Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: Protocol for a Pragmatic Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Exergame-based motor-cognitive training in older adults has been associated with improvements in physical, cognitive, and psychological functioning. The novel Cocare system (Dividat GmbH), developed through a user-centered design process, allows motor-cognitive training in a telerehabilitation setting. It includes (1) a stationary stepping platform for supervised exergame training (Dividat Senso; Dividat GmbH), (2) a home-based version (Dividat Senso Flex, which is a rollable pressure-sensitive mat; Dividat GmbH), (3) an assessment system (including motor-cognitive tests), and (4) a rehabilitation cockpit for remote training supervision and management. OBJECTIVE: The aim of this study is to test the feasibility and effectiveness of this novel training system. METHODS: A total of 180 older adults from Switzerland, Italy, and Cyprus aged ≥60 years with a prescription for rehabilitation are randomly allocated to an intervention group or a control group. Both groups continue with their usual care, whereas participants in the intervention group additionally perform a 2-week supervised exergame training program at rehabilitation centers, followed by a 10-week home training program under remote supervision. The assessment system is used to indicate the start level of each participant, and, in both intervention periods, standardized progression rules are applied. The measures of feasibility include adherence, attrition, exergame enjoyment, willingness to perform such a training program, and the number and types of help requests. Effectiveness is assessed in terms of cognitive and physical functioning, balance confidence, and quality of life. RESULTS: Data collection started in February 2023 and is ongoing. Final measurements are expected to be performed in January 2024. CONCLUSIONS: Owing to the user-centered design approach, the Cocare system is expected to be user-friendly and offers several novel features to cover the whole continuum of care. This pragmatic trial will provide valuable information regarding final necessary adaptations and subsequent implementation efforts. TRIAL REGISTRATION: ClinicalTrials.gov NCT05751551; https://www.clinicaltrials.gov/study/NCT05751551. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49377.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".