JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic involved a prolonged period of collective trauma and stress during which substantial increases in mental health concerns, like depression and anxiety, were observed across the population.

Objective:

In this context, CHAMindWell was developed to improve resilience and reduce symptom severity among a public hospital system’s patient population. The program employed repeated, online symptom screenings using Computer Adaptive Testing for Mental Health (CAT-MH) surveys, and participants were assigned to a tier based on symptom severity (Tier 1 = asymptomatic to mild; Tier 2 = moderate; Tier 3 = severe). Participants were provided tier-based mindfulness resources, treatment recommendations, and referrals. This program evaluation was conducted to explore engagement with and outcomes from CHAMindWell.

Methods:

Demographic data and CAT-MH severity scores were retrospectively examined from n=903 participants who enrolled between 12/13/2020 and 3/17/2022 and n=196 who completed at least one follow-up survey 3 to 6 months after enrollment. Logistic regressions were conducted to evaluate associations between demographic variables and survey completion. A McNemar’s chi-square test and paired-samples t-tests were performed to evaluate changes in the numbers of participants in Tier 1 versus Tier 2 or 3 and changes in depression, anxiety, and PTSD severity between baseline and follow-up.

Results:

White/Caucasian racial identity was associated with completing baseline CAT-MH, p=.012, OR=1.80 95% CI[1.14, 2.84]. Participants’ odds of having symptom severities below the clinical threshold (i.e., Tier 1) were significantly greater at follow-up, p=.002, OR=2.60, and significant reductions were observed across symptom domains over time.

Conclusions:

CHAMindWell is associated with reduced severity of mental health symptoms. Future work should aim to address program engagement inequities and attrition, and compare the impacts of CHAMindWell to a control condition to better characterize its effects.