Effectiveness of Acupressure and Acustimulation in Minimizing Driving Simulation Adaptation Syndrome
Bibliographic record
Abstract
We investigated the efficacy of acupressure and acustimulation in alleviating symptoms of Simulation Adaptation Syndrome (SAS). Twenty-five drivers (mean age = 35.6) reporting a history of driving-related sickness, motion sickness, and/or seasickness were recruited for a within subject, repeated-measures crossover study. Of all participants, 16 reported SAS during a placebo condition. These 16 participants drove the Atari research simulator for 15 minutes on 3 separate days (same time each day), wearing: (a) a placebo device, (b) acupressure beads, and (c) an acu-stimulation device. Every 3 minutes during each drive, participants rated their physical discomfort. Overall, the analysis of variance condition effect was significant (p < 0.05). Participants in the acustimulation condition reported significantly less physical discomfort (p < 0.005) compared with the placebo. There were no significant differences between the acupressure and placebo conditions or the acupressure and acustimulation conditions. These data suggest that acustimulation can help to significantly reduce or prevent SAS-related nausea and physical discomfort.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".