A co-design process to develop personalized mobility programming for individuals with mobility impairments
Bibliographic record
Abstract
Introduction: Individuals with neurological conditions (e.g., stroke, spinal cord injury, multiple sclerosis) may experience challenges to their mobility. While the individual needs for persons with neurological conditions may vary, thus making intervention development more difficult, identifying key personalization or tailoring variables may help to customize interventions. However, the process to personalize treatments has not been well described. It is also unclear how adaptive intervention design includes the perspective of those with lived experience. Co-design methods may be a way to be transparent about intervention development to meet the needs of persons with mobility impairments while ensuring the resulting intervention is relevant and applicable to those who will be participating. The purpose of the present article is to describe a co-design process to facilitate the development of personalized mobility programming for persons with mobility impairments. Methods: Development of a set of personalized mobility programming for individuals with mobility impairments was conducted following an adaptive intervention design approach with a co-design component. A series of working groups and individual sessions with key interest groups (e.g., persons with lived experience, fitness instructors, front-line clinicians, students) were conducted in order to develop the personalized mobility programming based on the needs and preferences described during various working groups. Results: = 1 registered kinesiologist). From the information gathered during the working groups a set of four personalized mobility programs were developed: (1) cognitive cardio class, (2) functional strength class, (3) mobility circuit group, and (4) an open gym. Participants also discussed the onboarding process, how to effectively track participant goals throughout the programming and personalization variables. Discussion: The current paper provides a guideline for future work that aims to develop programming that is personalized to the needs of the persons with mobility impairments due to various neurological conditions. The strengths of this approach include the collaborative nature of the program development, while the main limitations were logistical in nature (e.g., scheduling, engaging all working group members).
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".