Use of wearables to measure the effects of long COVID on activities of daily living and their relationship to perceived exertion, occupational performance, and quality of life
Bibliographic record
Abstract
Introduction: This study introduces a novel approach to understanding the impact of long COVID symptoms on daily life by integrating wearable devices to assess their influence on physical and mental quality of life, as well as perceived performance and satisfaction in daily activities. Methods: By leveraging technology such as accelerometers and pulse oximeters alongside assessment tools like the SF-12 Health Survey, the Canadian Occupational Performance Measure, and the Borg Scale, this research provides a comprehensive analysis that advances the field of occupational therapy. Results: An analytical observational study with 10 participants with long COVID and 10 healthy controls revealed that individuals with long COVID took significantly longer to complete tasks such as setting the table, sweeping, and climbing stairs, compared to the control group. Participants with long COVID also reported higher perceived exertion during all activities, as well as significantly worse physical health-related quality of life and lower satisfaction and performance in daily activities. Notably, perceived exertion correlated with reduced physical quality of life and diminished satisfaction and accomplishment in occupational tasks. Discussion: These findings emphasize the critical need for occupational therapy interventions to reduce perceived exertion, which could improve physical quality of life and enhance performance and satisfaction in daily activities for individuals with long COVID.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".