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.
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".