of knowledge about social distancing, only 46.2% always practiced it. Those who perceived social distancing as difficult [141], those who did not like the spokesperson of preventive health behaviors [142], those who were still working [141,151], those who reported more conflicts with their partner [181], and impulsive [179], conservative [128,149] and self-centered people [49] were less likely to comply. In terms of personality, low extroversion [146], high agreeableness [171], and high conscientiousness [95,146,171] predicted the approval of social distancing measures. The motivation to adhere to social distancing rules was affected by empathy [163] and altruism [182]. Finally, humor could be useful to defuse the situation during the lockdown [115].

Social distancing during the pandemic was associated with age, gender, social condition and psychological variables. The results are shown below.

Age: For age, the results showed that younger people were most likely to follow the guidelines, especially social distancing [143].

Gender: According to several pieces of research [89,95,141,161,179], women were more likely to comply with social distancing than men.

Social Distancing: For the social condition, conflicting results have been found. Charles and colleagues [141] found that low-income respondents were more likely to comply with social distancing than those with a higher income [141]. On the contrary, the results of Farias and colleagues [149] showed the lower the income was, the less compliance with social distancing was observed [149].

Comorbidity: According to Hoffart and colleagues [138], social distancing was significantly associated with depression and anxiety [138].

Table 10. Compliance and Social Distancing. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Compliance and Social Distancing
Valid papers	66
Sample size	91,601
Geographical regions	Denmark, France, Germany, Greece, India, Iran, Ireland, Israel, Italy, MTurk, Netherlands, Norway, Poland, Qatar, Serbia, South Korea, Switzerland, UK, USA
Mediator variables	Age, Gender, Education and Comorbidity

shows information about the papers analyzed for the study of compliance and social distancing.

8. Mental Health Variables: Anxiety

8.1. Introduction

We chose to analyze anxiety because research on past epidemics, like SARS, MERS, swine flu and Ebola, revealed a wide range of negative psychosocial impacts, of which anxiety was one of the main outcomes [7,37,183,184,185,186]. In particular, the quarantine had a huge impact on people: when comparing quarantined versus non-quarantined individuals, the first were more likely to show psychological distress and to have a high prevalence of psychological symptomatology [187]. A recent study about the COVID-19 emergency indicated that “53.8% of respondents rated the psychological impact of the outbreak as moderate or severe; (…) 28.8% of respondents reported moderate to severe anxiety symptoms” [186].

Additionally, uncertainty may exacerbate the already existing detrimental effect of the pandemic on anxiety [188]. Anxiety is not just an outcome of the pandemic but could also have repercussions on behaviours implemented during the pandemic.

In the work of [185], it emerged that high and low anxiety individuals [189] behaved differently on some specific COVID-19 related conducts. High anxiety individuals may cause crowding (and thus increase the likelihood of infection), but at the same time they may be reluctant to seek medical assistance for fear of transmission, whereas individuals with low anxiety were found reluctant to comply with preventive measures [185].

8.2. Measures

The already validated questionnaires used to measure anxiety are reported in

Table 11. Validated tools to measure “Anxiety”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
GAD-7 [190]	0.92	33
The Fear of Coronavirus-19 Scale (FCV-19S) [191]	0.82	1
Hospital Anxiety and Depression Scale (HADS) [192]	0.68 to 0.90	7
Depression Anxiety Stress 21-item scale (DASS-21) [193]	0.88	16
Hamilton Anxiety and Depression Scale [194]	0.73 to 0.78	1
Beck Anxiety Inventory (BAI) [195]	0.92	7
Short Health Anxiety Inventory (SHAI) [196]	0.95	3
Self-Reporting Questionnaire (SRQ-20) [197]	0.80	1
Corona-19 Fear Inventory (CFI) [198]	0.82	1
12-item General Health Questionnaire (GHQ12) [199]	0.90	2
Patient-Reported Outcomes Measurement Information System (PROMIS) [200]	0.97	1
Coronavirus Anxiety Scale (CAS) [201]	0.86	4
Self-Rating Anxiety Scale (SAS) [202]	0.82	13
GAD-2 (2-item Generalized Anxiety Disorder) [203]	0.77	2
PROMIS anxiety and depression short-form [204]	0.95 to 0.97	1
State-Trait Anxiety Inventory (STAI) [205]	0.65 to 0.75	5
Preventive COVID-19 Behaviour Scale (PCV-19BS) [170]	0.91	1
Psychological Symptom Screening Test (SCL-90-R) [206]	0.98	1
Ecological Momentary Assessments (EMAs) [207]	0.73	1
State-Trait Anxiety Inventory (STAI) [208]	0.85	3
Social Anxiety Scale for Children (SASC) [209]	0.76	1
Patient Health Questionnaire (PHQ-4) [210]	0.75	2

.

Only four papers decided to use ad hoc measures for anxiety [16,41,161,211].

8.3. Results for Anxiety

Fitzpatrick et al. (2020) [212] and Guo et al. (2020) [213] highlighted high and medium levels of anxiety in the USA and China populations. Pandemic related distress was associated with anxiety: specifically, perceiving symptoms of COVID-19 (fever, cough, etc.), loneliness and stress increased anxiety levels [214,215].

Following protective measures, like mask-wearing and handwashing, were related to lower anxiety levels [23,168,216]. Nonetheless, adopting these protections appeared as not enough for drastically lowering anxiety; instead, adhering to social isolation policies appeared as essentials to reduce anxiety levels [38].

Anxiety was also increased by social contexts. People who knew someone infected by COVID-19 [7,58,110,217,218,219,220] reported a higher level of anxiety. Anxiety was also higher in people with a positive COVID-19 diagnosis [201,221,222,223]. Unexpectedly, longer periods of lockdown did not lead to high anxiety levels [119,224,225].

Individuals with a higher level of COVID-19 knowledge were more likely to report a higher level of anxiety [16,23,41,157], but, despite this, up-to-date and accurate health information, along with being aware of the risks of the pandemic, were protective factors against the pandemic’s psychological burden [186,226]. The exposure time to COVID-19 information was associated with greater anxiety levels [211,216,226,227], as well as worries and concerns about COVID-19 [37,118,223,228,229].

Risk perception also played a role in shaping people’s anxiety in the pandemic scenario. Indeed, people who perceived a high risk and a realistic high threat reported higher levels of anxiety [6,96,122,133,230].

