Relationship Between Heart Rate and Perceived Stress in Intensive Care Unit Residents: Exploratory Analysis Using Fitbit Data
Notice bibliographique
Résumé
BACKGROUND: Intensive care unit (ICU) residents are exposed to high stress levels due to the intense nature of their work, which can impact their mental health and job performance. Heart rate measured through wearable devices has the potential to provide insights into residents' self-reported stress and aid in developing targeted interventions. OBJECTIVE: This exploratory study aims to analyze continuous heart rate data and self-reported stress levels and stressors in ICU residents to examine correlations between physiological responses, stress levels, and daily stressors reported. METHODS: A secondary data analysis was conducted on heart rate measurements and stress assessments collected from 57 ICU residents over a 3-week period using Fitbit Charge 3 devices. These devices captured continuous physiological data alongside daily surveys that assessed stress levels and identified stressors. The study used Spearman rank correlation, point-biserial correlation analysis, 2-tailed paired t tests, and mixed-effect models to analyze the relationship between heart rate features and stress indicators. RESULTS: The findings reveal complex interactions between stress levels and heart rate patterns. The correlation analysis between stress levels and median heart rate values across different percentile ranges showed that lower percentile heart rates (bottom 5%, 10%, 25%, and 50%) had modest correlations with stress, whereas higher percentiles (top 50%, 25%, 10%, and 5%) did not correlate significantly (all P>.05). The 2-tailed paired t test indicated significant differences in stress levels reported in midday versus end-of-day surveys (P<.001), although these changes in stress levels were not consistently reflected in heart rate patterns. Additionally, we explored and found that stressors related to "other health" issues had the highest positive correlation with stress level changes from midday to end-of-day surveys. However, the weak effect of these stressors on peak heart rate suggests that their impact on physiological measures like heart rate is not yet clear. According to our mixed-effects model, stress levels significantly influenced heart rate variations when hierarchical data were taken into account (P=.03), meaning that as the stress level increased, there was a significant increase in mean heart rate. CONCLUSIONS: This study highlights the complexity of using heart rate as an indicator of stress, particularly in high-stress environments like the ICU. Our findings suggest that while heart rate is found to correlate with self-reported stress in the mixed-effect model, its impact is modest, and it should be combined with other physiological and psychological measures to obtain a more accurate and comprehensive assessment of residents' stress levels.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».