Exploring Salivary Brain-Derived Neurotrophic Factor (BDNF) as a Potential Biomarker of Neuroplasticity in Older Adults Through Exergaming
Bibliographic record
Abstract
Background Dementia and late-life depression (LLD) are common among older adults and are often associated with cognitive decline. Exergaming, which integrates physical and cognitive stimulation, may promote neuroplasticity in this population. Noninvasive biomarkers, such as salivary brain-derived neurotrophic factor (BDNF) methylation, provide a novel approach for monitoring intervention-related neuroplastic changes. Objective This pilot study examined the feasibility of a four-week exergaming intervention aimed at promoting both cognitive and physical engagement in older adults with dementia or LLD. The study also assessed changes in salivary BDNF DNA methylation, a biomarker of neuroplasticity, using noninvasive collection and droplet digital PCR (ddPCR) analysis. Results Participants engaged meaningfully in the exergame, and cognitive metrics showed improved performance across sessions. BDNF methylation was detectable in saliva samples, confirming feasibility; however, the small sample size and limited statistical power precluded significant findings. No causal conclusions can be drawn. Conclusions This study demonstrates that exergaming, combined with saliva collection and ddPCR, is both feasible and acceptable for older adults with cognitive impairment or depression. The intervention design was informed by theoretical frameworks of neuroplasticity, motor learning, and task-specific training. Larger controlled studies are warranted to evaluate clinical efficacy, expand BDNF analyses, and further investigate underlying neuroplastic mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".