Perspectives of Persons With Arthritis on the Use of Wearable Technology to <scp>Self Monitor</scp> Physical Activity: A Qualitative Evidence Synthesis
Bibliographic record
Abstract
OBJECTIVE: We aimed to broaden understanding of the perspectives of persons with arthritis on their use of wearables to self-monitor physical activity, through a synthesis of evidence from qualitative studies. METHODS: We conducted a systematic search of 5 databases (including Medline, CINAHL, and Embase) from inception to 2018. Eligible studies qualitatively examined the use of wearables from the perspectives of persons with arthritis. All relevant data were extracted and coded inductively in a thematic synthesis. RESULTS: Of 4,358 records retrieved, 7 articles were included. Participants used a wearable during research participation in 3 studies and as part of usual self-management in 2 studies. In remaining studies, participants were shown a prototype they did not use. Themes identified were: 1) the potential to change dynamics in patient-health professional communication: articles reported a common opinion that sharing wearable data could possibly enable patients to improve communication with health professionals; 2) wearable-enabled self-awareness, whether a benefit or downside: there was agreement that wearables could increase self-awareness of physical activity levels, but perspectives were mixed on whether this increased self-awareness motivated more physical activity; 3) designing a wearable for everyday life: participants generally felt that the technology was not obtrusive in their everyday lives, but certain prototypes may possibly embarrass or stigmatize persons with arthritis. CONCLUSION: Themes hint toward an ethical dimension, as participants perceive that their use of wearables may positively or negatively influence their capacity to shape their everyday self-management. We suggest ethical questions pertinent to the use of wearables in arthritis self-management for further exploration.
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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 | 0.067 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".