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. 2020 Dec 24;13:3103–3120. doi: 10.2147/RMHP.S284102

Psychological Distress in Quarantine Designated Facility During COVID-19 Pandemic in Saudi Arabia

Abdulmajeed A Alkhamees 1,✉, Moath S Aljohani 2, Mohammed A Alghesen 3, Ali T Alhabib 4
PMCID: PMC7769153  PMID: 33380850

Abstract

Background

Quarantine is a useful measure for preventing and controlling pandemics; however, it might be stressful for quarantined individuals. Fear and anxiety about a disease can be overwhelming. These emotions were reported for individuals in involuntary quarantine facilities dedicated to quarantine purposes.

Methods

This cross-sectional study surveyed the individuals in involuntary quarantine institutions (for a planned period of 14 days of quarantine) in two regions of Saudi Arabia. The mental health status of individuals was assessed using the Revised Impact of Event Scale (IES-R) and Depression, Anxiety, and Stress Scale (DASS-21).

Results

The study surveyed 214 quarantined/isolated individuals. The stress, anxiety, and depression rates were 25.7%, 21.5%, and 32.7%, respectively. On the IES-R, 28.0% of the participants met the criteria for psychological distress. Female gender, self-reported history of psychiatric disorder, and average health status were significantly associated with negative psychological impact and depression, anxiety, and stress symptoms (p<0.05). Watching television was found to be a factor in reducing rates on the IES-R and DASS-21 scale while working out lowered rates on the IES-R alone (p<0.05).

Conclusion

During an institutional involuntary quarantine, additional attention should be paid to vulnerable groups like females and individuals with a history of psychiatric illness. More than one-fourth of our sample experienced a negative psychological impact; therefore, coping practices like working out should be encouraged. This study contributes to the ongoing discussion about the psychological aspects of being quarantined. Much work remains to be done to identify strategies that prevent and mitigate psychological distress throughout the quarantine experience and to determine whether these impacts will last for an extended period of time.

Keywords: COVID-19, anxiety, IES, depression, knowledge, quarantine, psychological impact, stress, pandemic, Saudi Arabia

Introduction

The coronavirus disease 2019 (COVID-19) originated in China. It spread over the globe in just a few months, the number of infected people and deaths increasing as it went. Most countries in the world enacted partial or complete border closures to foreign nationals 1 and recommended citizens to self-isolate at home or in a dedicated quarantine facility.2 Isolation and quarantine help protect the public by preventing exposure to people who have or may have a contagious disease. Isolation is the separation of sick people with a contagious disease from people who are not sick, and quarantine is the separation and restricted movement of people who have been exposed to a contagious disease while observing them for symptoms. Before the recent COVID-19 outbreak, cities were quarantined in areas of Canada and China during the severe acute respiratory syndrome (SARS) pandemic in 2003, and in South Korea in 2015 during the Middle East respiratory syndrome coronavirus (MERS-CoV).3 Quarantine has been confirmed as an effective tool for preventing and controlling an influenza pandemic.4 However, it may cause people high levels of stress. Fear and anxiety about a disease can be overwhelming. Previous studies also reported that quarantine could cause psychological harm; a study conducted in Taiwan investigated stress reactions among 338 staff members in a hospital and found that 5% suffered from an acute stress disorder, 20% felt stigmatized and rejected in their neighborhood because of their hospital work, and 20 out of 218 (9%) health-care workers reported reluctance to work or had considered resignation.5 In another study conducted in Australia during an equine influenza outbreak, approximately 34% of participants reported high psychological distress, compared to levels of around 12% in the general Australian population. Another study in China during the SARS outbreak examined symptoms of depression in hospital staff three years after being quarantined and found 9% (48 of 549) of all sampled participants had high depressive symptoms. Of this group, 60% (29 of 48) of them had been quarantined, while only 15% (63 of 424) of participants with low depressive symptom had been quarantined.

During the current COVID-19 pandemic, a nationwide survey in Italy showed that female gender was associated with higher levels of psychological distress. Having an acquaintance infected with COVID-19 increased both depression and stress. A history of stressful situations and medical problems raised depression and anxiety levels as well. Some other factors found to increase anxiety and stress levels include having a family member infected with COVID-19, being young, and needing to leave one’s home to go to work.6 A recent study conducted in quarantine facility centers in India reported that depression, anxiety, and stress were found in 49.4%, 40.9%, and 75.8% of the residents, respectively.7

Quarantine, although designed to be protective of individual and public health, brings with it negative health impacts that need to be understood, prevented, and mitigated. The more public health officials understand the nature of the quarantine experience, the more it can be designed to reduce adverse impacts, promote protective factors, and optimize short and long-term outcomes from a mental and physical well-being perspective. This study contributes to the growing body of knowledge related to the impact of quarantine during widespread infectious diseases by exploring aspects of mental health at COVID-19 quarantine facilities.

Only a few studies have examined the degree of psychological impact on people experiencing involuntary quarantine inside an institutionalized facility, so the main objective of this study is to measure the effects of involuntary quarantine among individuals in quarantine-designated facilities during the COVID-19 pandemic in Saudi Arabia.

Methodology

Study Design, Setting, and Sampling

A cross-sectional study was conducted in order to assess the psychological responses of COVID-19 pandemic on individuals placed in institution-based quarantine facilities in the kingdom of Saudi Arabia. It was based on quarantined individuals in seven various hotels designated for quarantine purposes during the pandemic in Riyadh and Qassim regions of Saudi Arabia. Each facility constituted of covering medical doctors, nurses, social workers, and psychologists with number quarantined/isolated individuals ranged from 40 to 200 individuals in each facility; each individual was placed in a single room alone, reasons for quarantine ranged from being traveler returning to the kingdom to a confirmed cased with mild disease. Each individual was observed for vital signs at least twice a day, with necessary medication dispersion whenever there is a need by the medical doctor on duty. Individuals were quarantined for a planned period of 14 days, and the survey was distributed in the 8th-day quarantine period.

We used an online-based questionnaire provided by google survey platform (Google LLC, Mountain View, California, USA), the survey distributed to quarantined individuals through small cards with letters explaining the study purpose, objectives, time needed for completion, and inviting them to participate. A quick response bar-code was provided in order to scan and participate in the study. Physical distribution was not feasible in order to minimize contact. All quarantined individuals in the selected facilities who fit inclusion criteria were invited to participate. Inclusion criteria were adult (>18 years), Arabic speaker, Saudi citizen or resident of Saudi Arabia, and being quarantined or isolated in the facility. The study was conducted in accordance with the Declaration of Helsinki protocol and was approved by the Qassim Institutional Review Regional Ethical Committee (No.H-04-Q-001). The study aims and objectives were explained to the respondents and they provided written informed consent. Data confidentiality was preserved throughout the study and was not breached unless for study needs. Data collected over a period from 29 April to19 May 2020, during a curfew was in place in the kingdom. We calculated sample size using Epi Info™ 7 (Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA). The calculation resulted in 192 respondents based on expected % prevalence (p) of 14.6% of respondents reported a score >20 on the IES-R scale during SARS quarantine,5% margin of error, and confidence interval (%) of 95%.8

Survey

The study tool was adopted primarily from a study conducted during the early days of COVID-19 outbreak throughout China.9 The self-administered survey covered several area including the following: Sociodemographic such as age, gender, educational level, being married or not, if having children, employment or being a medical sector employee, income per household in Saudi Riyal (1 USD=3.75 SAR), housing status and residents’ number in the house. The physical symptoms and health status as reported by quarantined individuals were also addressed (health status was rated from 1 to 5, where higher number meant better health status). History of chronic medical or psychiatric disorders were also reported. Visiting a hospital, tested for COVID-19 or being admitted in the past 14 days were included. Additionally, respondents were asked to report the time spent in quarantine/isolation and reasons for their placement in quarantine or isolation.

