The COVID-19-Related Lockdown in Qatar: Associations Among Demographics, Social Distancing, Mood Changes, and Quality of Life

Abstract

The worldwide spread of the coronavirus disease (COVID-19), the absence of medical treatment and vaccination, the delayed onset of symptoms, and the rapid human-to-human transmission have led the vast majority of countries to impose strict social distancing procedures. Whereas it appears that social distancing is an effective strategy for mitigating spread, it may also result in a variety of unintended negative consequences to individuals’ psychological well-being and mental health. During the peak of the COVID-19 pandemic, the present study examined associations among some demographic variables (gender, age, marital and working statuses, and having a family member or a friend infected with COVID-19), acceptance of social distancing, mood changes, and quality of life (QoL) in Qatar, a high-income Middle Eastern Arabic-speaking country. Older, married, and working participants were more accepting of social distancing than younger, unmarried, and non-working participants, respectively. Participants indicated that, during this time, they became more distressed, upset, scared, irritable, nervous, and afraid, and less inspired and determined. In a stark contrast, more individuals indicated that they became more interested, alert, and attentive, whereas higher percentages of participants reported feeling less guilty, hostile, and ashamed. Social distancing correlated positively with negative affect, whereas social avoidances correlated positively with positive affect and with physical, psychological, social, and environmental QoL. Finally, positive affect correlated positively, and negative affect correlated negatively, with these four domains of QoL. These results highlight the need for public health and clinical providers to consider peoples’ psychological well-being and mental health during the COVID-19 pandemic.

Investigating fear, depressive symptoms and coping mechanisms among Egyptian nursing students amidst the covid-19 pandemic: a cross-sectional study

“…In this context, the current study explored the relationship between fear of the coronavirus, coping strategies, and depressive symptoms among nursing students. Our results were interpreted in light of previous and similar literature that estimates the prevalence of mental health issues among healthcare workers [ 28 – 33 ].…”

impact of social distancing on the psychological quality of life during COVID-19 for Jordanian University students

“…As well, fourth year students were the most committed to social distancing instructions and were most physiological affected by social distancing in term of increased using of the internet and social aspects. Since these students were oldest and older ages were generally more content with the social distancing situation than the younger Megreya et al 2021). Also, may be due to that these students are in their last year at the university and of course their concerns about their future are high.…”

“…Another research study that was conducted by Megreya et al (2021), that evaluates the impacts of Covid-19 during the lockdown in Qatar that aimed to investigate the linkages between the various demographic variables, and its reflection on the social distancing situation and intended to explore the occurrences of the moon changes among the inhabitants, and the general quality of life. The results of the study were outstanding, as they revealed that respondents of older ages, also the married respondents, and the employed ones were generally more content with the social distancing situation than the younger, unmarried, and unemployed ones.…”

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

“…Data reported in Figure 10 can be collected from a variety of devices, apps, and search engines, to name a few. IoT data can be collected from wearable sensors [65], contents, and other related data can be gathered from e-commerce websites [66], mobile data can be gathered from mobile carriers/service-providers, social media data can be collected from SN service providers [67], historical data can be gathered from the hospitals websites/repositories [68], medical images and sounds data can be collected with wearable devices or automated machines, and demographics data can be obtained from trusted clinics/hospitals [69]. The logistic regression based models can assist in identifying hotspots in any territory based on the certain environment parameters and underlying conditions [70].…”