Non-linear parameters of specific resistance loops to characterise obstructive airways diseases.

BACKGROUND: Specific resistance loops appear in different shapes influenced by different resistive properties of the airways, yet their descriptive ability is compressed to a single parameter - its slope. We aimed to develop new parameters reflecting the various shapes of the loop and to explore their potential in the characterisation of obstructive airways diseases. METHODS: Our study included 134 subjects: Healthy controls (N = 22), Asthma with non-obstructive lung function (N = 22) and COPD of all disease stages (N = 90). Different shapes were described by geometrical and second-order transfer function parameters. RESULTS: Our parameters demonstrated no difference between asthma and healthy controls groups, but were significantly different (p < 0.0001) from the patients with COPD. Grouping mild COPD subjects by an open or not-open shape of the resistance loop revealed significant differences of loop parameters and classical lung function parameters. Multiple logistic regression indicated RV/TLC as the only predictor of loop opening with OR = 1.157, 95% CI (1.064-1.267), p-value = 0.0006 and R2 = 0.35. Inducing airway narrowing in asthma gave equal shape measures as in COPD non-openers, but with a decreased slope (p < 0.0001). CONCLUSION: This study introduces new parameters calculated from the resistance loops which may correlate with different phenotypes of obstructive airways diseases.

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds.

Bovine respiratory disease (BRD) complex in calves impairs health and welfare and causes severe economic losses for the Stockperson. Early recognition of BRD should lead to earlier veterinary (antibiotic/anti-inflammatory) treatment interventions thereby reducing the severity of the disease and associated costs. Coughing is one of the clinical manifestations of BRD. It is believed that by automatically and continuously monitoring the sounds within calf houses, and analysing the coughing frequency, early recognition of BRD in calves is possible. Therefore, the objective of the present study was to develop an automated calf cough monitor and examine its potential as an early warning system for BRD in artificially reared dairy calves. The coughing sounds of 62 calves were continuously recorded by a microphone over a three-month period. A sound analysis algorithm was developed to distinguish calf coughs from other sounds (e.g. mechanical sounds). During the sound recording period the health of the calves was assessed and scored periodically per week by a trained human observer. Calves presenting with BRD received antibiotic and/or anti-inflammatory treatment and the dates of treatment were recorded. This treatment date reference served as a comparison for the investigation of whether an increase in coughing frequency could be related to calves developing BRD. The calf cough detection algorithm achieved 50.3% sensitivity, 99.2% specificity and 87.5% precision. Four out of five periods, where coughing frequency was observed to be increased, coincided with the development of BRD in more than one calf. This period of increased coughing frequency was always observed before the calves were treated. Therefore, the calf cough monitor has the potential to identify early onset of BRD in calves.

Using dynamics of forced expiration to identify COPD where conventional criteria for the FEV1 /FVC ratio do not match.

BACKGROUND AND OBJECTIVE: The definition of chronic obstructive pulmonary disease (COPD) based on a fixed forced expiratory volume in 1 s (FEV1 )/forced vital capacity (FVC) ratio or on the lower limits of FEV1 /FVC of a healthy reference population is the subject of continuous debate. We explored whether dynamics of forced expiratory flow decline on spirometry can identify subjects with and without COPD when the two key diagnostic criteria are discordant. METHODS: Four hundred twenty-three individuals with a history of ≥15 pack-years smoking had pulmonary function measurements conducted. A second-order input-output model was used to describe the dynamics of the forced expiration. The capability of the model parameters to predict presence of disease was explored with a support vector machine classifier. In the discordant individuals, newly classified subjects were validated by other pulmonary function tests. RESULTS: In the non-discordant subjects (n = 370), the second-order model was able to confirm a diagnosis of COPD in 95% of subjects (n = 351). In the discordant individuals (n = 53), the classification by dynamic flow analysis found 28 patients to be healthy whereas 25 patients were still classified as COPD. Hyperinflation, increased airways resistance and reduced dynamic volumes were observed in the newly identified COPD group of discordant subjects. When using non-spirometry-based pulmonary function criteria as a standard for correct diagnoses in the individual discordant subjects, the model allocated 68% (n = 36) of the discordant to a correct diagnosis. CONCLUSIONS: Expiratory flow dynamics can detect airflow limitation and indicate the presence of COPD. In discordant subjects, our methodology allows a better identification of subjects with or without characteristics of COPD.

