scholarly journals A multimodal analysis of physical activity, sleep, and work shift in nurses with wearable sensor data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiantian Feng ◽  
Brandon M. Booth ◽  
Brooke Baldwin-Rodríguez ◽  
Felipe Osorno ◽  
Shrikanth Narayanan
Keyword(s):  
Physical Activity ◽  
Life Satisfaction ◽  
Sleep Quality ◽  
Work Performance ◽  
Wearable Sensors ◽  
Night Shift ◽  
Hospital Setting ◽  
Sensor Data ◽  
Large Hospital ◽  
Circadian Misalignment

AbstractNight shift workers are often associated with circadian misalignment and physical discomfort, which may lead to burnout and decreased work performance. Moreover, the irregular work hours can lead to significant negative health outcomes such as poor eating habits, smoking, and being sedentary more often. This paper uses commercial wearable sensors to explore correlates and differences in the level of physical activity, sleep, and circadian misalignment indicators among day shift nurses and night shift nurses. We identify which self-reported assessments of affect, life satisfaction, and sleep quality, are associated with physiological and behavioral signals captured by wearable sensors. The results using data collected from 113 nurses in a large hospital setting, over a period of 10 weeks, indicate that night shift nurses are more sedentary, and report lower levels of life satisfaction than day-shift nurses. Moreover, night shift nurses report poorer sleep quality, which may be correlated with challenges in their attempts to fall asleep on off-days.

Getting Enough Sleep: On the Importance of Collecting Longitudinal Data from Wearables to Assess Sleep Quality and Seasonal Effects on Variability in Daily Activity (Preprint)

2021 ◽  
Author(s):  
María Óskarsdóttir ◽  
Anna Sigridur Islind ◽  
Elias August ◽  
Erna Sif Arnardóttir ◽  
Francois Patou ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Data Collection ◽  
Sleep Quality ◽  
Daily Activity ◽  
Hospital Environment ◽  
Sensor Data ◽  
Seasonal Patterns ◽  
Sleep Patterns ◽  
Sleep Habits

BACKGROUND The method considered the gold standard for recording sleep is a polysomnography, where the measurement is performed in a hospital environment for 1-3 nights. This requires subjects to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. For longer studies with actigraphy, 3-14 days of data collection is typically used for both clinical and research studies. OBJECTIVE The primary goal of this paper is to investigate if the aforementioned timespan is sufficient for data collection, when performing sleep measurements at home using wearable and non-wearable sensors. Specifically, whether 3-14 days of data collection sufficient to capture an individual’s sleep habits and fluctuations in sleep patterns in a reliable way for research purposes. Our secondary goals are to investigate whether there is a relationship between sleep quality, physical activity, and heart rate, and whether individuals who exhibit similar activity and sleep patterns in general and in relation to seasonality can be clustered together. METHODS Data on sleep, physical activity, and heart rate was collected over a period of 6 months from 54 individuals in Denmark aged 52-86 years. The Withings Aura sleep tracker (non-wearable) and Withings Steel HR smartwatch (wearable) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. RESULTS Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We show specifically that in order to get more robust individual assessment of sleep and physical activity patterns through wearable and non-wearable devices, a longer evaluation period than 3-14 days is necessary. Additionally, we found seasonal patterns in sleep data related to changing of the clock for Daylight Saving Time (DST). CONCLUSIONS We demonstrate that over two months worth of self-tracking data is needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3-14 days for sleep quality assessment and call for rethinking standards when collecting data for research purposes. Seasonal patterns and DST clock change are also important aspects that need to be taken into consideration, and designed for, when choosing a period for collecting data. Furthermore, we suggest using consumer-grade self-trackers (wearable and non-wearable ones) to support longer term evaluations of sleep and physical activity for research purposes and, possibly, clinical ones in the future.

scholarly journals Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020 ◽  
Vol 10 (20) ◽  
pp. 7122
Author(s):  
Ahmad Jalal ◽  
Mouazma Batool ◽  
Kibum Kim
Keyword(s):  
Physical Activity ◽  
Human Health ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Tree Classifier ◽  
Combined Features

The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

Optimal Metamodeling to Interpret Activity-Based Health Sensor Data

2017 ◽  
Author(s):  
Souma Chowdhury ◽  
Ali Mehmani
Keyword(s):  
Physical Activity ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Medical Diagnostics ◽  
Sensor Data ◽  
Sensitivity Testing ◽  
Data Set ◽  
Functional Relationships ◽  
Median Error ◽  
The Impact

Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features — heart rate, QRS time, and QR ratio in each heartbeat period — models with median error of <25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.