The questionnaire included items related to COVID-19 knowledge and practices. These items included transmission routes, source of information, practiced preventive measures against SARS-Cov-2 such as wearing masks, hand hygiene, cough etiquette and others. In order to address concerns experienced by quarantined individuals, confidence of available diagnostic test for SARS-Cov-2, level of satisfaction of available health information and if they felt that there is unnecessary worrisome with regard to COVID-19 were covered. Participants were asked about their chances of being infected and survival probability if they contracted the infection. An additional question on the coping strategies practiced during the quarantine, including sleeping a lot, engaging in work, and others, were included.

To achieve study aim, the Impact of Event Scale-Revised (IES-R) and Depression, Anxiety, and Stress Scale (DASS-21) were used. These scales were used in several studies assessing the psychological responses following other outbreaks such as SARS, MERS and COVID-19.9–11 The IES-R used to assess post traumatic symptoms experienced during the last week. It has been translated and validated in native Arabic speakers.12,13 This scale consists of 22 items, divided into three subscales mean avoidance, intrusion and hyperarousal.12 Quarantined individuals were asked to choose answers ranged from “not at all” to “extremely” (0 to 4). Scoring was applied as in Table 1.12

Table 1.

Scoring for Impact of Event Scale-Revised (IES-R) and Depression, Anxiety, and Stress Scale (DASS-21)

Interpretation Impact of Event Scale-Revised (IES-R) Depression Items: 3, 5, 10, 13, 16, 17, and 21 Anxiety Items: 2, 4, 7, 9, 15, 19, and 20 Stress Items: 1, 6, 8, 11, 12, 14, and 18
Normal 0–23 0–9 0–6 0–10
Mild 24–32 10–12 7–9 11–18
Moderate 33–36 13–20 10–14 19–26
Severe >37 21–27 15–19 27–34
Extremely severe NA 28–42 20–42 35–42
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To assess mental health status, Arabic version of 21 item DASS scale was used.14 This scale shown to be valid and reliable among Arabs.14 It consists of three subscales, namely depression, anxiety, and stress. Every single subscale provides a summary score for seven items. Responses to each item ranged from zero to three “did not apply to me” to “applied to me most of the time”. Scoring for each subscale was different from one another as illustrated in Table 1.15

Statistical Analysis

Data were collected from 214 participants and analyzed. Data were initially collected into a Microsoft Excel spreadsheet and later transferred into IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, N.Y., USA) for analysis. Descriptive analysis was carried out and the results presented in frequencies and percentages (for categorical data), and means and standard deviation (for continuous data). Chi-square tests (and Fischer’s exact tests where appropriate) were carried out to identify statistically significant relationships between psychological health and each of sociodemographic factors, health status/service utilization factors, symptoms, knowledge about COVID-19, precautionary measures, and coping mechanisms. Psychological health was determined using the 22-item IES-R which assessed for subjective psychological distress, and the 21-item DASS with its three subscales assessing for stress, anxiety, and depression. Both scales were found to have strong reliability with Cronbach’s alpha of 0.901 for the IES-R and 0.944 for the DASS. Similarly, the subscales of the DASS had strong reliability with each of the stress, anxiety, and depression subscales having Cronbach’s alpha of 0.895, 0.867, and 0.858, respectively. All tests were carried out at a level of significance of p < 0.05.

Results

Psychological Impacts of COVID-19 Quarantine

The average scores of the participants on the IES-R and the subscales of the DASS-21 were 23.3 ± 14.7 (IES-R), 9.3 ± 10.7 (DASS stress subscale), 4.8 ± 8.1 (DASS anxiety subscale), and 7.4 ± 9.0 (DASS depression subscale). The proportion of participants that met the criteria for stress, anxiety, and depression were 25.7%, 21.5%, and 32.7%, respectively. On the IES-R, 28.0% of the participants met the criteria for psychological distress (PTSD). It is important to note that the participants had varying degrees of severity of each of stress, anxiety, and depression, as summarized in Table 1.

Sociodemographic Characteristics and Influence on Psychological Response to the COVID-19 Pandemic

There were more males (59.8%) than females in the study sample, with the majority of the participants being in the 18–30 years age category (56.1%) and closely followed by those in the 31–40 years age category (30.4%, Table 2). More than half of the population had at least a bachelor’s degree (41.1%) or a master’s degree (23.8%). The split across marital lines was almost similar, with 49.5% married and 49.1% single. Of the respondents who were currently or previously married, the majority had between 1 and 3 children (63.3%). With regards to employment status, students constituted 45.3%, while 39.3% others were in one form of employment or the other. As much as 10.7% of the participants were working in the medical field and 29.0% had at least a family member who works in the medical field.

Table 2.

Association Between Sociodemographic Variables and the Psychological Impact/Adverse Mental Health Status of COVID-19 Quarantine (n = 214)