Anxiety was also studied and investigated in some specific populations, like medical-care workers. Several papers analyzed samples composed healthcare personnel (doctors, nurses, physical therapists etc.), finding a high prevalence of anxiety symptoms [70,157,220,222,231,232,233,234,235]. Although, according to [8], there was no significant difference in anxiety levels among doctors, nurses and pharmacists. Instead, the essential aspect that appeared to affect healthcare personnel anxiety levels is the time spent in the hospital [72]. In addition, healthcare workers who believed the virus was developed in a lab reported higher levels of anxiety [235].

In conclusion, several studies found that lower anxiety was associated with a higher life satisfaction [236,237], higher crisis management appraise [238], higher perceived level of health [7], a higher number of leisure activities [7], more physical activity and exercise [226,239,240] and resilience [24,133,229]. Instead, higher anxiety was associated with loneliness [24], bad distress tolerance [24], more significant changes in daily life [7], paralyzing worry [229], reduced appetite [229], and engagement in COVID-19 prosocial acts [64].

Anxiety levels during the pandemic were associated with age, gender, social condition, socio-demographic variables, culture and psychological variables. The results are shown below.

Age: 21 papers found a strong relation between anxiety and age [4,118,143,157,187,221,222,224,225,239,241,242,243,244,245,246,247,248,249,250,251,251]. Specifically, 16 reports claimed that younger age seems to be associated with higher anxiety [4,118,187,201,221,224,225,239,241,242,243,244,245,247,248,249,251,252,253]. Only three papers argued the opposite [222,246,254]. Instead, specific anxiety for COVID-19 was higher with older age [248].

Gender: There are numerous articles that have found a strong correlation between gender and anxiety [4,157,255,256,257]. In almost all articles, the female gender was associated with a higher level of anxiety than men [8,16,67,104,161,185,187,221,222,225,233,242,243,244,245,250,258,259,260,261,262,263]. Females reported being more influenced by anxiety and experiencing more negative emotion [161]. Just one article seems to find mixed results, reporting higher GAD-7 scores for males than females [263].

Social Condition: Several studies have shown that a higher level of education is correlated with an increased anxiety about COVID-19 [67,201], particularly individuals with a lower level of education than middle school are more anxious [224]. Other studies show that high levels of anxiety appear to be income-related: lower income leads to higher levels of anxiety [187,233,248,256].

Socio-demographic variables: Research shows that unemployment, self-employment, private sector employment, lack of formal education, family size, and paternity (>2 children) were associated with a higher likelihood of negative mental health [247]. The confidence in the physician’s ability to diagnose COVID-19 infection, the decrease in the probability of contracting it and the lower frequency of seeking information about it, because of satisfaction with the information received, have been protective factors against negative mental health, like anxiety [247]. Another protective factor against COVID-19 anxiety is living in urban areas and living with parents [58]. Instead, longer working time and more years of work increased the risk of anxiety [220,261]. Finally, living in heavily crowded areas, where the social distancing requirements are lacking, could lead to higher levels of anxiety [264]. Students, especially abroad ones [54], experienced relatively greater anxiety during the period of the epidemic [7,265], except if they considered themselves healthy [155]. Research has also observed that levels of anxiety among medical students have decreased with the introduction of distance learning [37].

Culture: Only Liu et al. [24] investigated and found significant differences possibly due to culture diversity. In their study, Asian Americans, Hispanic/Latinos reported lower anxiety levels than Whites [24].

Comorbidity: Anxiety measured during COVID-19 epidemic showed different comorbidity effects [55,56,58,67,70,221,229,262,266,267,268]. People with psychological anxiety disorders appeared more vulnerable to the impact of the COVID-19 epidemic on their mental health in terms of well-being and quality of life [220,221]. Anxiety during the COVID-19 epidemic was associated with negative affect [269], PTSD symptoms [268] and risk of postpartum depression [266,267]. The well known relationship between anxiety and sleep disorders [188] seemed to be confirmed also about Coronavirus specific anxiety. Indeed, Coronavirus anxiety levels were higher in people with sleep disorders (like insomnia) and poor sleep quality [70,99,188,227,270,271,272]. Only one article [273] found no significant correlation between anxiety and sleep disorders.

Table 12. Anxiety. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Anxiety
Valid papers	102
Sample size	15,174,264
Geographical regions	Arabic emirates, Australia, Austria, Bangladesh, Brasil, Canada, China, Ecuador, Europe, India, Iran, Ireland, Italy, Japanese, Korea, Mexico, Nigeria, Norway, Pakistan, Philippines, Poland, Portugal, Spain, Swiss, Turkish, UK, USA
Mediator variables	Age, Gender, Education and Comorbidity

shows information about the papers analyzed for the study of anxiety.

9. Mental Health Variables: Stress

9.1. Introduction

The COVID-19 outbreak has caused public panic and mental health stress [10]. McEwen and colleagues [274] defined stress as an adaptive psycho-physical reaction in response to a physical, social or psychological stimulus, called a “stressor”. Stress-related responses are cognitive, emotional, behavioural and physiological [74,275]. During the first wave, the pandemic had an unprecedented impact on social lives around the world and can be viewed as a global stressor induced, beyond the risk for health, by the social isolation and distancing measures [276,277,278]. There is a lack of psychological literature related to epidemics (e.g., Ebola, Swine flu) or global pandemics; the last pandemic was the Spanish Flu of 1918, but there is not enough research about this [6]. The few recent studies about epidemics noted that there was increased stress due to the epidemic and quarantine [247]. Quarantine has been associated with high stress levels, depression, anxiety, irritability, insomnia, burnout, and physical symptoms [6,38,75,225,279]. Furthermore, being quarantined is associated with acute stress and trauma-related disorders, like Post Traumatic Stress Disorder (PTSD) [57,75,225,280,281,282,283]. In particular, during the first wave of COVID-19, PTSD appeared as characterized by involuntary memories of the trauma such as intrusions or nightmares, persistent avoidance of stimuli associated with the traumatic event, negative alterations in cognitions and mood that are associated with the trauma, as well as alterations in arousal and reactivity that are associated with the trauma [281,283]. Those populations who were more at risk, such as health workers, were more likely to develop this kind of disorder [10,57,225,284].

Moreover, the disruptive changes of the work market also led to higher general levels of stress [285]. For instance, many people had lost their jobs because of the pandemic, others had to work from home while taking care of the family (i.e., remote working) [7], especially teachers who were forced to rely on information and communication technology (ICT) despite their technological literacy and/or fluency [286]. In particular, this pandemic seemed to psychologically affect healthcare providers and other workers, since they were on the front line [284]. Additional factors that seem to exacerbate stress levels in the population during isolation or quarantine [287] were an incorrect perception about the transmission of the virus [288] and conspiratorial beliefs [46].

