Smartwatches are more distracting than mobile phones while driving: Results from an experimental study
Bibliographic record
Abstract
The use of smartwatches raises a number of questions about their potential for distraction in situations where sustained attention is paramount, like driving a motor vehicle. Our research examines distraction caused by smartwatch use in comparison to mobile phone use while driving. It also studies the difference in distractions caused by inbound text messages versus inbound voice messages, and outbound replies through text messages versus outbound voice replies. A within-subject experiment was conducted in a driving simulator where 31 participants received and answered text messages under four conditions: they received notifications (1) on a mobile phone, (2) on a smartwatch, and (3) on a speaker, and then responded orally to these messages. They also (4) received messages in a "texting" condition where they had to reply through text to the notifications. Eye tracking gaze distribution results show that participants were more distracted in the smartwatch condition than in the mobile phone condition, they were less distracted in the speaker condition than in the phone condition, and they were more distracted in the texting condition than in any of the others. The participants' driving performance remained the same in all conditions except in the texting condition, wherein it became worse. Eye tracking and pupillometry results suggest that participants' mental workload might be lower in the texting condition than in the other three conditions, although this result might be caused by a higher number of glances at the device in that condition. This study contributes to a better understanding of the distraction potential of smartwatches as well as identifying vocal assistants as the least distracting way of communicating while driving a vehicle. Industry leaders could become a key factor in informing the public of the smartwatch's potential for distraction.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".