Experiences of Wheelchair Users With Spinal Cord Injury With Self-Tracking and Commercial Self-Tracking Technology (“In Our World, Calories Are Very Important”): Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Commercial wearable and mobile wellness apps and devices have become increasingly affordable and ubiquitous. One of their aims is to assist the individual wearing them in adopting a healthier lifestyle through tracking and visualizing their data. Some of these devices and apps have a wheelchair mode that indicates that they are designed for different types of bodies (eg, wheelchair users with spinal cord injury [SCI]). However, research focuses mainly on designing and developing new condition-specific self-tracking technology, whereas the experiences of wheelchair users with SCI using self-tracking technology remain underexplored. OBJECTIVE: The objectives of this study were to (1) provide a comprehensive overview of the literature in the field of self-tracking technology and wheelchair users (as a basis for the study), (2) present the self-tracking needs of wheelchair users with SCI, and (3) present their experiences and use of commercial self-tracking technology. METHODS: We conducted semistructured interviews with wheelchair users with SCI to understand their experiences with self-tracking and self-tracking technologies, their self-tracking needs, and how they changed before and after the injury. The interviews were thematically analyzed using an inductive approach. RESULTS: Our findings comprised three themes: (1) being a wheelchair user with SCI, (2) reasons for self-tracking, and (3) experiences with self-tracking technologies and tools. The last theme comprised 3 subthemes: self-tracking technology use, trust in self-tracking technology, and calorie tracking. CONCLUSIONS: In the Discussion section, we present how our findings relate to the literature and discuss the lack of trust in commercial self-tracking technologies regarding calorie tracking, as well as the role of wheelchair users with SCI in the design of commercial self-tracking technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".