Effects of Online Attentional Bias Modification on Coronavirus Anxiety

Abstract

Objective: The negative impacts of the Coronavirus Disease 2019 (COVID-19)outbreak on public mental health are enhancing the number of individuals requiring psychotherapy. Besides, anxiety is becoming more frequent than any other mental health issue among individuals. Similar to other anxiety types, coronavirus anxiety is associated with elevated attentional bias. The present study aimed to examine the effects of Attentional Bias Modification (ABM) on attentional bias towards coronavirus-related stimuli, and coronavirus anxiety. Methods: This was a quasi-experimental study with pretest-posttest-follow-up and a control group design. The research participants were 37 adults (aged 18 to 38 y), who were randomly assigned to the experimental (n=18) and control (n=19) groups. The experimental group received ABM, while no intervention was delivered to the controls. Attentional bias and coronavirus disease anxiety were assessed at pretest, posttest, and 2 months follow-up stages using the Dot-Probe Task (DPT) and the Corona Disease Anxiety Scale (CDAS). The collected data were analyzed using two-way repeated-measures Analysis of Variance (ANOVA) and Fisher’s least significant difference in SPSS v. 26. Results: After receiving online ABM, the study participant’s coronavirus-related attentional bias decreased, leading to a significant reduction in coronavirus anxiety (P<0.05). These significant changes were evident at the posttest and maintained until the follow-up step. However, no significant changes occurred in the control group (P>0.05). Conclusion: The presented ABM could decrease coronavirus anxiety; thus, its online implementation is a suitable approach to treat individuals with this anxiety while observing social distancing.

Digital Therapy: Alleviating Anxiety and Depression in Adolescent Students During COVID-19 Online Learning - A Scoping Review

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Using what we know about threat reactivity models to understand mental health during the COVID-19 pandemic

“…Assessing, and ultimately intervening on, attentional bias is likely to require novel methods given that traditional methods of assessing attentional bias index have questionable psychometric properties and often conflate multiple distinct processes, e.g., orientation toward threat (i.e., vigilance) and disengagement from threat (Koster et al, 2004;MacLeod et al, 1986;Pettit et al, 2020). Metrics aiming to capture distinct attentional bias components may have superior psychometric properties to traditional metrics (Evans et al, 2018), are elevated in individuals with or at risk for anxiety disorders (Evans et al, 2016;Meissel et al, 2021), and may be differentially associated with COVID-related anxiety (Albery et al, 2021;Nasiry & Ameli, 2021). These findings from laboratory studies of attentional bias are generally consistent with results from self-report studies.…”

Psychometric Properties of COVID-19 Dot-probe Task in Iranian Adults

“…in corona anxiety (Hagerty & Williams, 2020;Loreta et al, 2020;Nasiry & Ameli, 2021;Schudy et al, 2020).…”

“…However, the reliability of the results of such studies depends on the reliability of the instruments used in the assessment of study variables. The most commonly utilized tool for the measurement of threat-related attentional bias is the dot-probe task (Schmukle, 2005) which has also been used for the measurement of corona-related attentional bias (Loreta et al, 2020;Nasiry & Ameli, 2021). The dot-probe task consists of trials in which pairs of threatful and neutral pictures are displayed on a computer screen for a short amount of time.…”

“…However, the relevant feature and the irrelevant feature variants of the visual search task show a medium level of reliability (Van Bockstaele, Salemink, Bögels, & Wiers, 2017) and lack any indications of validity (Van Bockstaele et al, 2020). Although the dot-probe task has been most commonly used in the studies investigating anxiety disorders, including corona anxiety (Loreta et al, 2020;Nasiry & Ameli, 2021), the standard version of this instrument shows poor psychometric properties and, therefore, might lead to inconsistent results across different studies (Cristea, Kok, & Cuijpers, 2015;Schmukle, 2005). To resolve this issue, a new version of this instrument with a response-based computational approach has been developed recently, which shows high levels of reliability and validity .…”