Predicting Risk of Heat-Related Injuries for Individuals Wearing Personal Protective Equipment Using Smartwatches: Feasibility Observational Study
Notice bibliographique
Résumé
Background: The risk of developing heat-related illness increases when personal protective equipment (PPE) is worn, especially in hot and humid environments. While cooling strategies are effective, they must be applied preemptively or delivered promptly, which can be difficult if individuals are working in dangerous environments or wearing contaminated PPE. Wearable sensors can be leveraged to continuously monitor health including heart rate, respiration rate, blood oxygen levels, and physical activity. Objective: This study aims to (1) evaluate the use of wearable sensors for monitoring the real-time health of individuals wearing PPE to mitigate the risk of developing a heat-related illness and enable timely intervention, (2) understand how PPE may affect smartwatch data quality and comfort, and (3) identify circumstances in which people wearing PPE may not be able to wear a smartwatch. Methods: Individuals participating in planned field trainings or exercises where PPE was being worn were asked to wear Garmin Fenix 6 smartwatch (Garmin Ltd) before, during, and after the event to monitor health and recovery. These convenience cohorts were selected to understand the feasibility of using smartwatches with different types of PPE (ie, level C PPE and firefighter gear) for different types of training (ie, a simulated environmental cleanup exercise and skill and tactical maneuver training for new firefighter recruits). Results: Two data collections were conducted using the Garmin Fenix 6 smartwatch to assess wearability, data quality, and data accuracy. For the first effort, participants wore the watch for 3.9-5.1 days, and wear compliance ranged from 83.8% to 99.9%. For the second effort, participants wore the watch for the exercise only, which was 3.5 hours. Participants were able to wear the watches for the entire time that they were wearing PPE and did not report any adverse events. Changes in heart rate corresponded with changes in physical activity, providing evidence that physiology can be acceptably monitored during physical activity. Heart rate data artifact ranged between 5.8% and 9.3% and was highest for the control participant (second data collection) who was not wearing PPE. Conclusions: Based on the results obtained from the 8 pilot users, the Garmin Fenix 6 smartwatch is an appropriate choice for continuously monitoring the health of individuals wearing PPE. The watch can be tolerated for extended wear periods and data quality is sufficient for monitoring heart rate and predicting core body temperature.
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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».