BACKGROUND
Mental health conditions pose a significant challenge for healthcare systems globally, and digital solutions have been proposed to address the challenges of limited access to healthcare, stigmatization, and lack of reliable data. An understanding of potential risk factors, comorbidities, and symptom constellations forms a necessary foundation for future digital solutions with diagnostic capabilities. Currently used digital products such as symptom checkers generate novel data sets that could provide insight into these correlations, helping both patients and healthcare providers.
OBJECTIVE
This study aimed to compare the characteristics of SC (Ada) users with a suggested mental health condition (MHC) in their symptom assessment (1) to all Ada users and (2) to the general population using the Global Burden of Diseases, Injuries, and Risk Factors (GBD) study 2019 dataset.
METHODS
Aggregated data from the SC was analyzed to provide a descriptive analysis of its users and compared to the World Population Prospects 2019 and the Global Burden of Disease study, which includes estimations for 195 countries and territories. Ada users worldwide who completed a symptom assessment between in 2020 or 2021 were included in the analysis. The study focused on user demographics, reported symptoms, and suggested conditions.
RESULTS
Out of 2,208,700 users, 20.9% received at least one MHC as the top suggestion. Female users (65.0%) were overrepresented in Ada's user base, and the largest number of users were aged 16-24 (57.0%). The average number of symptoms by users confirmed during the question flow was 6.7, with 1.9 symptoms entered initially. Major depressive disorder was the most frequently suggested MHC, affecting 10.2% of all Ada users, followed by other anxiety disorders (5.7%). Comparison of the Ada dataset with the GBD dataset showed that Depressive disorders and Anxiety disorders were the two most frequent MHCs in both datasets, with females more commonly affected than males.
CONCLUSIONS
Young and female users are overrepresented in the Ada userbase, especially among those with MHC suggestions compared with the GBD dataset. Ada's ability to reach these at-risk populations presents a significant opportunity for providing personalized and accessible healthcare solutions in the future. While the results cannot be easily generalized due to this population bias, this analysis highlights the potential of patient-reported data generated by symptom checkers as a source for epidemiological studies.