Kinesiotherapy With Exergaming as a Potential Modulator of Epigenetic Marks and Clinical Functional Variables of Older Women: Protocol for a Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Kinesiotherapy is an option to mitigate worsening neuropsychomotor function due to human aging. Moreover, exergames are beneficial for the practice of physical therapy by older patients. Physical exercise interventions are known to alter the epigenome, but little is known about their association with exergames. OBJECTIVE We aim to evaluate the effects of kinesiotherapy with exergaming on older women’s epigenetic marks and cognitive ability, as well as on their clinical functional variables. Our hypothesis states that this kind of therapy can elicit equal or even better outcomes than conventional therapy. METHODS We will develop a virtual clinic exergame with 8 types of kinesiotherapy exercises. Afterward, we will conduct a 1:1 randomized clinical trial to compare the practice of kinesiotherapy with exergames (intervention group) against conventional kinesiotherapy (control group). A total of 24 older women will be enrolled for 1-hour sessions performed twice a week, for 6 weeks, totaling 12 sessions. We will assess outcomes using epigenetic blood tests, the Montreal Cognitive Assessment test, the Timed Up and Go test, muscle strength grading in a hydraulic dynamometer, and the Game Experience Questionnaire at various stages. RESULTS The project was funded in October 2019. Game development took place in 2020. Patient recruitment and a clinical trial are planned for 2021. CONCLUSIONS Research on this topic is likely to significantly expand the understanding of kinesiotherapy and the impact of exergames. To the best of our knowledge, this may be one of the first studies exploring epigenetic outcomes of exergaming interventions. CLINICALTRIAL Brazilian Clinical Trials Registry/Registro Brasileiro de Ensaios Clínicos (ReBEC) RBR-9tdrmw; https://ensaiosclinicos.gov.br/rg/RBR-9tdrmw. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/32729
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.009 |
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".