Variables N (%) Psychological Distress (IES-R) Stress (DASS) Anxiety (DASS) Depression (DASS)
Yes No p Yes No p Yes No p Yes No p
Sex
Male 128 (59.8%) 28 (21.9%) 100 (78.1%) 0.014 22 (17.2%) 106 (82.8%) 0.001 21 (16.4%) 107 (83.6%) 0.027 35 (27.3%) 93 (72.7%) 0.041
Female 86 (40.2%) 32 (37.2%) 54 (62.8%) 33 (38.4%) 53 (61.6%) 25 (29.1%) 61 (70.9%) 35 (40.7%) 51 (59.3%)
Age
18–30 years 120 (56.1%) 42 (35.0%) 78 (65.0%) 0.039 37 (30.8%) 83 (69.2%) 0.194 30 (25.0%) 90 (75.0%) 0.135 47 (39.2%) 73 (60.8%) 0.024
31–40 years 65 (30.4%) 15 (23.1%) 50 (76.9%) 15 (23.1%) 50 (76.9%) 15 (23.1%) 50 (76.9%) 21 (32.3%) 44 (67.7%)
41–50 years 16 (7.5%) 0 (0.0%) 16 (100.0%) 1 (6.3%) 15 (93.8%) 0 (0.0%) 16 (100.0%) 1 (6.3%) 15 (93.8%)
51–60 years 9 (4.2%) 2 (22.2%) 7 (77.8%) 1 (11.1%) 8 (88.9%) 1 (11.1%) 8 (88.9%) 1 (11.1%) 8 (88.9%)
>60 years 4 (1.9%) 1 (25.0%) 3 (75.0%) 1 (25.0%) 3 (75.0%) 0 (0.0%) 4 (100.0%) 0 (0.0%) 4 (100.0%)
Educational level
Middle school 2 (0.9%) 0 (0.0%) 2 (100.0%) 0.186 0 (0.0%) 2 (100.0%) 0.824 0 (0.0%) 2 (100.0%) 0.573 0 (0.0%) 2 (100.0%) 0.471
High school 44 (20.6%) 18 (40.9%) 26 (59.1%) 9 (20.5%) 35 (79.5%) 7 (15.9%) 37 (84.1%) 18 (40.9%) 26 (59.1%)
Diploma 19 (8.9%) 4 (21.1%) 15 (78.9%) 6 (31.6%) 13 (68.4%) 6 (31.6%) 13 (68.4%) 6 (31.6%) 13 (68.4%)
University 88 (41.1%) 21 (23.9%) 67 (76.1%) 25 (28.4%) 63 (71.6%) 17 (19.3%) 71 (80.7%) 31 (35.2%) 57 (64.8%)
Master 51 (23.8%) 16 (31.4%) 35 (68.6%) 13 (25.5%) 38 (74.5%) 14 (27.5%) 37 (72.5%) 12 (23.5%) 39 (76.5%)
PhD 10 (4.7%) 1 (10.0%) 9 (90.0%) 2 (20.0%) 8 (80.0%) 2 (20.0%) 8 (80.0%) 3 (30.0%) 7 (70.0%)
Marital status
Single 105 (49.1%) 37 (35.2%) 68 (64.8%) 0.096 34 (32.4%) 71 (67.6%) 0.144 28 (26.7%) 77 (73.3%) 0.189 43 (41.0%) 62 (59.0%) 0.022
Married 106 (49.5%) 22 (20.8%) 84 (79.2%) 21 (19.8%) 85 (80.2%) 17 (16.0%) 89 (84.0%) 25 (23.6%) 81 (76.4%)
Divorced 2 (0.9%) 1 (50.0%) 1 (50.0%) 0 (0.0%) 2 (100.0%) 1 (50.0%) 1 (50.0%) 1 (50.0%) 1 (50.0%)
Widowed 1 (0.5%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 1 (100.0%) 1 (100.0%) 0 (0.0%)
Number of children
None 17 (15.6%) 0 (0.0%) 17 (100.0%) 0.014 1 (5.9%) 16 (94.1%) 0.080 2 (11.8%) 15 (88.2%) 0.133 1 (5.9%) 16 (94.1%) 0.003
1–3 children 69 (63.3%) 21 (30.4%) 48 (69.6%) 19 (27.5%) 50 (72.5%) 16 (23.2%) 53 (76.8%) 25 (36.2%) 44 (63.8%)
4–6 children 20 (18.3%) 1 (5.0%) 19 (95.0%) 1 (5.0%) 19 (95.0%) 0 (0.0%) 20 (100.0%) 0 (0.0%) 20 (100.0%)
7–10 children 2 (1.8%) 1 (50.0%) 1 (50.0%) 0 (0.0%) 2 (100.0%) 0 (0.0%) 2 (100.0%) 1 (50.0%) 1 (50.0%)
> 10 children 1 (0.9%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 1 (100.0%)
Employment status
Employed 84 (39.3%) 20 (23.8%) 64 (76.2%) 0.819 24 (28.6%) 60 (71.4%) 0.518 22 (26.2%) 62 (73.8%) 0.503 31 (36.9%) 53 (63.1%) 0.210
Self-employed 4 (1.9%) 1 (25.0%) 3 (75.0%) 0 (0.0%) 4 (100.0%) 1 (25.0%) 3 (75.0%) 2 (50.0%) 2 (50.0%)
Student 97 (45.3%) 31 (32.0%) 66 (68.0%) 26 (26.8%) 71 (73.2%) 20 (20.6%) 77 (79.4%) 32 (33.0%) 65 (67.0%)
Unemployed 21 (9.8%) 6 (28.6%) 15 (71.4%) 3 (14.3%) 18 (85.7%) 2 (9.5%) 19 (90.5%) 5 (23.8%) 16 (76.2%)
Retired 8 (3.7%) 2 (25.0%) 6 (75.0%) 2 (25.0%) 6 (75.0%) 1 (12.5%) 7 (87.5%) 0 (0.0%) 8 (100.0%)
Working in the medical field
Yes 23 (10.7%) 3 (13.0%) 20 (87.0%) 0.090 4 (17.4%) 19 (82.6%) 0.334 3 (13.0%) 20 (87.0%) 0.296 5 (21.7%) 18 (78.3%) 0.235
No 191 (89.3%) 57 (29.8%) 134 (70.2%) 51 (26.7%) 140 (73.3%) 43 (22.5%) 148 (77.5%) 65 (34.0%) 126 (66.0%)
Have a family member that works in the medical field
Yes 62 (29.0%) 14 (22.6%) 48 (77.4%) 0.256 18 (29.0%) 44 (71.0%) 0.476 14 (22.6%) 48 (77.4%) 0.805 15 (24.2%) 47 (75.8%) 0.090
No 152 (71.0%) 46 (30.3%) 106 (69.7%) 37 (24.3%) 115 (75.7%) 32 (21.1%) 120 (78.9%) 55 (36.2%) 97 (63.8%)
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Note: Bolded p-values are significant.

Across the scales, the factors listed above were tested to identify which had a statistically significant relationship with stress, anxiety, and depression on the DASS and subjective psychological distress on the IES-R. On the IES-R, sex, age, number of children, and the number of individuals residing in the household had statistically significant influence on the prevalence of subjective psychological distress (p < 0.05). Participants who were aged between 18 and 30 years (p = 0.039), females (p = 0.014), were significantly more likely to have been distressed, as shown on the IES-R. Being female (p = 0.001), were found to be significantly associated with stress on the appropriate subscale of the DASS. Similarly, participants who are females (p = 0.027) are significantly more likely to suffer from anxiety.

Using the depression subscale of the DASS, those who were females (p = 0.041), within the age category of 18 to 30 years (p = 0.024), single (p = 0.022), having between 1 and 3 children (p = 0.003), and living with more than three people in the same household (p = 0.039) were significantly more likely to have depression. Factors such as educational level, employment status, family monthly income, working in the medical field, or having a family member that works in the medical field did not have any statistically significant relationship with subjective distress (on the IES-R), stress, anxiety, or depression on the DASS.(Supplementary Table 1 and 3)

Health Status/Service Utilization and Symptoms Effect on Mental Health Status

Looking at the relationships between these variables and participants expression of psychological distress, stress, anxiety, and depression (Table 3); all variables had a statistically significant relationship with one or more of psychological distress, stress, anxiety, and depression (p < 0.05) except for having being diagnosed with a chronic disease, being tested for COVID-19 in the preceding 14 days, and reasons for the quarantine. On the IES-R, participants who noted that they had average health (p < 0.001) and those who had previously been diagnosed with a psychiatric disorder (p < 0.001) were significantly more likely to have psychological distress than their counterparts. On the stress subscale of the DASS, participants who believe their health status is average (p < 0.001), and have been previously diagnosed with a mental health disorder (p < 0.001) were significantly more likely to exhibit stress. Similarly, anxiety was significantly associated with being of good to average health (p < 0.001), previous diagnosis with a psychiatric disorder (p = 0.001), and visiting a hospital in the preceding 14 days (as assessed using the anxiety subscale of the DASS; p = 0.001).

Table 3.

Relationship Between Health Status/Health Service Utilization and the Psychological Impact/Adverse Mental Health Status of COVID-19 Quarantine (n = 214)

