Even low alcohol concentrations affect obstacle avoidance reactions in healthy senior individuals
Bibliographic record
Abstract
BACKGROUND: Alcohol is a commonly used social drug and driving under influence is a well-established risk factor for traffic accidents1. To improve road safety, legal limits are set for blood alcohol concentration (BAC) and driving, usually at 0.05% (most European countries) or 0.08% (most US states, Canada and UK). In contrast, for walking there are no legal limits, yet there are numerous accounts of people stumbling and falling after drinking. Alcohol, even at these low concentrations, affects brain function and increases fall risk. An increased fall risk has been associated with impaired obstacle avoidance skills. Low level BACs are likely to affect obstacle avoidance reactions during gait, since the brain areas that are presumably involved in these reactions have been shown to be influenced by alcohol. Therefore we investigated the effect of low to moderate alcohol consumption on such reactions.Thirteen healthy senior individuals (mean(SD) age: 61.5(4.4) years, 9 male) were subjected to an obstacle avoidance task on a treadmill after low alcohol consumption. Fast stepping adjustments were required to successfully avoid suddenly appearing obstacles. Response times and amplitudes of the m. biceps femoris, a prime mover, as well as avoidance failure rates were assessed. FINDINGS: After the first alcoholic drink, 12 of the 13 participants already had slower responses. Without exception, all participants' biceps femoris response times were delayed after the final alcoholic drink (avg ± sd:180 ± 20 ms; p < 0.001) compared to when participants were sober (156 ± 16 ms). Biceps femoris response times were significantly delayed from BACs of 0.035% onwards and were strongly associated with increasing levels of BAC (r = 0.6; p < 0.001). These delays had important behavioural consequences. Chances of hitting the obstacle were doubled with increased BACs. CONCLUSIONS: The present results clearly show that even with BACs considered to be safe for driving, obstacle avoidance reactions are inadequate, late, and too small. This is likely to contribute to an increased fall risk. Therefore we suggest that many of the alcohol-related falls are the result of the disruptive effects of alcohol on the online corrections of the ongoing gait pattern when walking under challenging conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".