Work Habit-Related Sleep Debt; Insights From Factor Identification Analysis of Actigraphy Data

The present study investigates the factors of “Weekday sleep debt (WSD)” by comparing activity data collected from persons with and without WSD. Since it has been reported that the amount of sleep debt as well the difference between the social clock and the biological clock is associated with WSD, specifying the factors of WSD other than chronotype may contribute to sleep debt prevention. We recruited 324 healthy male employees working at the same company and collected their 1-week wrist actigraphy data and answers to questionnaires. Because 106 participants were excluded due to measurement failure of the actigraphy data, the remaining 218 participants were included in the analysis. All participants were classified into WSD or non-WSD groups, in which persons had WDS if the difference between their weekend sleep duration and the mean weekday sleep duration was more than 120 min. We evaluated multiple measurements derived from the collected actigraphy data and trained a classifier that predicts the presence of WSD using these measurements. A support vector machine (SVM) was adopted as the classifier. In addition, to evaluate the contribution of each indicator to WSD, permutation feature importance was calculated based on the trained classifier. Our analysis results showed significant importance of the following three out of the tested 32 factors: (1) WSD was significantly related to persons with evening tendency. (2) Daily activity rhythms and sleep were less stable in the WSD group than in the non-WSD group. (3) A specific day of the week had the highest importance in our data, suggesting that work habit contributes to WSD. These findings indicate some WSD factors: evening chronotype, instability of the daily activity rhythm, and differences in work habits on the specific day of the week. Thus, it is necessary to evaluate the rhythms of diurnal activities as well as sleep conditions to identify the WSD factors. In particular, the diurnal activity rhythm influences WSD. It is suggested that proper management of activity rhythm may contribute to the prevention of sleep debt.

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Perceived daily sleep need and sleep debt in adolescents: associations with daily affect over school and vacation periods

SLEEP ◽

10.1093/sleep/zsab190 ◽

2021 ◽

Author(s):

Lin Shen ◽

Joshua F Wiley ◽

Bei Bei

Keyword(s):

Sleep Duration ◽

Multilevel Models ◽

Weighted Average ◽

Daily Basis ◽

Future Research ◽

The Past ◽

Sleep Debt ◽

Daily Affect ◽

Day Of The Week ◽

The Difference

Abstract Study Objectives To describe trajectories of perceived daily sleep need and sleep debt, and examine if cumulative perceived sleep debt predicts next-day affect. Methods Daily sleep and affect were measured over 2 school weeks and 2 vacation weeks (N=205, 54.1% females, M±SDage = 16.9±0.87 years). Each day, participants wore actigraphs and self-reported the amount of sleep needed to function well the next day (i.e., perceived sleep need), sleep duration, and high- and low-arousal positive and negative affect (PA, NA). Cumulative perceived sleep debt was calculated as the weighted average of the difference between perceived sleep need and sleep duration over the past 3 days. Cross-lagged, multilevel models were used to test cumulative sleep debt as a predictor of next-day affect. Lagged affect, day of the week, study day, and sociodemographics were controlled. Results Perceived sleep need was lower early in the school week, before increasing in the second half of the week. Adolescents accumulated perceived sleep debt across school days and reduced it during weekends. On weekends and vacations, adolescents self-reported meeting their sleep need, sleeping the amount, or more than the amount of sleep they perceived as needing. Higher cumulative actigraphy sleep debt predicted higher next-day high arousal NA; higher cumulative diary sleep debt predicted higher NA (regardless of arousal), and lower low arousal PA the following day. Conclusion Adolescents experienced sustained, cumulative perceived sleep debt across school days. Weekends and vacations appeared to be opportunities for reducing sleep debt. Trajectories of sleep debt during vacation suggested recovery from school-related sleep restriction. Cumulative sleep debt was related to affect on a daily basis, highlighting the value of this measure for future research and interventions.

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Perceived sleep need, cumulative sleep deficit, and associations with daily affect in adolescents: A 28-day study over school and vacation periods

10.31234/osf.io/pagzc ◽

2021 ◽

Author(s):

Lin Shen ◽

Joshua F. Wiley ◽

Bei Bei

Keyword(s):

