Barriers and Facilitators to Physical Activity for People With Scleroderma: A Scleroderma Patient‐Centered Intervention Network Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To support physical activity among people with systemic sclerosis (SSc [scleroderma]), we sought to determine the prevalence and importance of barriers and the likelihood of using possible facilitators. METHODS: We invited 1,707 participants from an international SSc cohort to rate the importance of 20 barriers (14 medical, 4 social or personal, 1 lifestyle, and 1 environmental) and the likelihood of using 91 corresponding barrier-specific and 12 general facilitators. RESULTS: Among 721 respondents, 13 barriers were experienced by ≥25% of participants, including 2 barriers (fatigue and Raynaud's phenomenon) rated "important" or "very important" by ≥50% of participants, 7 barriers (joint stiffness and contractures, shortness of breath, gastrointestinal problems, difficulty grasping, pain, muscle weakness and mobility limitations, and low motivation) by 26-50%, and 4 barriers by <26%. Overall, 23 of 103 facilitators (18 medical-related) were rated by ≥75% of participants as "likely" or "very likely" to use among those who experienced corresponding barriers. These facilitators focused on adapting exercise (e.g., using controlled, slow movement), taking care of one's body (e.g., stretching), keeping warm (e.g., wearing gloves), and protecting skin (e.g., covering ulcers). Among those participants who had previously tried the facilitator, all facilitators were rated by ≥50% as "likely" or "very likely" to use. Among those participants with the barrier who had not tried the facilitator, only 12 of 103 facilitators were rated by >50% of participants as "likely" or "very likely" to use. CONCLUSION: Medical-related physical activity barriers were common and considered important. Facilitators considered as most likely to be used involved adapting exercise, taking care of one's body, keeping warm, and protecting skin.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".