Remotely Supervised Exercise Programmes to Improve Balance, Mobility, and Activity Among People with Moderate to Severe Traumatic Brain Injury: Description and Feasibility
Bibliographic record
Abstract
Purpose: Further investigation into the feasibility of using videoconferencing and activity tracking devices to provide high-intensity home-based exercise programmes for people with a moderate or severe traumatic brain injury (TBI) is needed to inform clinical implementation and patient adoption. This study aimed to (1) determine if home-based telerehabilitation exercise programmes were feasible for people with a moderate or severe TBI and (2) better understand the lived experience of people with a TBI and their family partners with this programme. Methods: A mixed-methods approach consisting of measures of feasibility and semi-structured interviews was used. Five participants with moderate to severe TBI and their family partners completed two high-intensity home-based exercise programmes delivered remotely by a physiotherapist (i.e., daily and weekly). Results: Telerehabilitation services in home-based settings were feasible for this population. Adherence and engagement were high. Dyads were satisfied with the use of technology to deliver physiotherapy sessions. Conclusion: Telerehabilitation provides a delivery option that allows people with TBI to spend energy on therapy rather than on travelling. A pre-programme training on key components, such as the use of technology, safety precautions, and communication methods, likely improved the overall feasibility. Further research is needed to better understand the effectiveness of such a programme on balance, mobility, and physical activity levels.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".