Early myopericarditis diagnosed in a 31-year-old patient using smartwatch technology: A case report

INTRODUCTION

Smartwatches, wrist-mounted devices with computing capacity able to connect with other devices via short-range wireless networking, are today commonly used by the general population to monitor their health status using specific applications. Currently, these devices offer new possibilities in remote health care monitoring and integration with other applications, through alert notifications, collection of personal data by a variety of sensors and the storage of these data. Several companies are introducing smartwatches with "health status" monitoring software with multiple functions, i.e. electrocardiogram (ECG) sensors. Recently, detection of atrial fibrillation based on heart rate monitoring by optical sensors resulted to be feasible and reliable when using the Apple Watch® and its corresponding application. Indeed, previous case reports highlighted its sensitivity in detecting morphological changes typical of the Acute Coronary Syndrome.

CASE REPORT

We report the case of a healthcare worker, who experienced chest pain and diffuse myalgia, detected ECG alterations in the ST segment, and reached the Emergency Department Myopericarditis was diagnosed and treated promptly to prevent complications.

DISCUSSION

Acute viral myocarditis and pericarditis are clinical conditions, usually characterized by 21 a benign course that does not require medical evaluation. However, ventricular arrhythmias are also common in viral myocarditis, and the latter is associated with a large proportion of sudden cardiac deaths in the young population without previous structural heart disease. In this case report, smartwatch technology allowed the preventive implementation of interventions against potentially life-threatening complications. Further developments in smartwatch technology could lead to more sensitive and specific diagnostic algorithms for conditions that require immediate medical intervention.

Sleep Quality and Its Relationship to Anxiety and Hardiness in a Cohort of Frontline Italian Nurses during the First Wave of the COVID-19 Pandemic

INTRODUCTION

The COVID-19 pandemic has had a considerable impact on the psychological and psychopathological status of the population and health care workers in terms of insomnia, anxiety, depression, and post-traumatic stress disorder. The primary aim of this study was to describe and evaluate the impact of the pandemic on insomnia levels of a cohort of Italian nurses, particularly those involved in the care of COVID-19 patients. The secondary aim was to identify the interaction between insomnia and hardiness, anxiety, and sleep disturbances.

MATERIALS AND METHODS

A descriptive-exploratory study was conducted using an online survey during the first wave of the COVID-19 pandemic (March to July 2020). The questionnaire consisted of multiple-choice, open-ended, closed, and semi-closed questions. The psychometric tools administered were the Dispositional Resilience Scale (DRS-15), the State-Trait Anxiety Inventory (STAI-Y), and the Insomnia Severity Index (ISI).

RESULTS

a cohort of 1167 nurses fully completed the questionnaire (86.2% of total respondents). The insomnia scale survey showed an increase in post-pandemic scores compared to those before the pandemic, implying that insomnia levels increased after the first pandemic wave. Insomnia scores were directly correlated with anxiety levels (r = 0.571; p ≤ 0.05) and inversely correlated with hardiness levels (r = -0.324; p < 0.001). Multivariate analysis revealed the following protective factors: not having worked in COVID-19 wards, high levels of hardiness (commitment), and the presence of high pre-pandemic insomnia disorder. The main risk factor for insomnia reported in the analysis was a high anxiety score.

DISCUSSION AND CONCLUSION

Anxiety represented the main risk factor for insomnia severity in our sample, while hardiness was confirmed as a protective factor. Thus, it is necessary to design further studies to identify additional risk factors for poor sleep quality and to develop educational courses and strategies aimed at enhancing rest and sleep quality, especially for frontline nurses.

The Effectiveness of NIV and CPAP Training on the Job in COVID-19 Acute Care Wards: A Nurses' Self-Assessment of Skills

Background: Noninvasive ventilation (NIV) in COVID-19 patients outside of intensive care unit (ICU) settings was a feasible support during the pandemic outbreak. The aim of this study was to assess the effectiveness of an “on the job” NIV training program provided to 66 nurses working in 3 COVID-19 wards in an Italian university hospital. Methods: A quasi-experimental longitudinal before−after study was designed. The NIV Team education program, provided by expert ICU nurses, included: 3 h sessions of training on the job during work-shifts about the management of helmet-continuous positive airway pressure (CPAP) Venturi systems, and NIV with oronasal and full-face masks. An eleven-item “brief skills self-report tool” was administered before and after the program to explore the perception of NIV education program attendees about their level of skills. Results: In total, 59 nurses responded to the questionnaire. There was an improvement in the skill levels of the management of Helmet-CPAP (median before training 2, inter-quartile range (IQR) 0−6; median after training 8, IQR 3−9; p < 0.0001), and mask-NIV (median before training 2, IQR 0−6; median after training 8, IQR 3−9; p < 0.0001). Conclusions: Training on the job performed by expert ICU nurses can be a valuable and fast means to implement new Helmet-CPAP and mask-NIV skills outside of ICUs.

Design of a wearable device for physiological parameter monitoring in a COVID setting

The study focuses on the realization of an accurate device for the detection of different physiological parameters. It has been realized a simple portable system containing the necessary electronics and ensuring the monitoring of the blood oxygenation, the body temperature, the air quality, the respiratory rate and the ECG. The main processing unit consists in a Raspberry Pi Zero W connected to the Healthy Pi4. The latter provides the interface for the clinical pulse-oxymeter while the measures of temperature and quality air are provided using the I2C protocol. The Bluetooth module is finally used to provide the ECG and blood rate data. The collected data are elaborated using Matlab and Python. To evaluate the accuracy of the realized device some experimental tests have been conducted on different subjects, comparing subjects working in Covid area with others resting at home. In both cases the monitoring time was 4 hours. Results have shown good performances of the system, detecting accurately the differences of the parameters values between the two situations. The usability of the device was assessed by administering a questionnaire to the healthcare personnel involved in the experimentation. The outcome shows a good usability of the system as well as an acceptable dressing time.