Physical activity during COVID-19 in people with systemic sclerosis: A Scleroderma Patient-centred Intervention Network COVID-19 Cohort longitudinal study
Bibliographic record
Abstract
Introduction/Objective: People with systemic sclerosis (SSc) face barriers to physical activity. Few studies have described physical activity in SSc, and none have explored physical activity longitudinally during COVID-19. We evaluated physical activity from April 2020 to March 2022 among people with SSc. Methods: The Scleroderma Patient-centred Intervention Network (SPIN) COVID-19 Cohort was launched in April 2020 and included participants from the ongoing SPIN Cohort plus external enrolees. Participants completed measures bi-weekly through July 2020, then every 4 weeks afterwards (28 assessments). Physical activity was assessed via the self-reported International Physical Activity Questionnaire-Elderly. Analyses included estimated means with 95% confidence intervals for physical activity across assessments. Missing data were imputed for main analyses. Sensitivity analyses included evaluating only participants who completed >90% of items for >21 of 28 possible assessments ('completers') and stratified analyses by sex, age, country and SSc subtype. Results: A total of 800 people with SSc enrolled. Mean age was 55.6 (standard deviation (SD) = 12.6) years. Physical activity significantly decreased from April 2020 to March 2021 (standardized mean difference (SMD) = -0.17, 95% confidence interval (CI) = -0.26 to -0.07) and was stable from March 2021 to March 2022 (SMD = -0.05, 95% CI = -0.15 to 0.05). Results were similar for completers and subgroups. The proportion of participants who met World Health Organization minimum physical activity recommendations of at least 150 min of moderate-to-vigorous activity per week ranged from 63% to 82% across assessments. Conclusion: Physical activity decreased by a relatively small amount, on average, across the pandemic. Most participants met recommended physical activity levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".