Variables N (%) Psychological Distress (IES-R) Stress (DASS) Anxiety (DASS) Depression (DASS)
Yes No p Yes No p Yes No p Yes No p
Self-evaluation of health status
Poor 4 (1.9%) 4 (100.0%) 0 (0.0%) 0.000 4 (100.0%) 0 (0.0%) 0.000 4 (100.0%) 0 (0.0%) 0.000 4 (100.0%) 0 (0.0%) 0.007
Average 15 (7.0%) 10 (66.7%) 5 (33.3%) 6 (40.0%) 9 (60.0%) 4 (26.7%) 11 (73.3%) 7 (46.7%) 8 (53.3%)
Good 49 (22.9%) 11 (22.4%) 38 (77.6%) 17 (34.7%) 32 (65.3%) 15 (30.6%) 34 (69.4%) 19 (38.8%) 30 (61.2%)
Very good 146 (68.2%) 35 (24.0%) 111 (76.0%) 28 (19.2%) 118 (80.8%) 23 (15.8%) 123 (84.2%) 40 (27.4%) 106 (72.6%)
Suffering from chronic disease
Yes 20 (9.3%) 6 (30.0%) 14 (70.0%) 0.837 7 (35.0%) 13 (65.0%) 0.318 4 (20.0%) 16 (80.0%) 0.864 8 (40.0%) 12 (60.0%) 0.466
No 194 (90.7%) 54 (27.8%) 140 (72.2%) 48 (24.7%) 146 (75.3%) 42 (21.6%) 152 (78.4%) 62 (32.0%) 132 (68.0%)
Diagnosed with a psychiatric disorder
Yes 17 (7.9%) 15 (88.2%) 2 (11.8%) 0.000 15 (88.2%) 2 (11.8%) 0.000 9 (52.9%) 8 (47.1%) 0.001 15 (88.2%) 2 (11.8%) 0.000
No 197 (92.1%) 45 (22.8%) 152 (77.2%) 40 (20.3%) 157 (79.7%) 37 (18.8%) 160 (81.2%) 55 (27.9%) 142 (72.1%)
Visited a hospital in the past 14 days
Yes 23 (10.7%) 9 (39.1%) 14 (60.9%) 0.210 8 (34.8%) 15 (65.2%) 0.291 11 (47.8%) 12 (52.2%) 0.001 11 (47.8%) 12 (52.2%) 0.102
No 191 (89.3%) 51 (26.7%) 140 (73.3%) 47 (24.6%) 144 (75.4%) 35 (18.3%) 156 (81.7%) 59 (30.9%) 132 (69.1%)
Admitted in a hospital within the last 14 days
Yes 4 (1.9%) 1 (25.0%) 3 (75.0%) 0.891 2 (50.0%) 2 (50.0%) 0.262 2 (50.0%) 2 (50.0%) 0.161 4 (100.0%) 0 (0.0%) 0.004
No 210 (98.1%) 59 (28.1%) 151 (71.9%) 53 (25.2%) 157 (74.8%) 44 (21.0%) 166 (79.0%) 66 (31.4%) 144 (68.6%)
Tested for COVID in the past 14 days
Yes 157 (73.4%) 44 (28.0%) 113 (72.0%) 0.995 44 (28.0%) 113 (72.0%) 0.197 33 (21.0%) 124 (79.0%) 0.778 53 (33.8%) 104 (66.2%) 0.588
No 57 (26.6%) 16 (28.1%) 41 (71.9%) 11 (19.3%) 46 (80.7%) 13 (22.8%) 44 (77.2%) 17 (29.8%) 40 (70.2%)
Reason for quarantine
Diagnosed with COVID 2 (0.9%) 1 (50.0%) 1 (50.0%) 0.750 0 (0.0%) 2 (100.0%) 0.654 1 (50.0%) 1 (50.0%) 0.604 1 (50.0%) 1 (50.0%) 0.514
Close contact with COVID case 35 (16.4%) 9 (25.7%) 26 (74.3%) 10 (28.6%) 25 (71.4%) 7 (20.0%) 28 (80.0%) 14 (40.0%) 21 (60.0%)
Coming from outside Saudi Arabia 177 (82.7%) 50 (28.2%) 127 (71.8%) 45 (25.4%) 132 (74.6%) 38 (21.5%) 139 (78.5%) 55 (31.1%) 122 (68.9%)
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Note: Bolded p-values are significant.

Depression also appeared to be strongly associated with similar factors. Participants who were of average health (p = 0.007), been previously diagnosed with a psychiatric disorder (p < 0.001), and/or admitted in a hospital in the last 14 days (p = 0.004) had a significantly increased risk of depression (DASS-Depression subscale).

Participants were asked questions relating to symptoms they had experienced in the few days preceding the study (Table 4), the most predominant symptoms experienced were dizziness (5.6%), muscle pain (5.1%).

Table 4.

Relationship Between Symptoms and the Psychological Impact/Adverse Mental Health Status of COVID-19 Quarantine (n = 153)

Variables N (%) PTSD (IES-R) Stress (DASS) Anxiety (DASS) Depression (DASS)
Yes No p Yes No p Yes No p Yes No p
Symptoms
Cough
Yes 2 (0.9%) 0 (0.0%) 2 (100.0%) 0.375 0 (0.0%) 2 (100.0%) 0.403 0 (0.0%) 2 (100.0%) 0.457 0 (0.0%) 2 (100.0%) 0.322
No 212 (99.1%) 60 (28.3%) 152 (71.7%) 55 (25.9%) 157 (74.1%) 46 (21.7%) 166 (78.3%) 70 (33.0%) 142 (67.0%)
Diarrhea
Yes 5 (2.3%) 3 (60.0%) 2 (40.0%) 0.107 3 (60.0%) 2 (40.0%) 0.076 4 (80.0%) 1 (20.0%) 0.001 2 (40.0%) 3 (60.0%) 0.725
No 209 (97.7%) 57 (27.3%) 152 (72.7%) 52 (24.9%) 157 (75.1%) 42 (20.1%) 167 (79.9%) 68 (32.5%) 141 (67.5%)
Difficulty with breathing
Yes 10 (4.7%) 6 (60.0%) 4 (40.0%) 0.021 5 (50.0%) 5 (50.0%) 0.072 5 (50.0%) 5 (50.0%) 0.025 6 (60.0%) 4 (40.0%) 0.060
No 204 (95.3%) 54 (26.5%) 150 (73.5%) 50 (24.5%) 154 (75.5%) 41 (20.1%) 163 (79.9%) 64 (31.4%) 140 (68.6%)
Headache
Yes 10 (4.7%) 5 (50.0%) 5 (50.0%) 0.113 4 (40.0%) 6 (60.0%) 0.289 5 (50.0%) 5 (50.0%) 0.025 5 (50.0%) 5 (50.0%) 0.233
No 204 (95.3%) 55 (27.0%) 149 (73.0%) 51 (25.0%) 153 (75.0%) 41 (20.1%) 163 (79.9%) 65 (31.9%) 139 (68.1%)
Muscle pain
Yes 11 (5.1%) 5 (45.5%) 6 (54.5%) 0.187 5 (45.5%) 6 (54.5%) 0.124 7 (63.6%) 4 (36.4%) 0.000 7 (63.6%) 4 (36.4%) 0.025
No 203 (94.9%) 55 (27.1%) 148 (72.9%) 50 (24.6%) 153 (75.4%) 39 (19.2%) 164 (80.8%) 63 (31.0%) 140 (69.0%)
Nasal congestion
Yes 4 (1.9%) 3 (75.0%) 1 (25.0%) 0.035 3 (75.0%) 1 (25.0%) 0.023 4 (100.0%) 0 (0.0%) 0.000 0 (0.0%) 4 (100.0%) 0.159
No 210 (98.1%) 57 (27.1%) 153 (72.9%) 52 (24.8%) 158 (75.2%) 42 (20.0%) 168 (80.0%) 70 (33.3%) 140 (66.7%)
Palpitation
Yes 1 (0.5%) 0 (0.0%) 1 (100.0%) 0.532 0 (0.0%) 1 (100.0%) 0.556 1 (100.0%) 0 (0.0%) 0.055 1 (100.0%) 0 (0.0%) 0.151
No 213 (99.5%) 60 (28.2%) 153 (71.8%) 55 (25.8%) 158 (74.2%) 45 (21.1%) 168 (78.9%) 69 (32.4%) 144 (67.6%)
Sore throat
Yes 4 (1.9%) 1 (25.0%) 3 (75.0%) 0.891 1 (25.0%) 3 (75.0%) 0.974 0 (0.0%) 4 (100.0%) 0.291 3 (75.0%) 1 (25.0%) 0.069
No 210 (98.1%) 59 (28.1%) 151 (71.9%) 54 (25.7%) 156 (74.3%) 46 (21.9%) 164 (78.1%) 67 (31.9%) 143 (68.1%)
Fever
Yes 1 (0.5%) 0 (0.0%) 1 (100.0%) 0.532 0 (0.0%) 1 (100.0%) 0.556 0 (0.0%) 1 (100.0%) 0.600 0 (0.0%) 1 (100.0%) 0.485
No 213 (99.5%) 60 (28.2%) 153 (71.8%) 55 (25.8%) 158 (74.2%) 46 (21.6%) 167 (78.4%) 70 (32.9%) 143 (67.1%)
Dizziness
Yes 12 (5.6%) 8 (66.7%) 4 (33.3%) 0.002 7 (58.3%) 5 (41.7%) 0.008 8 (66.7%) 4 (33.3%) 0.000 6 (50.0%) 6 (50.0%) 0.189
No 202 (94.4%) 52 (25.7%) 150 (74.3%) 48 (23.8%) 154 (76.2%) 38 (18.8%) 164 (81.2%) 64 (31.7%) 138 (68.3%)
No symptoms
Yes 182 (85.0%) 46 (25.3%) 136 (74.7%) 0.032 42 (23.1%) 140 (76.9%) 0.036 29 (15.9%) 153 (84.1%) 0.000 55 (30.2%) 127 (69.8%) 0.064
No 32 (15.0%) 14 (43.8%) 18 (56.3%) 13 (40.6%) 19 (59.4%) 17 (53.1%) 15 (46.9%) 15 (46.9%) 17 (53.1%)
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Note: Bolded p-values are significant.