Sleep Duration ◽

Multilevel Models ◽

Weighted Average ◽

Sleep Restriction ◽

Positive And Negative Affect ◽

The Past ◽

Daily Affect ◽

Day Of The Week ◽

The Difference ◽

Sleep Deficit

Study Objectives: To describe trajectories of daily perceived sleep need (PSNeed) and sleep deficit across 28 consecutive days, and examine if cumulative sleep deficit predicts next-day affect.Methods: Daily sleep and affect were measured over 2 weeks of school and 2 weeks of vacation in 205 adolescents (54.1% females, Mage = 16.9 years). Each day, participants wore actigraphs and self-reported the amount of sleep needed to function well the next day (i.e., perceived sleep need), sleep duration, and high- and low-arousal positive and negative affect (PA, NA). Cumulative actigraphy and diary sleep deficit were calculated as the weighted average of the difference between PSNeed and sleep duration over the past 3 days. Cross-lagged, multilevel models were used to test cumulative sleep deficit as a predictor of next-day affect. Lagged affect, day of the week, study day, and sociodemographics were controlled.Results: PSNeed was lower early in the school week, before increasing in the second half of the week. Adolescents accumulated sleep deficit across school days and reduced it during weekends. During weekends and vacations, adolescents’ self-reported, but not actigraphy sleep duration, met PSNeed. Higher cumulative actigraphy sleep deficit predicted higher next-day high arousal NA; higher cumulative diary sleep deficit predicted higher NA (regardless of arousal), and lower low arousal PA the following day.Conclusions: Adolescents experienced sustained cumulative sleep deficit across school days. Non-school nights appeared to be opportunities for reducing sleep deficit. Trajectories of sleep deficit during vacation suggested recovery from school-related sleep restriction.

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Subjective Sleep Debt Is Correlated with Body Fat Percentage Independent of Sleep Duration

Diabetes ◽

10.2337/db18-1675-p ◽

2018 ◽

Vol 67 (Supplement 1) ◽

pp. 1675-P

Author(s):

XIAO TAN ◽

CHRISTIAN BENEDICT

Keyword(s):

Sleep Duration ◽

Body Fat ◽

Body Fat Percentage ◽

Fat Percentage ◽

Subjective Sleep ◽

Sleep Debt

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279 WACSAW: A Statistical and Individually Adaptive Method to Determine Sleep and Wakefulness from Activity

SLEEP ◽

10.1093/sleep/zsab072.278 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A111-A112

Author(s):

Austin Vandegriffe ◽

V A Samaranayake ◽

Matthew Thimgan

Keyword(s):

Time Series ◽

Optimal Transport ◽

Activity Patterns ◽

Adaptive Method ◽

Wasserstein Distance ◽

Activity Data ◽

Commercial Grade ◽

Movement Data ◽

The Difference ◽

Levene Test

Abstract Introduction Technological innovations have broadened the type and amount of activity data that can be captured in the home and under normal living conditions. Yet, converting naturalistic activity patterns into sleep and wakefulness states has remained a challenge. Despite the successes of current algorithms, they do not fill all actigraphy needs. We have developed a novel statistical approach to determine sleep and wakefulness times, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), and validated the algorithm in a small cohort of healthy participants. Methods WACSAW functional routines: 1) Conversion of the triaxial movement data into a univariate time series; 2) Construction of a Wasserstein weighted sum (WSS) time series by measuring the Wasserstein distance between equidistant distributions of movement data before and after the time-point of interest; 3) Segmenting the time series by identifying changepoints based on the behavior of the WSS series; 4) Merging segments deemed similar by the Levene test; 5) Comparing segments by optimal transport methodology to determine the difference from a flat, invariant distribution at zero. The resulting histogram can be used to determine sleep and wakefulness parameters around a threshold determined for each individual based on histogram properties. To validate the algorithm, participants wore the GENEActiv and a commercial grade actigraphy watch for 48 hours. The accuracy of WACSAW was compared to a detailed activity log and benchmarked against the results of the output from commercial wrist actigraph. Results WACSAW performed with an average accuracy, sensitivity, and specificity of >95% compared to detailed activity logs in 10 healthy-sleeping individuals of mixed sexes and ages. We then compared WACSAW’s performance against a common wrist-worn, commercial sleep monitor. WACSAW outperformed the commercial grade system in each participant compared to activity logs and the variability between subjects was cut substantially. Conclusion The performance of WACSAW demonstrates good results in a small test cohort. In addition, WACSAW is 1) open-source, 2) individually adaptive, 3) indicates individual reliability, 4) based on the activity data stream, and 5) requires little human intervention. WACSAW is worthy of validating against polysomnography and in patients with sleep disorders to determine its overall effectiveness. Support (if any):

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Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽

10.3390/molecules26133983 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3983

Author(s):

Ozren Gamulin ◽

Marko Škrabić ◽

Kristina Serec ◽

Matej Par ◽

Marija Baković ◽

...

Keyword(s):

Raman Spectra ◽

Principal Component ◽

Support Vector ◽

Gender Recognition ◽

Proof Of Concept ◽

Male And Female ◽

Tooth Type ◽

Tooth Apex ◽

The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

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Sleep and Performance during a Preseason in Elite Rugby Union Athletes

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094612 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4612

Author(s):

Angus R. Teece ◽

Christos K. Argus ◽

Nicholas Gill ◽

Martyn Beaven ◽

Ian C. Dunican ◽

...

