Experiences of Wearable Technology by Persons with Knee Osteoarthritis Participating in a Physical Activity Counseling Intervention: Qualitative Study Using a Relational Ethics Lens
Bibliographic record
Abstract
BACKGROUND: Current evidence indicates physical activity wearables could support persons with knee osteoarthritis (OA) to be more physically active. However, recent evidence also identifies some persons with arthritis experience guilt or worry while using a wearable if they are not as active as they feel they should be. Questions remain around how persons with knee OA experience benefits or downsides using a wearable in their everyday lives. Better understanding is needed if wearables are to be incorporated in arthritis self-management in ethically aware ways. OBJECTIVE: Using an ethics lens, we aimed to describe a range of experiences from persons with knee OA who used a wearable during a physical activity counseling intervention study. METHODS: This is a secondary analysis of qualitative interviews nested within a randomized controlled trial. Guided by phenomenography, we explored the experiences of persons with knee OA following participation in a physical activity counseling intervention that involved using a Fitbit Flex and biweekly phone calls with a study physiotherapist (PT) in an 8-week period. Benefits or downsides experienced in participants' relationships with themselves or the study PT when using the wearable were identified using a relational ethics lens. RESULTS: Interviews with 21 participants (12 females and 9 males) aged 40 to 82 years were analyzed. Education levels ranged from high school graduates (4/21, 19%) to bachelor's degrees or above (11/21, 52%). We identified 3 categories of description: (1) participants experienced their wearable as a motivating or nagging influence to be more active, depending on how freely they were able to make autonomous choices about physical activity in their everyday lives; (2) some participants felt a sense of accomplishment from seeing progress in their wearable data, which fueled their motivation; (3) for some participants, sharing wearable data helped to build mutual trust in their relationship with the study PT. However, they also expressed there was potential for sharing wearable data to undermine this trust, particularly if this data was inaccurate. CONCLUSIONS: Findings provide an early glimpse into positive and negative emotional impacts of using a wearable that can be experienced by participants with knee OA when participating in a randomized controlled trial to support physical activity. To our knowledge, this is the first qualitative study that uses a relational ethics lens to explore how persons with arthritis experienced changes in their relationship with a health professional when using a wearable during research participation.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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, 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".