Acute smartphone use impairs vigilance and inhibition capacities
Bibliographic record
Abstract
Smartphones are now in very widespread use, and concerns have arisen about potential detrimental effects, even with acute use. These adverse consequences are often linked to the emergence of mental fatigue. While the cognitive implications of fatigue are well-documented, knowledge about the specific influence of acute smartphone use on cognitive performance remains scarce. The aim of this study was therefore to investigate the impact of acute smartphone use on cognitive performance. It included two experiments: one designed to assess the impact of smartphone use on vigilance, and the other focusing on evaluating inhibition capacities. In Experiment 1, two groups of 40 participants completed a Psychomotor Vigilance Task (PVT) before and after using a smartphone for 45 min (experimental group), or before and after watching a documentary (control group). In Experiment 2, two groups of 40 participants were subjected to a similar experimental design but had to perform a Go/NoGo task instead of a PVT. Mental fatigue and drowsiness were evaluated with visual analog scales before and after smartphone use and watching a documentary. Results suggested that both watching a documentary and using a smartphone for 45 min increased subjective mental fatigue and drowsiness. Watching the documentary did not impair cognitive performance. Reaction times on the PVT and number of errors on NoGo trials in the Go/NoGo task were higher among the participants in the smartphone condition. These results indicate reduced vigilance and impaired inhibition capacities only after smartphone use. We conclude that acute smartphone use induces mental fatigue and decreases cognitive performance. Further research is needed to understand the mechanisms underlying this decline in cognitive performance.
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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 | 0.000 | 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".