9.2. Measures

The validated questionnaires used to measure stress are reported in

Table 13. Validated tools to measure “Stress”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency. 1: Modified version of PTSD Checklist for DSM-5 [289]; 2: adapted from the 14–item Perceived Stress Scale [290].

Psychological Tool	$\mathit{\alpha }$	Fr
Impact of Event Scale-Revised (IES-R) [291]	0.94	11
Perceived Stress Scale 10 (PSS-10) [290]	0.86	13
Changes in Outlook Questionnaire (SF-CiOQ) [292]	0.83 to 0.85	1
Global Psychotrauma Screen, post-traumatic stress symptoms subscale (GPS-PTSS) [293]	0.54	1
The Post-Traumatic Stress Disorder related to COVID-191 [289]	0.94	2
DASS-21 (Depression Anxiety Stress 21-item scale) [193]	0.88	22
Acute Stress Disorder Scale (ASDS) [294]	0.86	1
Peritraumatic Distress Index (PDI) [295]	0.92	1
The coronavirus Stress Measure (CSM)2 [290]	0.55	1
The Stanford Acute Stress Reaction (SASR) [296]	0.84	1
National Stressful Events Survey Acute Stress Disorder Short Scale (NSESSS) [297]	0.88	1
The Brief Symptom Inventory (BSI-18) [85]	0.72 to 0.91	1
The Professional Quality of Life Scale (ProQOL) [298]	0.82 to 0.90	1
Measurement of occupational stress and Covid-10 factors [299]	0.82	1
Maslach Burnout Inventory (MBI) [300]	0.84 to 0.87	2
PSS-4, the 4-item Perceived Stress Scale [301]	0.70	1
PTSD Checklist for DSM-5 (PCL-5) [302]	0.94	6
The Chinese Perceived Stress Scale [303]	0.85	1
PTSD Checklist–Civilian Version (PCL-C) [302]	0.94	4
COVID-19 Peritraumatic Distress Index (CPDI) [304]	0.92	1
Impact of Event Scale-6 (IES-6) [305]	0.88	1
The Perceived Stress Questionnaire-8 (PSQ-8) [306]	0.83	1

.

9.3. Results for Stress

The COVID-19 pandemic and the consequent lockdown period increased the level of stress in the population [5,7,78,259,307], and this was likely to increase as the number of lockdown days increased [287,308]. Higher levels of stress were associated with: loss of job/education [89,309], having to go out to work [187], having an acquaintance infected with COVID-19 [89,187,247], likelihood of contracting the virus [310], more hygiene behaviors [247,256,310], history of stressful situations [187], medical problems [74,187], risk perception and COVID-19 specific fear/worries [10,74,133,247,259,310], perception of changes in life [74], dysfunctional coping strategies (i.e., denial, substance use, behavioral disengagement) [55,74,245], loneliness [10], perceiving physical symptoms as COVID-19 [215], belief in conspiracy theories [46], low distress tolerance [10], low social support [10,55,276], and decreased sleep quality [271]. Protective factors for stress were associated with: resilience [259,311], greater social connectedness [276], seeking information on COVID-19 [74,89,183], up-to-date and accurate health information [186], functional coping styles (i.e., planning, religion) [74,245], internal locus of control [74], perception of being able to avoid the virus [74], satisfaction with life [237], personality traits (high agreeableness, high conscientiousness, high emotional stability and high extroversion) [74], less exposure to COVID-19 [233,273], and agreed/confidence with government measures [186,216,277]. Moreover, individuals who reported high levels of optimism, and reported that the lockdown situation also had positive aspects, had lower stress levels [277,312,313].

Interestingly, using the internet was positively associated with higher levels of stress [183,226,269,309]. For example, some studies showed that the use of social media (during the pandemic) was associated with symptoms of PTSD [183], because on these platforms there was information available that is not necessarily based on well-founded facts [226]. This could lead to confusion and uncertainty among individuals about the preventive measures taken to reduce the spread of the COVID-19 virus. The minimisation and unacceptability of the severity of the COVID-19 pandemic has been associated with stress [226]. When people reported more stress related to COVID-19, they felt less satisfied, less engaged, and more conflicted in their relationships [181]. For example, one study [7] saw how those who had a relationship without cohabitation perceived more individual and relational stress during lockdown [7].

The category of healthcare workers reported higher levels of stress [127,220,221,254,260,268,281,288]; and in particular, females reported to be more stressed than males [168,220,314]. According to [87], the main factors associated with stress were: concerns for personal safety, concerns for their families, and concerns for patient mortality; the factors that reduced stress were: correct guidance, and use of protective behaviors for prevention.

Furthermore, other studies showed correlations between stress and low social support [70], lack of psychological therapy [315], poor sleep quality [314], hyperarousal symptoms [314], risk perception [127], worries and knowledge about COVID-19 [127].

Finally, different studies showed that stress was strongly associated with symptoms of burnout (i.e., depersonalization, emotional exhaustion) [233,268,316]. In particular, females [279,284], those who were exposed to COVID-19 patients [285,316], and long-term workers [279,284] reported high levels of burnout.

Stress levels during the pandemic were associated with age, gender, social condition, culture and comorbidity. The results are shown below.

Age: results showed a significant impact of age on stress levels [74]. A lot of papers reported higher stress and PTSD levels among the younger individuals rather than the older one [7,75,118,187,243,247,258,260,277,308,317,318,319,320]. These results are not found within the medical staff sample [87].

Gender: numerous articles have reported a significant relation between gender and stress, with females reporting higher levels of stress and PTSD than males [10,74,186,187,220,221,225,237,242,243,247,252,259,260,277,281,282,309,317,319,320,321,322], in particular, women who have been in direct contact with a COVID-19 patient [260], or those who have a recent exposure history in Wuhan [282]. Accordingly to Newby and colleagues [310], not only women, but also those who identify as non-binary or with a different gender were associated with higher self-reported stress. Finally, Cai and colleagues [87] found that factors that could reduce stress (i.e., correct guidance and effective safeguards for prevention from disease transmission) had a larger impact on females than males.

Social Condition: people with lower education experienced higher levels of stress [74,252]. Regarding the difference between occupations, those who had undergone the greatest change in work, like medical staff and teachers, showed themselves as the most stressed [275,286,288]. Indeed, teachers and healthcare workers continued to work in emergency circumstances. Unemployment and discontinued working activity (working more) were also associated with higher stress [7,225,247,309]. Finally, lower incomes [56,233,247], student status [186,265,269,308,323], marital status [247,317], large families [74,317], and more years of working [220] were associated with more stress.

