Considerations for developing and implementing an online community-based exercise intervention with adults living with HIV: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: To describe the need for, and utility of, online community-based exercise (CBE) interventions with adults living with HIV and identify factors to consider in developing and implementing an online CBE intervention with adults living with HIV. DESIGN: Qualitative descriptive study using web-based semistructured interviews. PARTICIPANTS: We recruited adults representing at least one of five stakeholder groups with experience in CBE and/or HIV: (1) adults living with HIV, (2) rehabilitation professionals, (3) fitness personnel, (4) educators with eLearning experience and (5) representatives from HIV community-based organisations (CBOs). DATA COLLECTION: We asked participants to describe their experiences with online CBE, need and utility for online CBE and factors in developing and implementing online CBE interventions. We analysed data using a group-based thematic analytical approach. RESULTS: Among the 11 participants, most had experience working with adults living with HIV (73%) or with telehealth/rehabilitation/coaching in HIV or other chronic conditions (91%). Participants (eight women; two men; one non-binary; median age: 49 years), identified the need and utility for online CBE interventions to increase accessibility and continuity of care with adults living with HIV. Six factors to consider in developing and implementing online CBE included: (1) person-specific considerations (episodic nature of HIV, stigma, HIV disclosure), (2) accessibility of programme (physical space to exercise, reliable internet, access to devices, digital literacy), (3) programme delivery and technology (live vs prerecorded online classes, multiple online platforms for delivery, physical activity tracking, troubleshooting technology), (4) attributes of programme personnel (working with CBOs, relatable instructors, diverse staff), (5) programme content and design (tailored exercise classes, educational sessions) and (6) building community (shared experiences, peer support, social opportunities). CONCLUSIONS: There is a need and utility for online CBE in the context of HIV. Considerations for development and implementation span individual, structural and technical, and community dimensions. Results can inform the future development and implementation of online CBE with adults living with HIV and other chronic episodic conditions.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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, 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".