Using Physical Activity Trackers in Arthritis Self‐Management: A Qualitative Study of Patient and Rehabilitation Professional Perspectives
Bibliographic record
Abstract
OBJECTIVE: To compare and contrast the perspectives of patients with arthritis and those of rehabilitation professionals regarding starting and sustaining use of physical activity trackers (PATs). METHODS: We conducted focus group sessions with patients, physiotherapists, and occupational therapists in Ontario, Alberta, or British Columbia, Canada. To be eligible, patients must have self-reported a diagnosis of inflammatory or osteoarthritis. Rehabilitation professionals reported that at least 40% of their caseload was dedicated to arthritis care. Participants had any level of experience with PATs. A thematic analytic approach was used. RESULTS: The following 3 themes were identified: 1) anticipating sharing objective measures of physical activity. Participants agreed that use of PATs had the potential to improve consultations between patients with arthritis and rehabilitation professionals but were uncertain how to achieve this potential; 2) perceived or experienced barriers to start or continue using a PAT. Participants shared doubts about whether existing PATs would meet specific needs of patients with arthritis and expressed concerns about possible negative impacts; and 3) bolstering motivation? Although there was agreement that use of PATs could bolster the motivation of patients who were already active, patients and rehabilitation professionals had different opinions regarding whether use of PATs alone would motivate patients to start increasing activity levels. CONCLUSION: Our study highlights similarities and differences between the perspectives of patients and rehabilitation professionals regarding the potential value and risks of integrating PATs into arthritis self-management. Despite agreement about the potential of PATs, participants were uncertain how to effectively incorporate these tools to enhance patient-clinician consultations and had differing views about whether use of PATs would support a patient's motivation to be active.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".