Motivators, Barriers, and Opportunity for E‐Health to Encourage Physical Activity in Axial Spondyloarthritis: A Qualitative Descriptive Study
Bibliographic record
Abstract
OBJECTIVE: Physical activity is fundamental in the management of axial spondyloarthritis (SpA); however, evidence suggests that patients with axial SpA are not adhering to physical activity recommendations. E-health technology (e.g., telephone reminders and mobile text messaging) can increase participation in physical activity. The aims of this study were as follows: 1) to understand perspectives of the importance of physical activity in the management of axial SpA; 2) to describe factors associated with physical activity adherence; and 3) to explore the role of e-health technology to facilitate physical activity in patients with axial SpA. METHODS: Semistructured interviews were conducted with axial SpA patients attending an urban academic rheumatology clinic. Interviews were audio recorded and transcribed verbatim. Data were analyzed using thematic principles. Systematic labeling of the data set was completed using an inductive approach until saturation of emergent themes. RESULTS: Twelve patient interviews were completed. Most respondents were male (83.3%) with a mean ± SD age of 45.5 ± 12.5 years and a mean ± SD disease duration of 21.5 ± 14.9 years. Participants defined physical as any activity involving physical exertion. The role of physical activity in axial SpA management was well recognized and included symptom relief, pharmacologic synergy, and impact on general health. Motivators included a growth mindset, social support networks, and facility access. Barriers included fear of disease progression, life demands, and environmental restrictions. Feedback, electronic reminders, and virtual support networks were key components of e-health technology to facilitate engagement in physical activity. CONCLUSION: The results of this study provide a foundation to guide development of patient-centered e-health technology interventions to increase physical activity uptake in patients with axial SpA.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".