Knowledge About COVID-19 and Psychological Impact

On the relationship between knowledge/concerns about COVID-19 and the psychological impact/adverse mental health status of COVID-19 quarantine (Table 5), the majority of the participants correctly indicated that coronavirus was transmitted via exposure to droplets from sneezing and coughing and direct contact with infected surfaces (94.4%). However, this knowledge had no significant impact on whether they had psychological distress, stress, anxiety, and depression. The participants had strong confidence in the available options of diagnosing the disease, with 86.0% of them ranking high to very high on their confidence level. This had only a significant impact on stress and depression as those who had average confidence levels were more likely to suffer from stress (p = 0.014) and/or depression (p = 0.002) compared with their counterparts with stronger confidence levels. On satisfaction with the amount of information available about the disease, 77.6% of the participants were satisfied or very satisfied. This factor had a statistically significant influence on psychological distress, stress, and depression as those who were averagely satisfied with the amount of information available on COVID-19 were more likely to have psychological distress (p = 0.002), stress (p = 0.001), and/or depression (p = 0.001) compared with those who were very satisfied. Being up to date on information about COVID-19 by 87.9% of the participants had no significant influence on psychological distress, stress, anxiety, or depression.

Table 5.

Relationship Between Knowledge/Concerns About COVID-19 and the Psychological Impact/Adverse Mental Health Status of COVID-19 Quarantine (n = 214)

Variables N (%) Psychological Distress (IES-R) Stress (DASS) Anxiety (DASS) Depression (DASS)
Yes No p Yes No p Yes No p Yes No p
Knowledge of the mode of transmission of the coronavirus
Exposure to droplets, and direct contact with infected surfaces 202 (94.4%) 57 (28.2%) 145 (71.8%) 0.790 54 (26.7%) 148 (73.3%) 0.295 45 (22.3%) 157 (77.7%) 0.407 68 (33.7%) 134 (66.3%) 0.476
Through the air 6 (2.8%) 1 (16.7%) 5 (83.3%) 1 (16.7%) 5 (83.3%) 1 (16.7%) 5 (83.3%) 1 (16.7%) 5 (83.3%)
I do not know 6 (2.8%) 2 (33.3%) 4 (66.7%) 0 (0.0%) 6 (100.0%) 0 (0.0%) 6 (100.0%) 1 (16.7%) 5 (83.3%)
Confidence about options of diagnosing the disease
Not confident 1 (0.5%) 0 (0.0%) 1 (100.0%) 0.217 1 (100.0%) 0 (0.0%) 0.014 0 (0.0%) 1 (100.0%) 0.078 1 (100.0%) 0 (0.0%) 0.002
Slightly confident 2 (0.9%) 0 (0.0%) 2 (100.0%) 0 (0.0%) 2 (100.0%) 0 (0.0%) 2 (100.0%) 1 (50.0%) 1 (50.0%)
Moderately confident 27 (12.6%) 8 (29.6%) 19 (70.4%) 9 (33.3%) 18 (66.7%) 8 (29.6%) 19 (70.4%) 12 (44.4%) 15 (55.6%)
Very confident 73 (34.1%) 27 (37.0%) 46 (63.0%) 26 (35.6%) 47 (64.4%) 22 (30.1%) 51 (69.9%) 33 (45.2%) 40 (54.8%)
Extremely confident 111 (51.9%) 25 (22.5%) 86 (77.5%) 19 (17.1%) 92 (82.9%) 16 (14.4%) 95 (85.6%) 23 (20.7%) 88 (79.3%)
Satisfaction with the amount of information on the disease
Not satisfied 3 (1.4%) 2 (66.7%) 1 (33.3%) 0.002 2 (66.7%) 1 (33.3%) 0.001 2 (66.7%) 1 (33.3%) 0.089 2 (66.7%) 1 (33.3%) 0.001
Slightly satisfied 4 (1.9%) 2 (50.0%) 2 (50.0%) 2 (50.0%) 2 (50.0%) 1 (25.0%) 3 (75.0%) 3 (75.0%) 1 (25.0%)
Moderately satisfied 41 (19.2%) 15 (36.6%) 26 (63.4%) 14 (34.1%) 27 (65.9%) 11 (26.8%) 30 (73.2%) 18 (43.9%) 23 (56.1%)
Very satisfied 74 (34.6%) 28 (37.8%) 46 (62.2%) 26 (35.1%) 48 (64.9%) 19 (25.7%) 55 (74.3%) 31 (41.9%) 43 (58.1%)
Extremely satisfied 92 (43.0%) 13 (14.1%) 79 (85.9%) 11 (12.0%) 81 (88.0%) 13 (14.1%) 79 (85.9%) 16 (17.4%) 76 (82.6%)
Up to date on info about COVID-19
Yes 188 (87.9%) 50 (26.6%) 138 (73.4%) 0.207 51 (27.1%) 137 (72.9%) 0.199 39 (20.7%) 149 (79.3%) 0.472 64 (34.0%) 124 (66.0%) 0.264
No 26 (12.1%) 10 (38.5%) 16 (61.5%) 4 (15.4%) 22 (84.6%) 7 (26.9%) 19 (73.1%) 6 (23.1%) 20 (76.9%)
Have a relative diagnosed with the emerging coronavirus
Yes 17 (7.9%) 5 (29.4%) 12 (70.6%) 0.895 2 (11.8%) 15 (88.2%) 0.171 2 (11.8%) 15 (88.2%) 0.309 2 (11.8%) 15 (88.2%) 0.055
No 197 (92.1%) 55 (27.9%) 142 (72.1%) 53 (26.9%) 144 (73.1%) 44 (22.3%) 153 (77.7%) 68 (34.5%) 129 (65.5%)
You have been diagnosed with the emerging coronavirus
Yes 18 (8.4%) 5 (27.8%) 13 (72.2%) 0.980 3 (16.7%) 15 (83.3%) 0.359 2 (11.1%) 16 (88.9%) 0.262 4 (22.2%) 14 (77.8%) 0.322
No 196 (91.6%) 55 (28.1%) 141 (71.9%) 52 (26.5%) 144 (73.5%) 44 (22.4%) 152 (77.6%) 66 (33.7%) 130 (66.3%)
Perceived likelihood of infection during the current outbreak of the coronavirus
Very unlikely 115 (58.1%) 34 (29.6%) 81 (70.4%) 0.819 27 (23.5%) 88 (76.5%) 0.370 20 (17.4%) 95 (82.6%) 0.009 29 (25.2%) 86 (74.8%) 0.040
Unlikely 35 (17.7%) 10 (28.6%) 25 (71.4%) 8 (22.9%) 27 (77.1%) 6 (17.1%) 29 (82.9%) 14 (40.0%) 21 (60.0%)
Neutral 42 (21.2%) 9 (21.4%) 33 (78.6%) 15 (35.7%) 27 (64.3%) 15 (35.7%) 27 (64.3%) 21 (50.0%) 21 (50.0%)
Likely 4 (2.0%) 1 (25.0%) 3 (75.0%) 2 (50.0%) 2 (50.0%) 1 (25.0%) 3 (75.0%) 2 (50.0%) 2 (50.0%)
Extremely likely 2 (1.0%) 1 (50.0%) 1 (50.0%) 1 (50.0%) 1 (50.0%) 2 (100.0%) 0 (0.0%) 1 (50.0%) 1 (50.0%)
Chances of recovering if diagnosed with COVID-19
Very unlikely 6 (2.8%) 2 (33.3%) 4 (66.7%) 0.271 2 (33.3%) 4 (66.7%) 0.055 3 (50.0%) 3 (50.0%) 0.258 2 (33.3%) 4 (66.7%) 0.000
Unlikely 4 (1.9%) 0 (0.0%) 4 (100.0%) 1 (25.0%) 3 (75.0%) 0 (0.0%) 4 (100.0%) 2 (50.0%) 2 (50.0%)
Neutral 26 (12.1%) 8 (30.8%) 18 (69.2%) 9 (34.6%) 17 (65.4%) 6 (23.1%) 20 (76.9%) 13 (50.0%) 13 (50.0%)
Likely 63 (29.4%) 23 (36.5%) 40 (63.5%) 23 (36.5%) 40 (63.5%) 16 (25.4%) 47 (74.6%) 34 (54.0%) 29 (46.0%)
Extremely likely 115 (53.7%) 27 (23.5%) 88 (76.5%) 20 (17.4%) 95 (82.6%) 21 (18.3%) 94 (81.7%) 19 (16.5%) 96 (83.5%)
Opinion about COVID-concerns
Strongly disagree 99 (46.3%) 21 (21.2%) 78 (78.8%) 0.300 21 (21.2%) 78 (78.8%) 0.604 19 (19.2%) 80 (80.8%) 0.378 25 (25.3%) 74 (74.7%) 0.185
Disagree 47 (22.0%) 15 (31.9%) 32 (68.1%) 15 (31.9%) 32 (68.1%) 10 (21.3%) 37 (78.7%) 21 (44.7%) 26 (55.3%)
Undecided 43 (20.1%) 14 (32.6%) 29 (67.4%) 12 (27.9%) 31 (72.1%) 9 (20.9%) 34 (79.1%) 14 (32.6%) 29 (67.4%)
Agree 8 (3.7%) 3 (37.5%) 5 (62.5%) 3 (37.5%) 5 (62.5%) 4 (50.0%) 4 (50.0%) 3 (37.5%) 5 (62.5%)
Strongly agree 17 (7.9%) 7 (41.2%) 10 (58.8%) 4 (23.5%) 13 (76.5%) 4 (23.5%) 13 (76.5%) 7 (41.2%) 10 (58.8%)
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Note: Bolded p-values are significant.