Culture: Geographical differences in the effect of the pandemic were few and fragmentary, but are reported below. Italian, Chinese, Nigerian and Aboriginal populations reported high prevalence of stress symptoms [231,280,310]. Australians were more stressed than the Chinese population, but less than Italians [256]. The Austrian population was less stressed than the Chinese population [226].

Comorbidity: During the COVID-19 pandemic, a significant positive correlation was observed between stress and depression [251,309,312], anxiety [251,312], sleep [283], physical illness [220,262,310,320,324], somatization [312], and history of mental disorders [220,259,310].

Table 14. Stress. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Stress
Valid papers	84
Sample size	198,240
Geographical regions	Australia, Austria, Bangladesh, Brazil, China, France, India, Iran, Italy, Lebanon, Mexico, MTurk, Nepal, Nigeria, Norway, Poland, Portugal, Slovenia, Spain, Switzerland, Turkey, UK, USA
Mediator variables	Age, Gender, Social Condition, Culture and Comorbidity

shows information about the papers analyzed for the study of stress.

10. Mental Health Variables: Depression

10.1. Introduction

Depressive disorder affects thoughts, emotions, and physical health to varying degrees [325], and it often manifests as low mood, slow thinking, decreased activity, and impaired cognitive function [253]. The consequences are quite serious, ranging from interruption of interpersonal relationships to lifelong mental illness and suicidal behavior [78,253,312,326]. Previous public health pandemics have been linked to an increase in mental health problems. For example, Ebola, SARS, MERS, novel influenza A, equine influenza outbreak seemed to be associated with higher levels of depression [4,6,157]. In line with previous epidemics, the COVID-19 pandemic showed a similar effect on depression [212,222,223,272,304]. In particular, quarantine and social isolation, but also the strict prevention and control requirements, and the patients’ lack of communication with the outside world, were associated with higher rates of psychological depressive symptoms [78,185,187,214,216,223,245,251,321,327].

The pandemic not only elicited symptoms of psychological distress in people with no previous history of depressive symptoms, but also worsened them in people with a history of psychiatric disorders such as depression [78,326]. Another source of depression symptoms was individuated by the literature in the constant exposure to information about COVID-19 [185,216], combined with the uncertainty of the situation [185,251,312]. Just one of 97 papers did not capture effects of the pandemic on depression levels [214].

10.2. Measures

The already validated questionnaires used to measure depression are reported in

Table 15. Validated tools to measure “Depression”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Hospital Anxiety and Depression Scale (HADS) [330]	0.67 to 0.90	6
Filgueiras Depression Inventory (FDI) [252]	0.96	1
Edinburgh Postpartum Depression Scale (EPDS) [331]	0.82	3
Hamilton Anxiety and Depression Scale [217]	0.73	2
Beck Depression Inventory (BDI) [332]	0.92	5
Beck Depression Inventory II (BDI-II) [333]	0.82	2
Depression Anxiety Stress 21-item scale (DASS-21) [193]	0.88	20
Self-Rating Depression Scale (SDS) [334]	0.83	8
Patient Health Questionnaire-2 (PHQ-2) [335]	0.77	3
Depression Self-rating Scale for Children (DSRSC) [336]	0.77 to 0.84	1
Patient Health Questionnaire (PHQ–9) [337]	0.85	25
Patient Health Questionnaire (PHQ-8) [210]	0.89	1
PROMIS anxiety and depression short-form [204]	0.96 to 0.97	2
Patient Health Questionnaire - 4 (PHQ-4) [338]	0.75	4
Reflection and Rumination Questionnaire (RRQ) [339]	0.85 to 0.90	1
Center for Epidemiologic Studies Depression (CES-D) Scale [340]	0.85 to 0.90	6
COVID-19 Peritraumatic Distress Index (CPDI) [304]	0.92	1
Peritraumatic Distress Index (PDI) [295]	0.83	1
General Health Questionnaire - 12 [341]	0.90	1
The Brief Symptom Inventory (BSI-18) [342]	0.86	3
World Health Organization Self Reporting Questionnaire (SRQ-20) [343]	0.78	1
Brief Psychiatric Rating Scale - 18 (BPRS) [344]	0.75	1

. Only two studies used ad hoc questionnaires to analyze depression [328,329].

10.3. Results for Depression

The influence of COVID-19 on depression levels is not solely attributable to the factors outlined in the introduction. There were a number of dynamics that still impact depression and were increased during the pandemic [6,212,259,310,328]. Among these factors were those related to aspects strictly connected with COVID-19 [24,111,133,157,214,215,220,225,240,243,269,345,346,347], risk perception and its reactions [96,111,129,133,143,216,247,256,309], low quality of life [55,157,214,243,259,312], and addictions [220,241].

Pregnancy was another area impacted by COVID-19, especially when it concerned depression, due to its nature as a special, but also critical, moment in womens’ lives [267]. In particular, WHO reported that about 10% of pregnant women experience a mental disorder, primarily depression [267], whose likelihood has increased during the COVID-19 pandemic [266,348,349]. Furthermore, for both pregnant women and their husbands, fear of COVID-19 was significantly associated with their depression level [191].

As for the COVID-19 pandemic effects on medical staff, there is some agreement between scholars. Some works highlighted that medical staff particularly suffered from the COVID-19 pandemic in terms of depression symptoms, due also to the increased work-related risks and workload [4,8,220,222,254]. In particular, medical staff members were more likely to have an impairment of their attention, cognitive functioning, and clinical decision-making [220]. Some others found that healthcare workers were at risk to develop depression [10,213,217,219,220,231,232,235,242,254,262,268,270,350]. However, other papers found the contrary [221,260], thus suggesting that the effect of the pandemic on the depression of health workers can be mediated by factors such as: longer average working time, more time in contact with COVID-19 patients, spending more time thinking about COVID-19, and spending more time searching for coronavirus information [217,219,270]. Some protective factors individuated specifically for healthcare workers were: maintaining contacts through social networks [265], individual resilience [281], distress tolerance [281], social support [65], and having a meaning in life [312].

Regarding the general population, literature identified a number of protective factors towards depression. In particular, resilience [24,133,259], sexual satisfaction [236], pandemic-related prosocial experiences [64], hope and zest [183], positive affect [96,243,351], confidence about overcoming COVID-19 pandemic [157,216,352], living farthest from the epidemic [273], high levels of family support [24,307], credibility of updates [216,247,352], home self-quarantine [269,352], having a garden [245], continuing to work [245], physical exercise [239,307], meaning in life [312], trust in medical staff [216,247], taking prevention measures [168], and optimism [307,312] were associated with lower depressive symptoms.

