Predictors for interest to participate in digital mental health therapy: a cross-sectional survey of individuals with anxiety and depression

Abstract

Abstract Background Due to a multitude of factors, the onset of the COVID-19 pandemic resulted in a significant increase in mental health issues within society, including depression and anxiety. Due to the increased trend of mental health disorders in society, digital mental health therapies are more useful than ever. With the emergence of programs utilizing Internet Cognitive Behavioral Therapy (iCBT), mental health resources are easily accessible and can be widely implemented to those in need. The aim of this study was to identify predictors for interest to participate in SilverCloud digital mental health therapy among individuals with mild to severe anxiety and/or depression based on preliminary findings from the COVIDsmart study. Methods COVIDSmart participants who had moderate to severe anxiety and/or depression based on the PHQ-9 and GAD-7 scores, and who consented to be contacted for future studies, were invited to complete a needs assessment survey via Research Electronic Data Capture (REDCap). This assessment used self-reported measures including medical diagnoses, mental health services received, reasons for anxiety and/or depression, the use of coping strategies, suicidal ideology using the Ask Suicide Questions tool, and whether they would be interested in receiving free digital mental therapy. Descriptive statistics were used to report participants’ demographics and a logistic regression was used to find predictors for interest in participation in SilverCloud. SAS 9.4 was used and p values < 0.05 were considered significant. Results Out of the original 782 COVIDsmart participants, 634 consented to be contacted for future studies, 280 were subsequently invited to complete the SilverCloud needs assessment, and 120 individuals completed it. The largest demographic among these participants were females (70.83%) who identified as White (80.83%). The mean age was 48.74 years (SD = 14.66). Results revealed that having a mental health comorbidity significantly predicted the likelihood of interest in participating in the SilverCloud digital mental health program (p = 0.027). Conclusions In this study, mental illness comorbidities predicted the interest to participate in digital mental therapy. Fragmented healthcare and perceptions of unmet care needs are likely contributor factors. Further research with a diverse sample of participants is necessary for generalizability. Findings may have important implications for healthcare best practices.