Effects of pre-sleep media use on sleep/wake patterns and daytime functioning among adolescents: the moderating role of parental control.

The aim of this cross-sectional study was to investigate the influence of media use in the hour before bedtime on sleep/wake patterns and daytime functioning among adolescents and to examine the moderating role of parental control. A total of 1,926 Belgian students, 55% girls and 45% boys, with a mean age of 16.9 ± 1.5 years, completed a modified version of the School Sleep Habits Survey. Correlational analyses showed that media use, except television viewing, was associated with later bedtimes and longer sleep latencies. Cell phone and computer usage was negatively associated with daytime functioning. On schooldays, parental control had a moderating effect on the relationship between bedtime and computer use (ß = .05; p < .05) and between bedtime and mp3 player use (ß = .08; p < .01). During the weekend, parental control played a moderating role between bedtime and television viewing (ß = .06; p = .01). As media use can influence the sleep of adolescents considerably, parental control is necessary to regulate the exposure of adolescents to media and to moderate the detrimental effect of media use on sleep.

Modelling and quantification of the thermoregulatory responses of the developing avian embryo: electrical analogies of a physiological system.

Homeothermic animals, including birds, try to keep their body temperature at a constant level within certain boundaries by using thermoregulatory mechanisms. However, during incubation, the thermoregulatory system of the chicken embryo evolves through different stages from a poikilothermic to a homeothermic system. Hence, the thermal response of the fertile egg to changes in ambient temperature is different from one day to another during the embryonic development. The incubated egg can be considered as a physical (thermal) system, which transfers energy (heat) down a potential gradient (temperature difference). The heat flow between the micro-environment and the eggshell under a thermal driving force (temperature difference) has been studied in the past by using the analogy to the flow of electric charge under an electromotive-force. In this work, the thermal-response of incubated eggs to a step-increase in ambient-air temperature is studied and modelled. It is shown that the incubated egg is reacting as a first-order system between embryonic days ED01 and ED13, while, starting from ED14, the egg is reacting as a second-order system. This extends the existing RC (resistor-capacitor) circuit analogue to an RLC (resistor-inductor-capacitor) circuit analogue at the later stage of incubation. The concept of considering the fertile egg and its surrounding environment as an energy-handling device is introduced in this paper. It is suggested that the thermoregulation of the embryo has a thermal induction-like effect starting from ED14 and increasing gradually till hatching.

Computer quantification of airway collapse on forced expiration to predict the presence of emphysema.

BACKGROUND: Spirometric parameters are the mainstay for diagnosis of COPD, but cannot distinguish airway obstruction from emphysema. We aimed to develop a computer model that quantifies airway collapse on forced expiratory flow-volume loops. We then explored and validated the relationship of airway collapse with computed tomography (CT) diagnosed emphysema in two large independent cohorts. METHODS: A computer model was developed in 513 Caucasian individuals with ≥15 pack-years who performed spirometry, diffusion capacity and CT scans to quantify emphysema presence. The model computed the two best fitting regression lines on the expiratory phase of the flow-volume loop and calculated the angle between them. The collapse was expressed as an Angle of collapse (AC) which was then correlated with the presence of emphysema. Findings were validated in an independent group of 340 individuals. RESULTS: AC in emphysema subjects (N = 251) was significantly lower (131° ± 14°) compared to AC in subjects without emphysema (N = 223), (152° ± 10°) (p < 0.0001). Multivariate regression analysis revealed AC as best indicator of visually scored emphysema (R2 = 0.505, p < 0.0001) with little significant contribution of KCO, %predicted and FEV1, %predicted to the total model (total R2 = 0.626, p < 0.0001). Similar associations were obtained when using CT-automated density scores for emphysema assessment. Receiver operating characteristic (ROC) curves pointed to 131° as the best cut-off for emphysema (95.5% positive predictive value, 97% specificity and 51% sensitivity). Validation in a second group confirmed the significant difference in mean AC between emphysema and non-emphysema subjects. When applying the 131° cut-off, a positive predictive value of 95.6%, a specificity of 96% and a sensitivity of 59% were demonstrated. CONCLUSIONS: Airway collapse on forced expiration quantified by a computer model correlates with emphysema. An AC below 131° can be considered as a specific cut-off for predicting the presence of emphysema in heavy smokers.