Up to 7.9% of the participants had relatives diagnosed with the coronavirus, but this did not significantly influence their psychological health. However, 8.4% of the participants had been diagnosed with COVID-19, and interestingly, this was not found to have any statistically significant relationship with either psychological distress, stress, anxiety, or depression. With regards to the perceived likelihood of infection during the current outbreak, the majority of the participants believed that they were unlikely to be infected and this factor had significant influence on their psychological health as those who thought they were likely to get infected had higher chances of having anxiety (p = 0.009) and/or depression (p = 0.040). Similarly, the majority believed that even if they became infected, their chances of recovering were very high. Participants who did not know what their chances of recovering or who thought they were unlikely to recover were more likely to have depression (p < 0.001). (supplementary Table 2)

Precautionary Measures and Psychological Impact

Participants in the study had been using various means to protect themselves from getting infected with the coronavirus (Table 6). What the majority of them have been doing include washing and disinfecting hands (96.3%), avoiding handshakes (87.4%), using face masks (85.0%), social distancing (84.6%), and avoiding the sharing of utensils during meals (57.0%).

Table 6.

Relationship Between Participants’ Precautionary Measures and the Psychological Impact/Adverse Mental Health Status of COVID-19 Quarantine (n = 214)

Variables N (%) Psychological Distress (IES-R) Stress (DASS) Anxiety (DASS) Depression (DASS)
Yes No p Yes No p Yes No p Yes No p
How participants have protected themselves from COVID-19
Avoided sharing of utensils during meals
Yes 122 (57.0%) 37 (30.3%) 85 (69.7%) 0.390 30 (24.6%) 92 (75.4%) 0.669 29 (23.8%) 93 (76.2%) 0.351 37 (30.3%) 85 (69.7%) 0.392
No 92 (43.0%) 23 (25.0%) 69 (75.0%) 25 (27.2%) 67 (72.8%) 17 (18.5%) 75 (81.5%) 33 (35.9%) 59 (64.1%)
Avoided handshakes
Yes 187 (87.4%) 53 (28.3%) 134 (71.7%) 0.794 51 (27.3%) 136 (72.7%) 0.166 41 (21.9%) 146 (78.1%) 0.687 60 (32.1%) 127 (67.9%) 0.608
No 27 (12.6%) 7 (25.9%) 20 (74.1%) 4 (14.8%) 23 (85.2%) 5 (18.5%) 22 (81.5%) 10 (37.0%) 17 (63.0%)
Social distancing
Yes 181 (84.6%) 56 (30.9%) 125 (69.1%) 0.027 50 (27.6%) 131 (72.4%) 0.132 43 (23.8%) 138 (76.2%) 0.059 59 (32.6%) 122 (67.4%) 0.934
No 33 (15.4%) 4 (12.1%) 29 (87.9%) 5 (15.2%) 28 (84.8%) 3 (9.1%) 30 (90.9%) 11 (33.3%) 22 (66.7%)
Washing and disinfecting hands
Yes 206 (96.3%) 59 (28.6%) 147 (71.4%) 0.319 54 (26.2%) 152 (73.8%) 0.384 45 (21.8%) 161 (78.2%) 0.528 69 (33.5%) 137 (66.5%) 0.214
No 8 (3.7%) 1 (12.5%) 7 (87.5%) 1 (12.5%) 7 (87.5%) 1 (12.5%) 7 (87.5%) 1 (12.5%) 7 (87.5%)
Used a face mask
Yes 182 (85.0%) 56 (30.8%) 126 (69.2%) 0.034 48 (26.4%) 134 (73.6%) 0.591 42 (23.1%) 140 (76.9%) 0.179 59 (32.4%) 123 (67.6%) 0.828
No 32 (15.0%) 4 (12.5%) 28 (87.5%) 7 (21.9%) 25 (78.1%) 4 (12.5%) 28 (87.5%) 11 (34.4%) 21 (65.6%)
Did nothing
Yes 3 (1.4%) 0 (0.0%) 3 (100.0%) 0.276 0 (0.0%) 3 (100.0%) 0.305 0 (0.0%) 3 (100.0%) 0.361 0 (0.0%) 3 (100.0%) 0.224
No 211 (98.6%) 60 (28.4%) 151 (71.6%) 55 (26.1%) 156 (73.9%) 46 (21.8%) 165 (78.2%) 70 (33.2%) 141 (66.8%)
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Note: Bolded p-values are significant.

Regarding the relationships between these protective measures and psychological health, only the use of a face mask had any significant influence on the expression of psychological distress. Participants who used face masks were significantly more likely to have psychological distress compared with those who did not (p = 0.020).

