Study for the Design of a Protocol to Assess the Impact of Stress in the Quality of Life of Workers

Concheiro-Moscoso, Patricia; Groba, Betania; Martínez-Martínez, Francisco José; Miranda-Duro, María del Carmen; Nieto-Riveiro, Laura; Pousada, Thais; Queirós, Cristina; Pereira, Javier

doi:10.3390/ijerph18041413

Cited by 9 publications

(8 citation statements)

References 63 publications

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“…[ 3 ]. Long working hours, reduced sleep opportunities with minimal recovery time, shift work, and work stress contribute to impaired physical, cognitive, and emotional functioning [ 4 , 5 ]. The harmful effects of poor sleep quality and drowsiness in healthcare professionals include reduced cognitive skills [ 6 ], reduced psychomotor performance and productivity [ 7 ], and increased risk of causing medical errors and compromising patient health and safety [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Poor Sleep Quality and Daytime Sleepiness in Health Professionals: Prevalence and Associated Factors

Carvalho

Barcelos

Oliveira

et al. 2021

IJERPH

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This study aimed to examine the prevalence rates of poor quality sleep and daytime sleepiness in health professionals and their association with socioeconomic, lifestyle, and anthropometric factors and psychosocial work conditions. This cross-sectional study was performed with health professionals from various public and private hospitals in Rio Verde, Goiás, Brazil. Daytime sleepiness and sleep quality were assessed using the Epworth Sleepiness Scale and the Pittsburgh Sleep Quality Index, respectively. The variables were analyzed using multiple hierarchical Poisson regression in the statistical package Stata version 13.0. A total of 244 health professionals participated in this study (women, 78.28%). The rates of abnormal daytime sleepiness and poor sleep quality were 50.41% (n = 123) and 55.74% (n = 136), respectively. Reduced physical activity was associated with poor sleep quality (prevalence ratio (PR) = 1.32; 95% confidence interval (CI) 1.02–1.70, p = 0.035). Age between 20 and 29 years (PR = 2.59; 95% CI 1.37–4.91, p = 0.021) and 30 and 49 years (PR = 2.09; 95% CI 1.12–3.91, p = 0.021), as well as excessive alcohol consumption (PR = 1.29; 95% CI 1.01–1.66, p = 0.048), were risk factors for daytime sleepiness. Conversely, adequate bodyweight (PR = 0.52; 95% CI 0.33–0.82, p = 0.005) was considered a protective factor. The present findings suggest high rates of abnormal daytime sleepiness and poor sleep quality among healthcare professionals. We emphasize the importance of physical activity, adequate weight, and healthy habits for better quality sleep and reduced daytime sleepiness.

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Section: Introductionmentioning

confidence: 99%

Poor Sleep Quality and Daytime Sleepiness in Health Professionals: Prevalence and Associated Factors

Carvalho

Barcelos

Oliveira

et al. 2021

IJERPH

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“…Furthermore, this model allows adding wearable devices with more accuracy physiological sensors [ 11 , 13 , 31 , 60 ]. However, when cheap wearable devices become more popular in the market, manufacturers will probably include more accuracy sensors for monitoring biosignals and physical activity [ 28 , 29 , 34 , 35 , 36 , 37 ]. For this research, we opted for massive and cheap devices that are probably the most used by people in their daily lives.…”

Section: Discussionmentioning

confidence: 99%

“…Medium range devices with precise sensors have more extensive use, targeted to high-performance athletes, mainly for fitness and sports, and these include, for example, Garmin and Microsoft Band [ 10 , 12 , 32 , 33 ]. Affordable devices for all audiences are used for general purposes, for instance, the Xiaomi Mi Band [ 10 , 34 , 35 , 36 , 37 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

Santamaría-Granados

Mendoza-Moreno

Chantre-Astaiza

et al. 2021

Sensors

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The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

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“…Thus, in this project, workers have been able to have an insight and be aware of their stress level, different stressors, and other parameters related to their activity, sleep, and heart rate. This data can help the worker to improve in their routines and habits and add strategies to reduce their stress level and improve their quality of life [1].…”

Section: Discussionmentioning

confidence: 99%

SQoF-WEAR Project. The Use of Wearable Devices to Identify the Impact of Stress on Workers’ Quality of Life

Concheiro-Moscoso

Groba

Monteiro-Fonseca

et al. 2021

The 4th XoveTIC Conference

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(1) Background: Stress is a major public health problem due to its relevant health, social and economic repercussions. Moreover, stress can be associated with work; when stress increases over time, burnout can occur, an occupational phenomenon recognized by the WHO in 2019. There is interest in the use of wearable devices to monitor and control stressors and their influence on the condition of workers. This study aims to identify the level of job stress and its influence on the quality of life of workers. (2) Methods:This longitudinal study was carried out between the end of May and mid-July 2021. Three assessment tools along with a daily and a weekly questionnaire were computerized through the RedCap platform. The participants had to fill out the diary and weekly questionnaires and wear a Xiaomi Mi Band 5 during the project. (3) Results and discussion: Thirty-six workers from the University of Coruña and from the University of Porto participated in the project. This study promotes the awareness of workers regarding their work stress and the influence of this factor on their quality of life using physiological (e.g., activity, sleep, and heart rate) and psychological indicators (self-report questionnaires in different moments).

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Study for the Design of a Protocol to Assess the Impact of Stress in the Quality of Life of Workers

Cited by 9 publications

References 63 publications

Poor Sleep Quality and Daytime Sleepiness in Health Professionals: Prevalence and Associated Factors

Poor Sleep Quality and Daytime Sleepiness in Health Professionals: Prevalence and Associated Factors

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

SQoF-WEAR Project. The Use of Wearable Devices to Identify the Impact of Stress on Workers’ Quality of Life

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