Physical Activity Experiences of People with Multiple Sclerosis during the COVID-19 Pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, government and health officials introduced measures such as social distancing and facility closures that amplified barriers to physical activity. Certain groups, including people with multiple sclerosis (MS), have been underserved during the pandemic. In this qualitative study we aimed to: (1) explore the physical activity experiences of people with MS during the COVID-19 pandemic; (2) identify the facilitators and barriers to physical activity during COVID-19 for people with MS; and (3) make recommendations for inclusive physical activity policy and programming. We conducted semi-structured interviews with 11 adults (9 women) with MS during January and February 2021. Following an inductive thematic analysis, three themes were developed: (1) changing opportunities and adapting to new opportunities; (2) social isolation and physical activity; and (3) adapting physical activity to stay safe from COVID-19. Common facilitators identified included having knowledge and resources to adapt activities, social connections, and access to outdoor recreation opportunities. Identified barriers included fear and anxiety related to the spread of the virus, a loss of in-person activity options, and the closure of physical activity spaces. Online and at-home opportunities for physical activity were a valued and accessible way to address barriers to physical activity for people with MS, and should be maintained post-pandemic while considering flexibility to accommodate variable support needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".