Depression levels during the pandemic were associated with age, gender, social condition, culture and psychological variables. The results are shown below.

Age: Age seemed to be significantly associated with depression [157]. Younger people reported a higher prevalence of depressive symptoms during the COVID-19 pandemic [4,7,184,221,225,235,239,242,243,245,247,248,249,258,347]. Conversely, older people showed lower depression levels [7].

Gender: The findings showed that women had higher levels of depression than men [4,8,10,67,157,186,187,220,221,222,225,239,242,243,247,254,255,258,259,260,262,272,307,321,347], in line with pre-COVID-19 literature [353,354]. A prolonged exposure to domestic hostility due to quarantine worsened the symptoms [259]. Just one research found the contrary [310].

Social Condition: An association between sociodemographic variables and depression was captured: lower levels of education, unemployment, lower income, living in urban areas, living in crowded areas where it is impossible to maintain social distance, not having a child, having an acquaintance infected with COVID-19 have been associated with higher levels of depression [6,8,67,185,187,226,233,245,247,248,253,264,347]. Remote working was associated with lower depressive symptoms [245], while being a student seemed to be significantly associated with higher levels of depression [4,8,186,254,265,310].

Culture: Some differences related to culture were found: during the COVID-19 pandemic, Chinese, Asian Americans, and Israeli Arabs were at low risk of present depressive symptoms, compared to Spanish, White Americans, and Israeli Jewish [7,24,355]. According to Gobbi and colleagues [326], Turkish had the lowest level of depression, compared to Canadians, Pakistanians, and Americans [326].

Comorbidity: Anxiety [38,96,223,345], stress [214,312], sleep difficulties [223,271], history of mental health issues and chronic illness [220,221,255,259,262,268,310,347,356] were consistent predictors of higher depression during COVID-19 pandemic. These results also are in line with pre-COVID-19 literature [353,354].

Table 16. Depression. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Depression
Valid papers	97
Sample size	243,830
Geographical regions	Australia, Austria, Bangladesh, Brazil, Canada, China, France, India, Iran, Ireland, Italy, Mexico, MTurk, Nigeria, Pakistan, Portugal, Saudi Arabia, South Korea, Spain, Switzerland, Turkey, UK, Urban South Africa, USA
Mediator variables	Age, Gender, Education, Culture and Comorbidity

shows information about the papers analyzed for the study of Depression.

11. Mental Health Variables: Other Consequences of COVID-19 on Mental Health: Wellbeing, Psychological Distress, Fear of COVID-19 and Sleep

11.1. Introduction

The COVID-19 pandemic, as well as previous epidemics (such as SARS, H1N1, Ebola virus), have caused other psychological consequences with respect to those considered in the previous paragraphs, both on individuals affected by these diseases, as well as on the non-infected ones [3,5,8,9]. In particular, four constructs about Mental Health and Quality of Life domains will be examined below due to their connection with the COVID-19 outbreak.

Wellbeing: Living under the threat of the pandemic and its consequences represented a significant challenge to wellbeing [329,350,357]: indeed, the first wave of the COVID-19 pandemic was linked to worsening in wellbeing and mental health [9,329,358]. The sudden outbreak of COVID-19 and the preventive measures had a strong impact reducing the quality of life of the population [5,93]: forcing a drastic change in habits and routines [359], reducing social contact [329], and restricting freedom of movement as a consequence of social isolation [5]. Both the sudden outbreak of a new and unknown virus and the measures adopted to decrease its spread have had a strong impact on the mental health and the psychological well-being of the population [5].

Psychological Distress: Variables positively associated with psychological wellbeing were negatively associated with psychological distress [360]. APA defined psychological distress as a set of painful mental and physical symptoms that are associated with normal fluctuations of mood in most people [361]. Furthermore, Arvids Dotter and colleagues [362] defined psychological distress as a state of emotional suffering associated with stressors and demands that are difficult to cope with in daily life. When facing something new or unknown, such as the COVID-19 virus, a lack of effective treatment can lead to psychological distress in health care professionals as well as in patients [362]. The risk of getting infected, the lockdown scenario, and the consequent changes in habits may contribute to feelings of loneliness (connected to social isolation) and psychological distress [5,359,363].

Risk factors associated with greater distress were: not having an adequate supply (of food or goods of first need) [364], quarantine [364,365], low level of health perception [364], risk control [364], risk perception [364], low social support (family) [363], negative coping styles [75], delay in returning to work and school [365], negative thoughts [366], being close to potential risk groups [9], and work environment [3]. In the pandemic, healthcare workers are at high risk of psychological distress [357]: they were worried about overtime work, the stigma of the illness, and the health of their families and themselves [279]. While, protective factors associated with distress were: taking personal prevention/protection and clothing disinfection measures, clear communication of directives, and precautionary measures [364].

Fear of COVID-19: Fear is a negative emotion accompanied by excessive levels of emotive avoidance concerning particular stimuli, and it is an adaptive danger response [133,178,367]. It is associated with clinical phobias [178], social anxiety [178], risk perception [367], health anxiety [367], bad psychological and physical health [133], use of social media [367], high neuroticism and worries [366]. However, fear to some extent can be helpful for people in terms of leading them to comply in protective behaviours against COVID-19 [107,133,178].

In particular, it is also necessary to define the fear of COVID-19, that is based on four basic pillars: fear of the body, significant others, uncertainty, and action/inaction [9,368]. Especially the uncertainty led to changes in habits that are associated with decreased wellbeing and increased psychological distress [9,178].

Sleep: Finally, sleep is an indispensable physiological process in maintaining physical health [369] and sleep quality is a key indicator of health [70]. The stressful situations caused by the COVID-19 pandemic appeared to enhance symptoms such as sleep suppression, increased wakefulness, insomnia, difficulty falling asleep, maintaining sleep, waking up early, daytime sleepiness, nightmares and daytime dysfunction, and other sleep-related disorders [69,70,188,273,283,369]. On the other side, social support appeared to reduce stress and consequently improve sleep quality, and also enhance wellbeing [70].

