Self-adaptive over progressive non-adaptive immersive virtual reality serious game to promote motor learning in older adults – A double blind randomized controlled trial
Bibliographic record
Abstract
Older adults often experience cognitive and functional decline, which can be mitigated by innovative interventions like immersive virtual reality (iVR). Self-adaptive serious games, which dynamically adjust task difficulty, may provide additional benefits by tailoring challenges to individual performance. However, the comparative effectiveness of self-adaptive versus progressive non-adaptive iVR interventions on motor learning in older adults remains underexplored. This randomized controlled trial primarily aimed to compare the effects of self-adaptive and progressive non-adaptive iVR interventions on motor learning in older adults. A total of 33 older adults were randomly assigned to either a self-adaptive (n = 17) or progressive non-adaptive (n = 16) iVR group. Both interventions involved an iVR serious game requiring unimanual reaching, target selection, and distractor inhibition under increasing difficulty over three days. Performance was evaluated pre- and post-intervention using a standardized iVR task, consisting of hitting a virtual target while ignoring distractors. This task features five levels of difficulty (level 0: no distractors; level 4: 17 low-saliency distractors). Primary outcomes, assessed during the standardized task, included changes in speed-accuracy trade-offs (SAT) and relative response time (RT, compared to level 0). Both participants and assessors were blinded to the intervention. After the intervention, both groups exhibited significant SAT improvements at level 1, 3 and 4 (P < 0.05) and RT gains at level 3 and 4 (P < 0.05). The self-adaptive group demonstrated significantly greater SAT (P = 0.049) and relative RT (P = 0.004) improvements at level 4. Findings suggest that older adults can achieve motor learning in iVR, particularly with self-adaptive serious games, highlighting potential for self-rehabilitation. Clinical trial NCT06141642 approved the 21th November 2023.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".