Abstract
The aim of this study is to investigate the effectiveness of a novel self-administered at-home daily virtual reality (VR)-based intervention (COVID Feel Good) for reducing the psychological burden experienced during the COVID-19 lockdown in Italy. A total of 40 individuals who had experienced at least two months of strict social distancing measures followed COVID Feel Good between June and July 2020 for one week. Primary outcome measures were depression, anxiety, and stress symptoms, perceived stress levels, and hopelessness. Secondary outcomes were the experienced social connectedness and the level of fear experienced during the COVID-19 pandemic. Linear mixed-effects models were fitted to evaluate the effectiveness of the intervention. Additionally, we also performed a clinical change analysis on primary outcome measures. As concerning primary outcome measures, participants exhibited improvements from baseline to post-intervention for depression levels, stress levels, general distress, and perceived stress (all p < 0.05) but not for the perceived hopelessness (p = 0.110). Results for the secondary outcomes indicated an increase in social connectedness from T0 to T1 (p = 0.033) but not a significant reduction in the perceived fear of coronavirus (p = 0.412). Among these study variables, these significant improvements were maintained from post-intervention to the 2-week follow-up (p > 0.05). Results indicated that the intervention was associated with good clinical outcomes, low-to-no risks for the treatment, and no adverse effects or risks. Globally, evidence suggests a beneficial effect of the proposed protocol and its current availability in 12 different languages makes COVID Feel Good a free choice for helping individuals worldwide to cope with the psychological distress associated with the COVID-19 crisis, although large scale trials are needed to evaluate its efficacy.
Highlights
In late 2019, the virus responsible for the severe acute respiratory syndrome coronavirus 2 COVID-19 was first identified in a small Chinese town [1,2] forcing the worldInt
The models that include the factor immersion reported higher values of Akaike’s information criteria (AIC) and Bayesian information criteria (BIC) parameters, suggesting that the models according to this formula:
The models that include the factor immersion reported higher values of AIC and BIC parameters, suggesting that the models according to this formula: =outcome ~ time, were the best fit for the data
Summary
In late 2019, the virus responsible for the severe acute respiratory syndrome coronavirus 2 COVID-19 was first identified in a small Chinese town [1,2] forcing the worldInt. In late 2019, the virus responsible for the severe acute respiratory syndrome coronavirus 2 COVID-19 was first identified in a small Chinese town [1,2] forcing the world. Res. Public Health 2021, 18, 8188 to face unprecedented medical, economic, and social challenges. After an uncontrollable diffusion of the disease from China to other countries, the World Health Organisation (WHO) declared the COVID-19 outbreak a ‘Public Health Emergency of International
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