Keyword(s):

Body Composition ◽

Sleep Duration ◽

Aerobic Capacity ◽

Rugby Union ◽

Significant Group ◽

Sleep Quantity ◽

Time Interaction ◽

Wrist Actigraphy ◽

And Performance ◽

H 30

Background: Preseason training optimises adaptations in the physical qualities required in rugby union athletes. Sleep can be compromised during periods of intensified training. Therefore, we investigated the relationship between sleep quantity and changes in physical performance over a preseason phase in professional rugby union athletes. Methods: Twenty-nine professional rugby union athletes (Mean ± SD, age: 23 ± 3 years) had their sleep duration monitored for 3 weeks using wrist actigraphy. Strength and speed were assessed at baseline and at week 3. Aerobic capacity and body composition were assessed at baseline, at week 3 and at week 5. Participants were stratified into 2 groups for analysis: <7 h 30 min sleep per night (LOW, n = 15) and >7 h 30 min sleep per night (HIGH, n = 14). Results: A significant group x time interaction was determined for aerobic capacity (p = 0.02, d = 1.25) at week 3 and for skinfolds at week 3 (p < 0.01, d = 0.58) and at week 5 (p = 0.02, d = 0.92), in favour of the HIGH sleep group. No differences were evident between groups for strength or speed measures (p ≥ 0.05). Conclusion: This study highlights that longer sleep duration during the preseason may assist in enhancing physical qualities including aerobic capacity and body composition in elite rugby union athletes.

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Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽

10.3390/s21010194 ◽

2020 ◽

Vol 21 (1) ◽

pp. 194

Author(s):

Sarah Gonzalez ◽

Paul Stegall ◽

Harvey Edwards ◽

Leia Stirling ◽

Ho Chit Siu

Keyword(s):

Activity Recognition ◽

Principal Components ◽

Classification Accuracy ◽

Wearable Sensors ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Measurement Units ◽

The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

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Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

BMC Nephrology ◽

10.1186/s12882-021-02421-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tianqi Tu ◽

Xueling Wei ◽

Yue Yang ◽

Nianrong Zhang ◽

Wei Li ◽

...

Keyword(s):

Deep Learning ◽

Renal Biopsy ◽

Membranous Nephropathy ◽

Learning Algorithm ◽

Hyperspectral Imagery ◽

Chinese Patients ◽

Support Vector ◽

Deep Learning Algorithm ◽

The Difference ◽

Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

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Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Sensors ◽

10.3390/s19020419 ◽

2019 ◽

Vol 19 (2) ◽

pp. 419 ◽

Cited By ~ 11

Author(s):

Dongdong Du ◽

Jun Wang ◽

Bo Wang ◽

Luyi Zhu ◽

Xuezhen Hong

Keyword(s):

Prediction Models ◽

Extraction Methods ◽

Oxide Semiconductor ◽

Soluble Solids ◽

Support Vector ◽

Least Squares Regression ◽

Accuracy Rate ◽

Linear Discriminant ◽

The Difference ◽

Ripe Stage

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

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Sleep Indices and Cardiac Autonomic Activity Responses during an International Tournament in a Youth National Soccer Team

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18042076 ◽

2021 ◽

Vol 18 (4) ◽

pp. 2076

Author(s):

Pedro Figueiredo ◽

Júlio Costa ◽

Michele Lastella ◽

João Morais ◽

João Brito

Keyword(s):

Heart Rate ◽

Frequency Domain ◽

Sleep Duration ◽

Time Domain ◽

Soccer Players ◽

Autonomic Activity ◽

Male Youth ◽

Youth Soccer ◽

Wrist Actigraphy ◽

Cardiac Autonomic Activity

This study aimed to describe habitual sleep and nocturnal cardiac autonomic activity (CAA), and their relationship with training/match load in male youth soccer players during an international tournament. Eighteen elite male youth soccer players (aged 14.8 ± 0.3 years; mean ± SD) participated in the study. Sleep indices were measured using wrist actigraphy, and heart rate (HR) monitors were used to measure CAA during night-sleep throughout 5 consecutive days. Training and match loads were characterized using the session-rating of perceived exertion (s-RPE). During the five nights 8 to 17 players slept less than <8 h and only one to two players had a sleep efficiency <75%. Players’ sleep duration coefficient of variation (CV) ranged between 4 and 17%. Nocturnal heart rate variability (HRV) indices for the time-domain analyses ranged from 3.8 (95% confidence interval, 3.6; 4.0) to 4.1 ln[ms] (3.9; 4.3) and for the frequency-domain analyses ranged from 5.9 (5.6; 6.5) to 6.6 (6.3; 7.4). Time-domain HRV CV ranged from 3 to 10% and frequency-domain HRV ranged from 2 to 12%. A moderate within-subjects correlation was found between s-RPE and sleep duration [r = −0.41 (−0.62; −0.14); p = 0.003]. The present findings suggest that youth soccer players slept less than the recommended during the international tournament, and sleep duration was negatively associated with training/match load.

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