11.2. Measures

Wellbeing: The validated questionnaires used to measure wellbeing are reported in

Table 17. Validated tools to measure “Wellbeing”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Ryff’s Psychological Well-Being Scales [370]	0.84	1
Professional Quality of Life Scale (ProQOL) [371]	0.75 to 0.88	1
Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) [372]	0.91	3
Psychological Well-Being Scale [373]	0.86	1
Psychological General Well-Being Index (PGWBI) [374]	0.90 to 0.94	2
WHO Quality of Life-BREF (WHOQOL-BREF) [375]	0.70 to 0.82	1
The Revised Illness Perception Questionnaire (IPQ) [376]	0.61 to 0.90	1
Positive and Negative Affect Schedule (PANAS) [377]	0.90 to 0.91	2
The satisfaction with life scale [378]	0.89	3
Danish Mental Health and Well-Being Survey (DMHWBS) [379]	0.94	1
Mental Health Continuum–Short Form (MHC–SF) [380]	0.89	1
The SCL90-R questionnaires [381]	0.70	1
My Life Today [382]	0.82	1
PERMA-Profiler [383]	0.96	1
The Brief Symptom Inventory (BSI-18) [342]	0.86	1

.

Different research used ad hoc questionnaires to analyze wellbeing [17,38,122,212,233,329].

Distress: The validated questionnaires used to measure distress are reported in

Table 18. Validated tools to measure “Distress”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Subjective Units of Distress Scale (SUDS) [384]	no	1
COVID-19 Peritraumatic Distress Index (CPDI) [304]	0.92	3
Kessler 6 questionnaire (K6) [385]	0.89	8
Distress Tolerance Scale [386]	64 to 0.90	1
Kessler 10 questionnaire (K10) [385]	0.88	3
GHQ -12 [387]	0.90	6
Impact of Event Scale [388]	0.94	2
Distress Symptoms (BSI) [342]	0.82	1

.

Different research used ad hoc questionnaires to analyze distress [8,45,90,166,310,363,366].

Fear of COVID-19: The validated questionnaires used to measure the fear of COVID-19 are reported in

Table 19. Validated tools to measure “Fear of COVID-19”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Fear of the Coronavirus Questionnaire (FCQ) [367]	0.77	1
The Fear of COVID-19 Scale (FCV-19S) [170]	0.82	6
COVID-19 Fear Inventory (CFI) [198]	0.82	1
The numeric rating scale (NRS) [389]	no	1

.

Different research used ad hoc questionnaires to analyze fear [37,107,133,212,277,390].

Sleep: The already validated questionnaires used to measure sleep are reported in

Table 20. Validated tools to measure “Sleep”. In the table are reported the psychological tools adopted by the studies taken into account, their internal consistency (reliability), and their frequency.

Psychological Tool	$\mathit{\alpha }$	Fr
Insomnia Severity Index (ISI) [391]	0.91	6
Pittsburgh Sleep Quality Index (PSQI) [392]	0.69	9
Sleep Self Assessment Scale (SRSS)[393]	no	1
PROMIS Sleep-related impairment [394]	0.90	1

.

Different research used ad hoc questionnaires to analyze sleep quality [69,188,256,269,395].

11.3. Results for Other Consequences of COVID-19 on Mental Health

The influence of COVID-19 on the levels of wellbeing, distress, fear, and sleep appeared as mediated by different factors that are reported below.

Wellbeing: The initial stages of the pandemic had minimal detrimental effects on wellbeing [68]. Several studies found that quarantine [310,359], living in regions with higher COVID-19 prevalence [309], loneliness [214,329,396,397], risk perception [3,122], conspiracy beliefs [45], suspected infection [71], spending time searching for information about COVID-19 [71], searching information on social media [3,359], not practicing prevention measures [3], fear of COVID-19 [9,17,397,398], intolerance of uncertainty [9], rumination [9], exhaustion [397], internet addiction [357], work overload [398], stressful life events [357], negative coping styles [71,86,93], and substance use [93] were associated with lower levels of mental wellbeing. On the contrary, physical exercise [239,399], positive coping strategies (e.g., emotional support, humor, religion) [8,71,77,93], optimistic attitude [35] (Imtiaz et al., 2020a), hope [106,183,329], resilience [106,329], perception of effective protective measures [397], satisfaction at work [397], good social support [71,357,398], and higher self-efficacy [3,398] were associated with an increase of wellbeing levels.

Regarding healthcare workers, it is demonstrated that their workload had a significant negative impact on their psychological wellbeing [279,400].

Distress: Greater mental distress was evident post lockdown [8,214]. The decrease in distress during the initial phase was attributed to preventive measures (including medical support and resources to stop the spread of the virus) [304]. Among the healthcare workers, high levels of distress were found [90,397,401,402], finding also that those who believed the virus was developed intentionally in a lab reported higher levels of distress [401].

Fear: Regarding the fear of COVID-19, it was associated with psychological distress and life satisfaction [170,228,403]: this was a mediating factor between intolerance of uncertainty and wellbeing [9]. According to Mertens and colleagues [367], there are four predictors of the fear of COVID-19: health anxiety, regular media use, social media use, and risks for loved ones [367]. Nevertheless, COVID-19 fear has been shown to predict positive changes in behavior (social distancing, hand hygiene) [107,170,178,390]. Furthermore, people who had higher levels of risk perception experienced more fear, and they were more inclined to develop a mental disorder [133,228]. Finally, between healthcare workers there was also a negative correlation between fear of COVID-19 and wellbeing [279,400,403,404].

Sleep: Reporting sleep-related functional problems was significantly associated with higher distress [397]. During quarantine, sleep timing markedly changed: people went to bed and woke up later, and spent more time in bed, but, paradoxically, also reported a lower sleep quality [271]. The difficulty in falling asleep was the result of an increase of “presleep cognitions”, which could probably lead to PTSD [283]. According to Di and colleagues, and Bai and colleagues [395,405], even children reported sleep difficulties during the COVID-19 pandemic [395,405]. In particular, sleep disorders were higher in COVID-19 confirmed cases [223], and those who had adversity experiences/worries [69], no exercise [65], and lower social support [65,69].

According to different studies [65,184,263], healthcare workers had the highest rate of poor sleep compared to other occupations [65,184,263]. They reported more difficulty in falling asleep [314], short sleep duration [314], and more sleep disturbance like insomnia [231,314].

Wellbeing, distress, fear and sleep levels during the pandemic were associated with age, gender, social condition, culture and comorbidity.

Age: Several studies have shown that being younger and having negative self-perceptions about ageing were associated with increased discomfort [5,75,309,319,359,363,364,406,407]. Thus, as a result, older people with positive self-perceptions of ageing seem to be more resistant during the COVID-19 outbreak [8,86,363,397]. In contrast with these findings, only Zhang and colleagues [365] argued that age was positively associated with psychological distress [365]. Nevertheless, an effect of the quarantine was found even on young people: children and adolescents in quarantine suffered greater psychological distress than children and adolescents not in quarantine [166]. Sleep disorders were more frequent in younger people [256,288,408]. In particular, as far as COVID-19 hospitalized patients are concerned, sleep disorders are greater in people aged 50 and over [223].

