Implementing a self-management mobile app for spinal cord injury during inpatient rehabilitation and following community discharge: A feasibility study
Bibliographic record
Abstract
Objective: To determine the feasibility of implementing and evaluating a self-management mobile app for spinal cord injury (SCI) during inpatient rehabilitation and following community discharge.Design: Pilot feasibility study.Setting: Rehabilitation hospital and community.Participants: Inpatients from rehabilitation hospital following admission for their first SCI.Intervention: A mobile app was developed to facilitate self-management following SCI. The app consisted of 18 tools focusing on goal setting, tracking various health aspects, and identifying confidence regarding components of self-management. In-person training and follow-up sessions were conducted during inpatient rehabilitation and follow-up calls were provided after participants were discharged into the community.Main outcome measures: Participants completed outcome measures at baseline, community discharge, and 3-months post discharge. This study focused on feasibility indicators including recruitment, retention, respondent characteristics, adherence, and app usage. Additionally, participants’ self-management confidence relating to SCI (e.g. medication, skin, bladder, pain) was evaluated over time.Results: Twenty participants (median age 39, IQR: 31 years, 85% male) enrolled in the study. Participants’ Spinal Cord Injury Independence Measure (SCIM-III) median score was 23 and IQR was 33 (range: 7–84), which did not correlate with app usage. Retention from admission to discharge was 85% and 70% from discharge to 3-months post discharge. Individuals in the study who used the app entered data an average of 1.7x/day in rehabilitation (n = 17), and 0.5x/day in the community (n = 7). Participants’ bowel self-management confidence improved between admission and discharge (P < 0.01).Conclusions: Feasibility indicators support a larger clinical trial during inpatient rehabilitation; however, there were challenges with retention and adherence following community discharge.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".