Expectations of a Health-Related Mobile Self-Management App Intervention Among Individuals With Spinal Cord Injury
Bibliographic record
Abstract
Background Our research team developed a mobile application (app) to facilitate health-related self-management behaviors for secondary conditions among individuals with spinal cord injury (SCI). To facilitate mobile app adoption and ongoing use into the community, it is important to understand potential users’ expectations and needs. Objectives The primary objective of this study was to explore user expectations of a mobile app intervention designed to facilitate self-management behavior among individuals with SCI. Methods Data were collected via one-on-one, semi-structured interviews with a subsample of 20 community-dwelling participants enrolled in a larger, clinical trial. Analysis of the transcripts was undertaken using a six-phase process of thematic analysis. Results Our analysis identified three main themes for expectations of the mobile app intervention. The first theme, desiring better health outcomes, identified participants’ expectation of being able to improve their psychological, behavioral, and physical health outcomes and reduce associated secondary conditions. The second theme, wanting to learn about the mobile app’s potential , identified participants’ interest in exploring the functionality of the app and its ability to promote new experiences in health management. The third theme, desiring greater personal autonomy and social participation , identified participants’ desire to improve their understanding of their health and the expectation for the app to facilitate social engagement with others in the community. Conclusion By exploring end-users’ expectations, these findings may have short-term effects on improving continued mobile health app use among SCI populations and long-term effects on informing future development of mobile app interventions among chronic disease populations.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".