Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app.

ABSTRACT:

Background

Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups.

Objective

Here, we sought to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended.

Methods

To d

SUBMITTER: Shvetcov A 

PROVIDER: S-EPMC10511781 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature