Long COVID Physical Activity Impact Coding Categories from 34 U.S. Patient Interviews

34 adults in the United States with self-reported long COVID participated in semi-structured interviews about physical activity. Deductive thematic analysis of interview content produced coding categories for worsened and improved health experiences attributed to physical activity. The dataset was authored by Zoe Sirotiak and last updated in May 2026.

Use Cases

• Train models to categorize patient-reported outcomes based on themes like post-exertional malaise and symptom improvement.
• Analyze the relationship between physical activity and specific symptom clusters mentioned in the description.
• Validate survey instruments for long COVID by comparing against qualitative interview themes.
• Study demographic patterns in perceived health impacts, based on the participant cohort described.

Strengths

• Includes data from 34 patient interviews, providing qualitative depth.
• Analysis is based on a defined deductive thematic methodology.
• Contains specific quantitative findings from the study, such as 64.7% of participants reporting symptom worsening.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

Zoe Sirotiak via figshare.

Collection Method

Semi-structured interviews analyzed via deductive thematic analysis.

Freshness

Last updated 2026-05-27 17:32:20; freshness should be verified.

Geography

United States (U.S.)