JMIR Publications

ABSTRACT

Background:

Smell disorders are commonly reported with COVID-19 infection. Some patients show prolonged smell-related issues, even after the respiratory symptoms are resolved. To explore the concerns of patients, and to provide an overview for each specific smell disorder, we explored a longitudinal survey that was administered online, and contained self-reports on the changes of smell that participants (n=1560) experienced at two time points.

Objective:

Participants who still suffered from smell disorders at the second time point, hence named ‘longhaulers’, were compared to those who were not, hence named ‘non-longhaulers’.

Methods:

Specifically, three aims were pursued in this study. First, to classify smell disorders based on the participants’ self-reports. Second, to classify the sentiment of each self-report using a machine learning approach, and third, to find specific keywords that best describe the smell dysfunction in those self-reports.

Results:

We found that the prevalence of parosmia and hyposmia was higher in longhaulers than in non-longhaulers. Furthermore, the results suggest that longhaulers stated self-reports with more negative sentiment than non-longhaulers. Finally, we found specific keywords that were more typical for either longhaulers compared to non-longhaulers.

Conclusions:

Taken together, our work shows consistent findings with previous studies, while at the same time, provides new insights for future studies investigating smell disorders.