Envisioning future cognitive telerehabilitation technologies: a co-design process with clinicians
Bibliographic record
Abstract
Purpose Cognitive telerehabilitation is the concept of delivering cognitive assessment, feedback, or therapeutic intervention at a distance through technology. With the increase of mobile devices, wearable sensors, and novel human-computer interfaces, new possibilities are emerging to expand the cognitive telerehabilitation paradigm. This research aims to: (1) explore design opportunities and considerations when applying emergent pervasive computing technologies to cognitive telerehabilitation and (2) develop a generative co-design process for use with rehabilitation clinicians. Methods We conducted a custom co-design process that used design cards, probes, and design sessions with traumatic brain injury (TBI) clinicians. All field notes and transcripts were analyzed qualitatively. Results Potential opportunities for TBI cognitive telerehabilitation exist in the areas of communication competency, executive functioning, emotional regulation, energy management, assessment, and skill training. Designers of TBI cognitive telerehabilitation technologies should consider how technologies are adapted to a patient's physical/cognitive/emotional state, their changing rehabilitation trajectory, and their surrounding life context (e.g. social considerations). Clinicians were receptive to our co-design approach. Conclusion Pervasive computing offers new opportunities for life-situated cognitive telerehabilitation. Convivial design methods, such as this co-design process, are a helpful way to explore new design opportunities and an important space for further methodological development. Implications for Rehabilitation Designers of rehabilitation technologies should consider how to extend current design methods in order to facilitate the creative contribution of rehabilitation stakeholders. This co-design approach enables a fuller participation from rehabilitation clinicians at the front-end of design. Pervasive computing has the potential to: extend the duration and intensity of cognitive telerehabilitation training (including the delivery of 'booster' sessions or maintenance therapies); provide assessment and treatment in the context of a traumatic brain injury (TBI) patient's everyday life (thereby enhancing generalization); and permit time-sensitive interventions. Long-term use of pervasive computing for TBI cognitive telerehabilitation should take into account a patient's changing recovery trajectory, their meaningful goals, and their journey from loss to redefinition.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".