Assessment of Key Health and Wellness Indicators Among North Carolina Emergency Medical Service Providers

Brice, Jane H.; Cyr, Julianne; Hnat, Andrew T.; Wei, Trent; S, Principe; Thead, Sarah E.; Delbridge, Theodore R.; Winslow, James E.; Studnek, Jonathan R.; Fernández, Antonio R. Martínez; Forrest, Emily E.

doi:10.1080/10903127.2018.1489017

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Previous work has shown that EMS personnel have a high prevalence of poor health for certain LS7 components such as obesity, poor diet, and inadequate physical activity. 7 , 8 , 9 EMS personnel also have a high prevalence of non‐traditional CVD risk factors, such as poor sleep quality, short or long sleep duration, and high stress, 10 , 11 , 12 , 13 which may further increase the risk of developing CVD. No prior studies have examined the relationship between sleep, stress, and CVH within the occupational group of EMS personnel.…”

Section: Introductionmentioning

confidence: 99%

“…However, not all EMS personnel work in a fire‐based setting, and their CVD risk remains unclear. Previous work has shown that EMS personnel have a high prevalence of poor health for certain LS7 components such as obesity, poor diet, and inadequate physical activity 7–9 . EMS personnel also have a high prevalence of non‐traditional CVD risk factors, such as poor sleep quality, short or long sleep duration, and high stress, 10–13 which may further increase the risk of developing CVD.…”

Section: Introductionmentioning

confidence: 99%

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Associations between sleep, stress, and cardiovascular health in emergency medical services personnel

et al. 2021

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Objective Our objective was to quantify the associations between sleep duration and perceived and chronic stress with ideal cardiovascular health (CVH) among emergency medical services (EMS) personnel from county‐based EMS agencies. Methods We conducted a cross‐sectional survey of cardiovascular disease (CVD)‐free EMS personnel from 4 US EMS agencies. The questionnaire consisted of the Pittsburgh Sleep Quality Index (PSQI), Perceived Stress Scale (PSS), Chronic Burden Scale, and the CVH components (smoking, body mass index, physical activity, diet, blood glucose, blood pressure, cholesterol, each scored 0–2 points). The components were summed and ideal CVH considered 11–14 points. Mixed effects logistic regression models with a random intercept for agency were used to estimate the odds of ideal CVH for good sleep quality (PSQI < 5 points), recommended sleep duration (7 to < 9h), low perceived stress (PSS < 26 points), and low chronic stress (0 recent stressful events). Results We received 379 responses (response rate = 32%). There was low prevalence of good sleep quality (23%) and recommended sleep duration (25%), but 95% reported low perceived stress, and 33% had low chronic stress. Ideal CVH was reported by 30%. No significant associations between ideal CVH and sleep quality, perceived stress, or chronic stress were found. There was a nearly 2‐fold increase in the odds of ideal CVH with recommended sleep duration (odds ratio: 1.83, 95% confidence interval: 1.08–3.10). Conclusion In this sample of EMS personnel, only recommended sleep duration was associated with ideal CVH. Future longitudinal studies are needed to understand the relationship between sleep, stress, and CVD in this understudied occupational group.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Associations between sleep, stress, and cardiovascular health in emergency medical services personnel

et al. 2021

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“…Patient care also includes wellness. In this case, IoMT can leverage the periodic and continuous measurement and evaluation of wellness indicators [62,63]. Solutions include using smart thermometers capable of sharing essential data with medical applications [64].…”

Section: Internet Of Medical Things (Iomt) In Healthcarementioning

confidence: 99%

CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security

Dadkhah,

Carlos Pinto Neto,

Ferreira

et al. 2024

Preprint

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The Internet of Things (IoT) has a growing presence in society's daily lives. These lightweight devices can be easily deployed and maintained, enabling extensive adoption in different environments. Furthermore, one of the most promising areas for using IoT devices is healthcare, comprising devices referred to as the Internet of Medical Things (IoMT). Several examples of healthcare services are supported by IoMT devices, e.g., continuous health monitoring. Conversely, there is an increasing concern with the cybersecurity aspects of these devices, and several attacks against IoT infrastructures have been launched in the past few years. These cybersecurity concerns also apply to healthcare applications, where the tradeoff between the benefits and security of IoMT devices must be observed. Given the complexity and amount of data IoMT network traffic generates, advanced methods become especially useful in these environments. Although Machine Learning (ML) brings various techniques and solutions to improve cyberattack detection, prevention, and mitigation, essential features are not addressed in the current state-of-the-art benchmark dataset contributions. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of IoMT security solutions. In order to accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT, and Bluetooth). These attacks are categorized into five classes: DDoS, DoS, Recon, MQTT, and spoofing. This effort aims to establish a baseline complementary to the state-of-the-art contributions. The outcome supports researchers in investigating and developing new solutions to make healthcare systems more secure using different mechanisms (e.g., machine learning - ML). This research goes beyond merely conducting attacks on IoMT devices. We also attempt to capture the lifecycle of these devices in different vital phases, from the moment they join the network until they leave, which is called profiling. Profiling allows the different classifiers to identify anomalies of each device individually in the healthcare network. The CICIoMT2024 dataset has been published on CIC's dataset page, making it available for other researchers to use.

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EMS clinician wellness

Patterson¹,

Weaver²,

Hostler³

2021

Emergency Medical Services

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Assessment of Key Health and Wellness Indicators Among North Carolina Emergency Medical Service Providers

Cited by 11 publications

References 15 publications

Associations between sleep, stress, and cardiovascular health in emergency medical services personnel

Associations between sleep, stress, and cardiovascular health in emergency medical services personnel

CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security

EMS clinician wellness

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