scholarly journals Can a ‘rewards-for-exercise app’ increase physical activity, subjective well-being and sleep quality? An open-label single-arm trial among university staff with low to moderate physical activity levels

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sakari Lemola ◽  
Anna Gkiouleka ◽  
Brieze Read ◽  
Anu Realo ◽  
Lukasz Walasek ◽  
...  
Keyword(s):  
Physical Activity ◽  
Life Satisfaction ◽  
Sleep Quality ◽  
Positive Affect ◽  
Well Being ◽  
Moderate Physical Activity ◽  
Activity Levels ◽  
Subjective Well Being ◽  
Open Label ◽  
Physical Activity Levels

Abstract Background This study examined the impact of a ‘rewards-for-exercise’ mobile application on physical activity, subjective well-being and sleep quality among 148 employees in a UK university with low to moderate physical activity levels. Methods A three-month open-label single-arm trial with a one-year follow-up after the end of the trial. Participants used the Sweatcoin application which converted their outdoor steps into a virtual currency used for the purchase of products available at the university campus’ outlets, using an in-app marketplace. The primary outcome measure was self-reported physical activity. Secondary measures included device-measured physical activity, subjective well-being (i.e., life satisfaction, positive affect, negative affect), and self-reported sleep quality. Results The findings show an increase in self-reported physical activity (d = 0.34), life satisfaction (d = 0.31), positive affect (d = 0.29), and sleep quality (d = 0.22) during the three-month trial period. Conclusion The study suggests that mobile incentives-for-exercise applications might increase physical activity levels, positive affect, and sleep quality, at least in the short term. The observed changes were not sustained 12 months after the end of the trial.

scholarly journals Quantifying Caregiver Movement when Measuring Infant Movement across a Full Day: A Case Report

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2886 ◽  
Author(s):  
Judy Zhou ◽  
Sydney Y. Schaefer ◽  
Beth A. Smith
Keyword(s):  
Physical Activity ◽  
Case Report ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Future Research ◽  
Wearable Sensor ◽  
Sensor Signal ◽  
Background Motion ◽  
Full Day ◽  
Leg Movements

There is interest in using wearable sensors to measure infant movement patterns and physical activity, however, this approach is confounded by caregiver motion. The purpose of this study is to estimate the extent that caregiver motion confounds wearable sensor data in full-day studies of infant leg movements. We used wearable sensors to measure leg movements of a four-month-old infant across 8.5 hours, during which the infant was handled by the caregiver in a typical manner. A researcher mimicked the actions of the caregiver with a doll. We calculated 7744 left and 7107 right leg movements for the infant and 1013 left and 1115 right “leg movements” for the doll. In this case, approximately 15% of infant leg movements can be attributed to background motion of the caregiver. This case report is the first step toward removing caregiver-produced background motion from the infant wearable sensor signal. We have estimated the size of the effect and described the activities that were related to noise in the signal. Future research can characterize the noise in detail and systematically explore different methods to remove it.

Relationship between sleep disorders, HIV status and cardiovascular risk: cross-sectional study of long-haul truck drivers from Southern Africa

2021 ◽  
pp. oemed-2020-107208
Author(s):  
Johanna Roche ◽  
Alinda G Vos ◽  
Samanta T Lalla-Edward ◽  
W D Francois Venter ◽  
Karine Scheuermaier
Keyword(s):  
Sleep Quality ◽  
Sleep Disorders ◽  
Sleep Duration ◽  
Southern Africa ◽  
Night Shift ◽  
Truck Drivers ◽  
Hiv Status ◽  
Cvd Risk ◽  
Circadian Misalignment ◽  
Haul Truck

ObjectivesLong-haul truck drivers (TDs) may have lifestyles that promote cardiovascular disease (CVD), including diet, sleep and activity issues. Most studies conducted among truckers investigated the relationship between poor sleep and cardiometabolic health, but none assessed whether suspected obstructive sleep apnoea (OSA) and shortened sleep were associated with markers of cardiometabolic risk. We determined whether sleep disorders and circadian misalignment were associated with chronic inflammation and CVD risk in TDs from Southern Africa.MethodsParticipants were recruited at roadside wellness centres in Gauteng and Free State Provinces, South Africa. OSA risk was assessed using the Berlin Questionnaire, while sleep duration and sleep quality were assessed using items from the Pittsburgh Sleep Quality Index. Clinical information, neck circumference (NC), metabolic profile, elevated BP, HIV status and C-reactive protein (CRP) were collected. CVD risk was assessed using the Framingham Risk Score (FRS).ResultsOut of 575 participants aged on average 37.7 years, 17.2% were at OSA risk, 72.0% had elevated BP, 9.4% had HIV and 28.0% were obese. Mean sleep duration was 7.4±1.8 hours, and 49.6% reported working night shift at least once a week. Shortened sleep, OSA risk, age, body mass index, NC and years as full-time TD were associated with greater FRS independently of HIV status and night shift. Working night shift was associated with higher CRP levels in HIV+ compared with HIV− participants.ConclusionsCircadian misalignment in HIV, and OSA and short sleep duration in all truckers were associated with increased CVD risk. Truckers should be given careful attention in terms of health management and sleep education.