Coping Mechanisms and Psychological Impact

A wide variety of coping mechanisms have been in use by participants to manage the stress associated with the COVID-19 quarantine (Table 7). The wide array of coping measures includes talking to important people in their lives (78.0%), engaging in religious practices (72.4%), watching TV (46.7%), sleeping (32.2%), working out (27.1%), eating (22.4%), and doing work-related tasks (19.6%). Other less commonly used coping measures include reading (2.3%), studying (1.9%), and participating in hobbies (0.9%). Some of these coping mechanisms significantly improved or worsened the psychological health of the participants. Using sleep as a coping mechanism increased the risk of anxiety (p = 0.028) and/or depression (p = 0.045). On the other hand, participants who did not participate in religious practices were more likely to have depression (p = 0.012); same as those who did not watch TV who were more likely to have psychological distress (p = 0.002), stress (p = 0.016), and/or anxiety (p = 0.030). Working out was also found to be protective as those who chose not to were more likely to suffer from psychological distress (p = 0.032).

Table 7.

Relationship Between Participants’ Coping Measures and the Psychological Impact/Adverse Mental Health Status of COVID-19 Quarantine (n = 214)

Variables N (%) PTSD (IES-R) Stress (DASS) Anxiety (DASS) Depression (DASS)
Yes No p Yes No p Yes No p Yes No p
Coping mechanisms used by participants
Hobbies
Yes 2 (0.9%) 1 (50.0%) 1 (50.0%) 0.487 1 (50.0%) 1 (50.0%) 0.429 1 (50.0%) 1 (50.0%) 0.324 1 (50.0%) 1 (50.0%) 0.601
No 212 (99.1%) 59 (27.8%) 153 (72.2%) 54 (25.5%) 158 (74.5%) 45 (21.2%) 167 (78.8%) 69 (32.5%) 143 (67.5%)
Doing work
Yes 42 (19.6%) 12 (28.6%) 30 (71.4%) 0.932 12 (28.6%) 30 (71.4%) 0.635 12 (28.6%) 30 (71.4%) 0.213 12 (28.6%) 30 (71.4%) 0.524
No 172 (80.4%) 48 (27.9%) 124 (72.1%) 43 (25.0%) 129 (75.0%) 34 (19.8%) 138 (80.2%) 58 (33.7%) 114 (66.3%)
Eating
Yes 48 (22.4%) 17 (35.4%) 31 (64.6%) 0.196 13 (27.1%) 35 (72.9%) 0.803 10 (20.8%) 38 (79.2%) 0.899 19 (39.6%) 29 (60.4%) 0.249
No 166 (77.6%) 43 (25.9%) 123 (74.1%) 42 (25.3%) 124 (74.7%) 36 (21.7%) 130 (78.3%) 51 (30.7%) 115 (69.3%)
Reading
Yes 5 (2.3%) 0 (0.0%) 5 (100.0%) 0.158 0 (0.0%) 5 (100.0%) 0.183 0 (0.0%) 5 (100.0%) 0.236 0 (0.0%) 5 (100.0%) 0.115
No 209 (97.7%) 60 (28.7%) 149 (71.3%) 55 (26.3%) 154 (73.7%) 46 (22.0%) 163 (78.0%) 70 (33.5%) 139 (66.5%)
Religious practices
Yes 155 (72.4%) 44 (28.4%) 111 (71.6%) 0.854 35 (22.6%) 120 (77.4%) 0.090 31 (20.0%) 124 (80.0%) 0.388 43 (27.7%) 112 (72.3%) 0.012
No 59 (27.6%) 16 (27.1%) 43 (72.9%) 20 (33.9%) 39 (66.1%) 15 (25.4%) 44 (74.6%) 27 (45.8%) 32 (54.2%)
Sleeping
Yes 69 (32.2%) 25 (36.2%) 44 (63.8%) 0.066 24 (34.8%) 45 (65.2%) 0.036 21 (30.4%) 48 (69.6%) 0.028 29 (42.0%) 40 (58.0%) 0.045
No 145 (67.8%) 35 (24.1%) 110 (75.9%) 31 (21.4%) 114 (78.6%) 25 (17.2%) 120 (82.8%) 41 (28.3%) 104 (71.7%)
Talked to important people
Yes 167 (78.0%) 49 (29.3%) 118 (70.7%) 0.423 43 (25.7%) 124 (74.3%) 0.976 32 (19.2%) 135 (80.8%) 0.117 53 (31.7%) 114 (68.3%) 0.567
No 47 (22.0%) 11 (23.4%) 36 (76.6%) 12 (25.5%) 35 (74.5%) 14 (29.8%) 33 (70.2%) 17 (36.2%) 30 (63.8%)
Studying
Yes 4 (1.9%) 0 (0.0%) 4 (100.0%) 0.208 1 (25.0%) 3 (75.0%) 0.974 0 (0.0%) 4 (100.0%) 0.291 0 (0.0%) 4 (100.0%) 0.159
No 210 (98.1%) 60 (28.6%) 150 (71.4%) 54 (25.7%) 156 (74.3%) 46 (21.9%) 164 (78.1%) 70 (33.3%) 140 (66.7%)
Watched TV
Yes 100 (46.7%) 18 (18.0%) 82 (82.0%) 0.002 18 (18.0%) 82 (82.0%) 0.016 15 (15.0%) 85 (85.0%) 0.030 32 (32.0%) 68 (68.0%) 0.836
No 114 (53.3%) 42 (36.8%) 72 (63.2%) 37 (32.5%) 77 (67.5%) 31 (27.2%) 83 (72.8%) 38 (33.3%) 76 (66.7%)
Working out
Yes 58 (27.1%) 10 (17.2%) 48 (82.8%) 0.032 11 (19.0%) 47 (81.0%) 0.169 8 (13.8%) 50 (86.2%) 0.094 14 (24.1%) 44 (75.9%) 0.103
No 156 (72.9%) 50 (32.1%) 106 (67.9%) 44 (28.2%) 112 (71.8%) 38 (24.4%) 118 (75.6%) 56 (35.9%) 100 (64.1%)
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Note: Bolded p-values are significant.

Discussion

The findings of this study contribute to the growing body of knowledge related to the impact of quarantine during widespread infectious diseases by exploring aspects of mental health during a COVID-19 quarantine. This study helps respond to the call for action put forth by Holmes et al. 2020.16

The rates of mental health symptoms found in this study are consistent with the observation that quarantine and lockdowns have a psychological impact. Psychological distress, depression, anxiety, insomnia, stigma, and low self-esteem have all been associated with quarantine.17 These associations provide a compelling argument for the establishment and implementation of protective measures in anticipation of both COVID-19’s “second wave” and future infectious outbreaks.