Gender: Several studies showed that being female was associated with higher levels of distress, and lower levels of psychological wellbeing [5,86,183,233,239,309,319,363,364,397,406,407,409,410]. Finally, fear of COVID-19 was more severe in females than in males [411]. Most of the papers found the prevalence of sleep disorders as insomnia [225,408] or poor sleep quality was higher among women [256]. Only Zhpu and colleagues [263] found conflicting results: in their paper, the sleep quality of men was worse than that of women [263].

Social Condition: People who were unemployed [309,407,409] or who had just lost their job reported higher psychological distress levels [406]. As for the workers, faculty, post-doctoral researchers and students showed lower levels of wellbeing than staff members [233,345]. Another significant association was found with marital status: those who were married had higher levels of wellbeing than those who were divorced because they had more social contacts and social support [318]. A relationship emerged between job loss, being single, and sleep disorders [69,256]. In particular, unemployed mothers or mothers who continued to work outside were more likely to suffer from sleep disorders than those who continued working at home or stopped working during lockdown [405].

Culture: Only one paper investigated cultural differences. According to Kimhi and colleagues [355], Arab respondents expressed a significantly higher level of COVID-19 distress compared with Jewish ones, who, on the contrary, reported higher levels of well-being and resilience [355].

Comorbidity: Having a background illness [239,310,324,398], depression [214,350], anxiety [214,329,350,412], stress [214], burnout [397], sleep problems [397], alcohol abuse [350], and OCD [38] were found to increase psychological distress. For what concerns fear, this was significantly related to depression [133,327], anxiety [133,234,327], specific phobias [327], stress [133,277], health anxiety [228], and general poor mental health [212]. PTSD [283,314], anxiety [70,188,223,256,269,271,272,408], physical dysfunctions [223], stress [70,256,269,271,369], depression [256,269,271], and general mental illness [69] were associated with sleep problems.

Table 21. Other consequences of COVID-19. Summary of sources, with the number of papers analyzed, the total sample size, the provenance of the sample and the mediator variables. “MTurk” is used to indicate a sample extended worldwide.

Other Consequences of COVID-19
Valid papers	87
Sample size	303,418
Geographical regions	Australia, Bangladesh, Brazil, Canada, China, Cuba, Cyprus, Denmark, Ecuador, Ethiopia, France, Germany, Greece, Holland, India, Iran, Iraqi Kurdistan, Israel, Italy, Japan, Jordan, Mexico, MTurk, New Zealand, Nigeria, Pakistan, Poland, Spain, Switzerland, Turkey, UK, United Arab Emirates, USA
Mediator variables	Age, Gender, Education, Culture and Comorbidity

shows information about the papers analyzed for the study of Wellbeing, Psychological Distress, Fear of COVID-19 and Sleep.

12. Discussion

This review aimed to explore the impact of the first wave of COVID-19 on people, analyzing different psychological and psychosocial dimensions. To identify the domains impacted by COVID-19 we relied on previous pandemic literature (e.g., Ebola, SARS, MERS, Novel influenza A, Equine influenza), and chose to reanalyze them in the current one. The analysis of the results revealed that the dimensions most impacted by COVID-19 among psychosocial variables were: beliefs about COVID-19, coping strategies, risk perception, social support, and compliance and social distancing.

In particular, regarding the three fundamental moderators about beliefs and media persuasion (attitudes, knowledge, and finding news on social networks) the analysis of works showed that, although some attitudes were optimistic (e.g., the probability of being infected was perceived as low), most participants took precautionary measures to prevent infection [34]. On the contrary, higher levels of negative thoughts (e.g., fatalism toward the pandemic) were associated with lower behavioral intentions to comply with protective measures [36]. Furthermore, accurate knowledge about COVID-19 predicted more information-seeking behaviour, the ability to discern between true and false information, compliance with preventive measures, and showed lower panic reactions, a lower level of anxiety, and greater risk perception [25,41,126]. The major sources people used to seek information during the pandemic were TV and social media, but these also led to an increase in disinformation and a spread of fake news and conspiracy theories [16,17,43]. Regarding conspiracy theories about COVID-19, some studies showed that subjects who believed in them complied less with the suggested protective behaviours and were less likely to get vaccinated [18,51,52]. On the contrary, those whose core beliefs (beliefs that guide individuals in their identity) were less influenced by the pandemic reported to engage more in social isolation measures [38].

Beliefs about COVID-19, its transmission, and its protective measures, demonstrated to strongly influence compliance with them. In particular, low compliance was associated with belief in conspiracy theories, impulsivity, and self-centered behaviour [48,49]; instead, higher compliance with protective measures was associated with fear of COVID-19, trust in government and science, and higher risk perception [134,154,169,179].

The risk perception has shown a double effect: on the one hand, a higher risk perception induces higher levels of compliance, for example, in terms of compliance with social distancing [56]. On the other hand, however, it can lead to avoidant behaviors, such as being reluctant to seek medical assistance for fear of transmission, which can damage both the health of the individual and make the management of the pandemic more difficult, probably due to the sense of loss of control that is notoriously associated with high levels of risk perception [100,185].

Quarantine, and the COVID-19 pandemic in general, also had negative social consequences. For example, many social support networks have been interrupted: indeed, social distancing led to boredom, loneliness, social isolation fatigue, anxiety, and depression [54,135,159]. Nevertheless, social support proved to be crucial and had a positive impact on coping with the COVID-19 pandemic [56,59,66]. The results showed that positive coping strategies were correlated with less distress, fear, and general vulnerability, and higher wellbeing. In contrast, negative coping strategies (e.g., substance use, behavioral disengagement) were related to higher stress, distress, fear, and anger [77,79,88].

In addition, psychological dimensions regarding some areas of mental health, specifically anxiety, stress, depression, wellbeing, and sleep, were impacted due to the pandemic.

Restrictive measures had, in general, a protective effect on anxiety, whereas being more in contact with the virus resulted in higher levels of it. In particular, anxiety was negatively related to protective measures from COVID-19 [23,73,168], whereas it related positively to testing positive for COVID-19, being in an environment exposed to COVID-19 [58,110,219], knowing people who were COVID-19 positive [201,221,222], and having more knowledge about COVID-19 [16,23,41].

