Investigating the Effects of Notification Sound Frequency on Cardiovascular and Cognitive Responses using the Digital Non-Verbal Stroop Colour-Word Test
Bibliographic record
Abstract
Due to the prevalence of notifications from electronic devices in students’ everyday lives, this study investigates the physiological and cognitive effects of cellphone notifications using a digital non-verbal Stroop Colour Word Test. The study aims to assess cardiovascular measures, distraction, and stress with the objective to determine the relationship between notification frequency and cardiovascular responses, in addition to its impact on the Stroop test performance. Participants ( N =20) were recruited using convenience sampling. Baseline cardiovascular measures were taken after 5 minutes of rest, following by three rounds of digital non-verbal Stroop test assessing Stroop performance, distraction, and stress under no, low-frequency and high-frequency notification conditions. Notification frequency was found to significantly influence cardiovascular measures and Stroop test performance, with a notable increase in rate pressure product across varying notification levels. Distraction levels and self-perceived stress increased with notification frequency, significantly impacting cognitive performance and cardiovascular responses. Increased notification frequency correlated with heightened cardiovascular strain, reduced cognitive performance, and increased distraction and stress levels, supporting the dose-dependent impact of notification on physiological and cognitive performance. The findings highlight the detrimental effects of frequent digital notification interruptions on cardiovascular and cognitive health, suggesting long-term implications for productivity and learning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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".