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Record W4410958303 · doi:10.1037/emo0001530

Cardiac responses to daily threats and challenges during wakefulness and sleep.

2025· article· en· W4410958303 on OpenAlexaff
William von Hippel, Jeongeun Kim, Finnbar Fielding, Emily R. Capodilupo, Gregory J. Grosicki, Kristen E. Holmes

Bibliographic record

VenueEmotion · 2025
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsImpact
Fundersnot available
KeywordsWakefulnessPsychologySleep (system call)Developmental psychologyCognitive psychologyAudiologyNeuroscienceElectroencephalographyMedicine

Abstract

fetched live from OpenAlex

This research examines cardiovascular response to everyday threats and challenges during wakefulness and sleep. Approximately 11,000 people, who comprised a diverse sample ethnically but not socioeconomically, completed three weekly morning and evening surveys in which they indicated whether they expected and experienced threats and challenges that day. Participants also provided measures of blood pressure on morning surveys and provided measures of average heart rate during the day and resting heart rate when asleep via their WHOOP biometric capture device. Enrollment began in April 2024, and data collection ceased in July 2024. Results indicated that both threat and challenge were associated with higher blood pressure and higher average heart rate during the day. In contrast, when people were asleep, threat was associated with higher resting heart rate but challenge was associated with lower resting heart rate. These results suggest that the body achieves more restorative sleep in preparation for perceived challenges but not for perceived threats, raising the possibility that the greater stress associated with threats disrupts the body's capacity for restorative sleep. The generalizability of these results to members of economically marginalized groups remains an open question. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.275
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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