The COVID-19 pandemic was also associated with higher levels of depression, both in people with a history of psychiatric disorders and in others [78,326]. These results seemed to be due to quarantine and social isolation, but also to the constant exposure to information about COVID-19 [187,223,251].

Stress appeared to be positively related to the COVID-19 pandemic and the subsequent lockdown period [7,78,307]. This effect is buffered by some protective factors, such as lower exposure to COVID-19 [233,273] and some personality traits, such as emotional stability and conscientiousness [74].

The research has highlighted some risk and protection factors of wellbeing during the pandemic. Quarantine, living in areas with high virus prevalence, overexposure to COVID-19 information, and conspiracy theories seem to decrease wellbeing; while exercise, positive coping strategies, and greater social support seem to promote it [8,399].

The pandemic also impacted the distress experienced by people, particularly of those who were already physically and/or mentally ill. Indeed, poor sleep quality was positively correlated with stressful situations caused by the COVID-19 pandemic [69,273,369].

Overall, the COVID-19 experience may be useful to derive some evidence-based insights to be applied in case of future similar emergencies. Concerning compliance, promoting a correct risk perception in the population by disseminating correct information [17,19,20,39,46,169], and suggesting effective (and positive) coping strategies [71,77,87,91,93,94], would be helpful in increasing the degree of compliance with governmental measures [39,48,76,95,102,104,105,120,123,127,134,137,139,142,148,152,169,178]. Nonetheless, compliance is also affected by the perception of the effectiveness of such restrictions. For this reason, a better media campaign, focused not on fear but on efficiency (and self-efficacy), would be ideal, in the event that an extreme situation, such as the COVID-19 pandemic, should recur [21,24,40,41,46,49,169].

As highlighted by the literature, different management of the emergency would have also led to better psychosocial and psychological outcomes. In this regard, social support appeared to ease emergency management [55,66,67,69,70,71]. Greater social support was also useful in reducing the levels of anxiety, depression, stress, that ultimately contributed to people’s compliance with the rules, fear of contagion, and management of the emergency (both collectively and individually) [55,67,70,71]. Given the widespread increase in psychological/psychiatric symptoms (anxiety, depression, stress, insomnia, and psychological distress) [4,58,64,96,225,233,243,271,319,347], countries all over the world, especially those most affected by the virus, should envisage in such situations a prompter professional support to citizens, so to prevent the emerging or worsening of symptoms.

Finally, one of the factors identified in the scientific literature as key to countering the pandemic was resilience. Resilience resulted in stress [259,311], anxiety [24,57,133], and depression [157] protective factors, and has a positive relationship with wellbeing [329]. Resilience is fundamental in enabling people to change their behaviour in response to stressful events, like the COVID-19 pandemic, which in this specific case may prevent someone from getting sick or dying. For this reason, resilience appears to promote readiness to change, which is one of the factors that enable communities to respond adequately to emergencies such as a pandemic [12,13,14].

As described above, the pandemic affected several psychological dimensions. However, its effect appeared to be conditioned by a few factors that allowed us to qualitatively describe the psychological and socio-demographic profile of the most resilient people. The factors that more frequently had a significant influence on the previously analyzed variables are: age, gender, culture, social condition, healthcare workers and workload.

As for age, younger people appeared to be less resilient: they were more affected in terms of both psychosocial variables and mental health. Young people showed greater compliance with preventive measures, probably due to their greater risk perception and knowledge of the virus. Despite their major levels of compliance, young people suffered more from the effects of quarantine, implemented negative coping strategies more often, and suffered more from psychological distress and, in general, mental health problems (i.e., anxiety, depression, stress) [24,40,73,74,143,154,157,166,347].

Similarly, women were most affected by the pandemic: they tended to comply more with the rules because they were more worried and perceived greater risks, so they mainly used emotionally focused coping strategies. Again, despite showing higher compliance levels, women were more susceptible to anxiety, depression, stress, sleep disorders, and distress than men [89,114,139,187,223,363,408]. Furthermore, post-partum depression increased during the pandemic [266,348,349]. In light of this, young women appear to be the least resilient, especially under certain conditions such as postpartum.

An effect of culture was particularly evident on beliefs: collectivist societies seemed to have more accurate beliefs about COVID-19 than individualist societies [47,49]. However, the levels of risk perception, as well as people’s mental health, apart from possible cultural factors, appeared to be affected by the levels of contagion around the world. So, it was not possible to distinguish between the two causes which one influences more the results [78,127].

Lower levels of education were associated with more fear of dying by COVID-19, perception of susceptibility to the infection and worse overall knowledge about the pandemic [47], more anxiety [247], stress [258] and depression [347].

Lower income and unemployment were associated with higher levels of anxiety [247], stress [56], depression [347], higher levels of distress [406] and more sleep disorders [69,256]. The results about compliance and social distancing were conflicting regarding income [40,101,141,149].

Finally, healthcare workers experienced an increase in workload that led to several consequences, including higher risk perception [127], higher levels of anxiety [184], stress [288], depression [222], less wellbeing [232], worse sleep quality [184], and higher risk of psychological distress [357]. Healthcare workers implemented coping strategies like religious strategies, acceptance, planning, physical activity, virtual support groups, talk therapy, and positive framing [8,87,90]. Of these factors, some (e.g., age, gender) are known to be relevant to mental health even before the pandemic; nevertheless, the papers we analyzed did not distinguish the effects of these variables before and after the pandemic.

In conclusion, the aim of the paper was to research how the COVID-19 pandemic impacted the psychological health of the global population during the first wave. In particular, we investigated which psychosocial and psychological variables were most affected, delineating a first and preliminary picture of symptoms related to COVID-19 that could be called “psychological COVID-19 syndrome”. This syndrome appeared to be characterized by an increase in stress, depression, anxiety, sleep disturbance, and distress related symptoms. Healthcare workers were the most affected by the consequences of the COVID-19 pandemic, as they found themselves on the front lines facing the virus and experienced a sudden increase in workload. In addition to healthcare workers, the results showed that people who proved less resilient were young women, with low income and low education, particularly if they were in post-partum condition. In light of this, it would be hopeful to implement prevention and aid projects for these individuals, particularly affected by the pandemic.

The number of studies about the psychological effects of the COVID-19 pandemic is increasing. However, the literature available until June 2020 was still in the early stages of maturity. Therefore, thanks to the articles published since June 2020, the “psychological COVID-19 syndrome”, hypothesized in this review through the first wave studies, could be modified or expanded. Future research should investigate the second and third waves of the pandemic so that comparisons can be made with the present research.