Physiological and Cognitive Effects of Alarm Waking and Circadian Disruption: A Case Study on Heart Rate, Core Temperature, and Vigilance
Bibliographic record
Abstract
Modern lifestyles often rely on alarm clocks that disrupt the body’s natural sleep-wake cycle, yet their physiological consequences remain underexplored. This case study investigates the effects of alarm-induced waking versus natural waking on key physiological and cognitive parameters: heart rate, core body temperature, and reaction time. Over a two-week observational period, a 21-year-old male participant tracked biometric data during days involving alarm use and days without. Heart rate was measured using a smartwatch, core temperature with an oral thermometer, and cognitive vigilance via reaction-time trials on a mobile app. Results show that alarm-induced waking was associated with higher waking heart rates (mean: 61.5 bpm vs. 58.75 bpm), lower core body temperature (36.4°C vs. 36.55°C), and slower reaction times (mean: 0.372 sec vs. 0.315 sec) compared to free days. Sleep timing analysis revealed approximately 1.5 hours of social jetlag, defined as the discrepancy between mid-sleep time on workdays and free days. These findings suggest that alarm clocks interfere with physiological readiness by interrupting circadian-regulated processes such as thermoregulation, cardiovascular balance, and cognitive arousal. While limited by a single-subject design, this study demonstrates measurable physiological disruption from alarm clock use and reinforces the importance of aligning sleep schedules with biological rhythms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".