scholarly journals SARS-CoV-2 infection in health care workers: a retrospective analysis and model study

2020 ◽  
Author(s):  
Yansen Bai ◽  
Xuan Wang ◽  
Qimin Huang ◽  
Han Wang ◽  
David Gurarie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sleep Quality ◽  
Night Shift ◽  
Hospital Setting ◽  
Stress Index ◽  
Poor Sleep ◽  
Working Pressure ◽  
Model Study ◽  
Retrospective Data Collection ◽  
Fundamental Research

ABSTRACTBackgroundThere had been a preliminary occurrence of human-to-human transmissions between healthcare workers (HCWs), but risk factors in the susceptibility for COVID-19, and infection patterns among HCWs have largely remained unknown.MethodsRetrospective data collection on demographics, lifestyles, contact status with infected subjects for 118 HCWs (include 12 COVID-19 HCWs) from a single-center. Sleep quality and working pressure were evaluated by Pittsburgh Sleep Quality Index (PSQI) and The Nurse Stress Index (NSI), respectively. Follow-up duration was from Dec 25, 2019, to Feb 15, 2020. Risk factors and transmission models of COVID-19 among HCWs were analyzed and constructed.FindingsA high proportion of COVID-19 HCWs had engaged in night shift-work (75.0% vs. 40.6%) and felt they were working under pressure (66.7% vs. 32.1%) than uninfected HCWs. COVID-19 HCWs had higher total scores of PSQI and NSI than uninfected HCWs. Furthermore, these scores were both positively associated with COVID-19 risk. An individual-based model (IBM) estimated the outbreak duration among HCWs in a non-typical COVID-19 ward at 62-80 days and the basic reproduction number R0 =1.27 [1.06, 1.61]. By reducing the average contact rate per HCW by a 1.35 factor and susceptibility by a 1.40 factor, we can avoid an outbreak of the basic case among HCWs.InterpretationPoor sleep quality and high working pressure were positively associated with high risks of COVID-19. A novel IBM of COVID-19 transmission is suitable for simulating different outbreak patterns in a hospital setting.FundingFundamental Research Funds for the Central Universities

scholarly journals Wavelet-Based Analysis of Physical Activity and Sleep Movement Data from Wearable Sensors among Obese Adults

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3710 ◽  
Author(s):  
Rahul Soangra ◽  
Vennila Krishnan
Keyword(s):  
Physical Activity ◽  
Health Care ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Physical Activities ◽  
Sensor Data ◽  
Time Interval ◽  
Wearable Sensor ◽  
Obesity Management ◽  
Sleep Behavior

Decreased physical activity in obese individuals is associated with a prevalence of cardiovascular and metabolic disorders. Physicians usually recommend that obese individuals change their lifestyle, specifically changes in diet, exercise, and other physical activities for obesity management. Therefore, understanding physical activity and sleep behavior is an essential aspect of obesity management. With innovations in mobile and electronic health care technologies, wearable inertial sensors have been used extensively over the past decade for monitoring human activities. Despite significant progress with the wearable inertial sensing technology, there is a knowledge gap among researchers regarding how to analyze longitudinal multi-day inertial sensor data to explore activities of daily living (ADL) and sleep behavior. The purpose of this study was to explore new clinically relevant metrics using movement amplitude and frequency from longitudinal wearable sensor data in obese and non-obese young adults. We utilized wavelet analysis to determine movement frequencies on longitudinal multi-day wearable sensor data. In this study, we recruited 10 obese and 10 non-obese young subjects. We found that obese participants performed more low-frequency (0.1 Hz) movements and fewer movements of high frequency (1.1–1.4 Hz) compared to non-obese counterparts. Both obese and non-obese subjects were active during the 00:00–06:00 time interval. In addition, obesity affected sleep with significantly fewer transitions, and obese individuals showed low values of root mean square transition accelerations throughout the night. This study is critical for obesity management to prevent unhealthy weight gain by the recommendations of physical activity based on our results. Longitudinal multi-day monitoring using wearable sensors has great potential to be integrated into routine health care checkups to prevent obesity and promote physical activities.

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