This is one of the few studies that assesses the psychological impact of involuntary quarantine during the COVID-19 pandemic. The participants met the criteria for stress, anxiety, and depression at a rate of 25.7%, 21.5%, and 32.7%, respectively, while 28.0% reported the psychological impact of quarantine as moderate or severe. These findings are consistent with a recent study which reported participants’ level of anxiety and depression to be 22.2% and 22.1%, respectively, for people under hotel quarantine.18 Another study conducted during an equine influenza outbreak in Australia found that 34% of quarantined horse owners reported psychological distress.19

An increased rate of psychological distress was found in single females. This finding is in keeping with other studies demonstrating that demographic factors can influence psychological responses to an infectious outbreak in the community. Taylor et al found that younger people were more at risk of psychological distress during Australia’s epidemic of equine flu.19 Guo et al found that female sex predicted higher perceived helplessness scores in a study of quarantined COVID-19 patients.9 Park et al reported that young age, female gender, and caregiver status were linked with increased stressor exposure and stressfulness during the COVID-19 pandemic.20 Younger orthopedic surgeons experienced more stress during a COVID-19 lockdown in India than older orthopedic surgeons.21

This study did not identify health-care worker status as being a risk factor for increased mental health symptoms in response to quarantine. However, many authors have documented the negative psychological sequelae of quarantine or lockdown on health-care providers.21–23

This study found an association between face mask use and psychological distress symptoms. As a tangible and visible reminder of the infectious outbreak, a face mask may act as a trigger for psychological distress. Likewise, a mask may invoke more stigma than washing hands or social distancing. Stigma has been identified as a source of psychological distress in several studies.9,24–26

A history of reported psychiatric disorder and self-reported poor health was predominant across all scales. This is consistent with findings during the MERS outbreak in South Korea that reported that stressors like quarantine or isolation might worsen the pre-existing mental illnesses.25,27 Self-reported poor health is known to be associated with signs of psychological distress; this study’s results demonstrate that respondents with poor/average self-reported health status report greater psychological distress, depression, anxiety, and stress symptoms than their counterparts.28 The association between physical and mental symptoms observed in this study is not specific to the quarantine or COVID-19. This relationship is well documented in the literature.29–32 In this study, more somatic symptoms were associated with anxiety than with stress, psychological distress, or depression. This finding may reflect an acute, fear-based response to experiencing symptoms indicative of a COVID-19 infection. Participants in this study who felt more likely to get infected with COVID-19 had an elevated risk of anxiety symptoms. This result is not unlike the reported association between perceived risk of infection and depression during the SARS epidemic.26

A diagnosis of COVID-19 did not result in higher rates of psychological distress, depression, anxiety, or stress. It may be that for some people the possibility of getting COVID-19 and the associated unknowns invoke more distress than the lived experience. Both unknowns and anticipation of a possible threat have been linked to anxiety.33 Lower rates of satisfaction with COVID-related information increased the likelihood of psychological distress, stress, and depression. Others have reported the negative impact of suboptimal information provision during outbreaks.22 Brooks et al 202034 noted that communication about quarantine rationale and requirements is vital to reducing the psychological stress of isolation. Findings like these have resulted in strong advocacy for providing timely and accurate communication to those significantly impacted by infectious outbreaks.25,35,36 Sahu et al21 recommended that open communication be prioritized to alleviate some of the stress experienced by doctors during the lockdown. Internet-based interventions during quarantine offer the opportunity to mitigate the distress of participants by addressing “unknowns.” Relevant and potentially reassuring information could be provided in concert with the psychological support provided by psychoeducation, coping skills, and mindfulness training.37

The level of engagement with precautionary measures in this study was high, and the percentage of complete nonparticipation low. However, even low rates of non-compliance can sabotage quarantine efforts and lower the beneficial impact of cooperation by others. High levels of targeted information about quarantine and the infectious outbreak may help lower the number of individuals who wholly or partially reject recommended safety measures.8,38 Additionally, compliance with the protective measures recommended by health authorities has been shown to be varied by the perceived benefit (or efficacy) of that recommended measure. This variation may also be influenced by one’s self-efficacy, ie one’s belief in their ability to accomplish a measure or action. Perceived risk of infection and one’s belief that they can make a difference in the community may influence that relationship and thereby influence the level of compliance practiced by the individuals.39

This study contributes to the understanding of coping mechanisms used during quarantine and their ability to provide psychological benefits. The exploration of this topic remains a priority for those working towards minimizing the negative impact of quarantine on well-being.40 The study’s results demonstrated a protective effect of religious activities against depression. Many religious practices include the concept of altruism or the “greater good.” Lui et al showed that an “altruistic acceptance of risk” was protective against depressive symptoms in those quarantined during the SARS outbreak. Explicitly encouraging altruistic behavior during quarantine may prove beneficial.38 Sleep can be an avoidance behavior. The link between avoidance and anxiety and depression41,42 may explain why some mental health symptoms were higher in those using sleep to manage quarantine-related stress. Park et al found distractions to be commonly used as a coping mechanism by American workers during the COVID-19 pandemic. Watching television and working out are distractions that provided some psychological benefits in this study.20 As physical activity can decrease during quarantine,43 overtly facilitating this coping mechanism through such avenues as online exercise programs may be beneficial. Others have suggested that in quarantine some people cope with stress by eating and drinking in an attempt to feel better (also known as “stress-related eating”).44

Internal resources may not be sufficient to offset the psychological impact of living through a pandemic and being quarantined. In response, Kang et al45 reported on an initiative that provided cognitive behavioral therapy, progressive muscular relaxation, mindfulness, relevant education, online self-help programs, and telephone crisis lines to address the mental distress associated with the COVID-19 quarantine. Telemedicine provides a venue to address deficits in adaptive, effective coping mechanisms within a quarantined or pandemic-affected population.37 Bäuerle et al and Balanzá-Martínez et al advocated for online health resources to address the mental health needs of the population during infectious outbreaks.40

This study shows increased levels of psychological distress, depression, anxiety, and stress symptoms among individuals in a quarantine-designated facility, indicating the importance of providing proper psychological support for those individuals, with more focus on vulnerable groups with a history of psychiatric illnesses or poor health status. It is also important to educate quarantined individuals with appropriate ways to cope with stress, such as working out or praying, and to provide proper tools to help them do that in the confined space of quarantine, if possible.

One of the study’s limitations is the cross-sectional design, which provides only a transient view of quarantined individuals’ psychological responses. Longitudinal studies might be needed to evaluate whether those responses will last after the quarantine/isolation is discontinued. Furthermore, this study utilized self-reported scales for the history of chronic diseases, psychiatric illnesses, and psychiatric symptoms, which were not confirmed by reviewing medical records or medical or psychiatric interviews; self-reporting may have limited reliability when compared to interviews conducted by a specialist. To minimize contact, researchers approached the respondents through cards with printed QR codes; this may have prevented some individuals from participating if they were unfamiliar with such a method. The study included seven selected facilities/hotels in two regions of the kingdoms, which might limit the generalizability of the study’s results to other regions or facilities run by institutions other than the ministry of health in the kingdom; however, this study provides an insight into the psychological impact of involuntarily quarantined individuals in designated facilities which are rarely reported in the literature.

Conclusions

In conclusion, during an institutional involuntary quarantine imposed by the government of Saudi Arabia, nearly one-fourth of the sampled population experienced a moderate or severe psychological impact. Females and single individuals had an increased rate of psychological distress. This study found that religious activities and exercise had a protective effect against depression and psychological distress. This study contributes to the ongoing conversation about the psychological aspects of being quarantined but much work remains to be done. The results of this study call not only for more research but also for the timely implementation of measures to prevent and mitigate the psychological distress seen in those living through the quarantine experience in designated facilities.

Acknowledgments

We would like to extend our thanks to all respondents and participants. Researchers would like to thank the Deanship of Scientific Research, Qassim University, for funding publication of this project.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Contributions

Conceptualization, Abdulmajeed Alkhamees and Moath Aljohani; Data curation, Moath Aljohani; Formal analysis, Abdulmajeed Alkhamees; Investigation, Abdulmajeed Alkhamees; Methodology, Abdulmajeed Alkhamees, Ali T. Alhabib, Mohammed A ALGHESEN and Moath Aljohani; Project administration, Abdulmajeed Alkhamees; Supervision, Abdulmajeed Alkhamees and Moath Aljohani; Writing – original draft, Abdulmajeed Alkhamees, Ali T. Alhabib, Mohammed A ALGHESEN and Moath Aljohani; Writing – review & editing, Abdulmajeed Alkhamees, Ali T. Alhabib, Mohammed A ALGHESEN and Moath Aljohani. All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare no conflicts of interest.

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