A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity.

Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant's daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology-clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.

Smartphone-Based Passive Sensing in Monitoring Patients With Cancer: A Systematic Review.

PURPOSE: Patients with cancer are prone to frequent unplanned hospital visits because of disease or treatment complications. Smartphone-based passive sensing (SBPS) comprises data collection using smartphone sensors or device usage patterns, which may be an affordable and burdenless technique for remote monitoring of patients with cancer and timely detection of safety events. The aim of this article was to systematically review the published literature to identify the current state of SBPS in oncology care and research. METHODS: A literature search was done with cutoff date July 29, 2022, using six different databases. Articles were included if they reported original studies using SBPS in patients with cancer or cancer survivors. Data extracted from studies included type of sensors used, cancer type, study objectives, and main findings. RESULTS: Twelve studies were included, the oldest report being from 2017. The most frequent of the nine analyzed sensors and smartphone analytics was the accelerometer (eight studies) and geolocation (eight studies), followed by call logs (two studies). Breast cancer was the most studied cancer type (eight studies with 111 patients), followed by GI cancers (six studies with 133 patients). All studies aiming for feasibility concluded that SBPS in oncology was feasible (seven studies). SBPS was used as a monitoring tool, with passively sensed data being correlated with adverse events, symptom burden, cancer-related fatigue, decision conflict, recovery trends after surgery, or psychosocial impact. SBPS was also used in one study as a predictive tool for health deterioration. CONCLUSION: SBPS shows early promise in oncology, although it cannot yet replace traditional tools to monitor quality of life and clinical outcomes. For this, validation of SBPS will be required. Therefore, further research is warranted with this developing technique.

"Yes, we can!": Perceptions of collective efficacy sources in volleyball.

Collective efficacy can be defined as a group's shared confidence that they will successfully achieve their goal. We examined which behaviours and events are perceived as sources of collective efficacy beliefs in a volleyball context. In study 1, volleyball coaches from the highest volleyball leagues (n = 33) in Belgium indicated the most important sources of collective efficacy. This list was then adapted based on the literature and on feedback given by an expert focus group, resulting in a 40-item questionnaire. In Study 2, coaches and players from all levels of volleyball in Belgium (n = 2365) rated each of these sources on their predictive value for collective efficacy. A principal component analysis revealed that the 40 sources could be divided into eight internally consistent factors. Positive supportive communication (e.g., enthusiasm after making a point) was identified as the factor most predictive for positive collective efficacy beliefs. The factor referring to the negative emotional reactions of players (e.g., discouraging body language) was the most predictive for negative collective efficacy beliefs. These findings offer a starting point for the design of continuous measurements of collective efficacy through observation.

Development and technical validation of a smartphone-based pediatric cough detection algorithm.

INTRODUCTION: Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children. METHODS: The training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions. RESULTS: The final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source. CONCLUSION: This novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.

Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm.

Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying. Methods: For the development of the algorithm a training dataset containing 897 5-s clips of crying infants and 1,263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1,591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-min audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm. Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75 and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone. Conclusion: The algorithm was accurate in detecting cry duration and was robust to various changes in ambient settings.

Heart rate-based nighttime awakening detection.

Sleep fragmentation is a cause of impaired daytime performance. We have developed an algorithm for detection of nighttime awakenings based on heart rate. As much as 15 healthy normal sleepers, 23 +/- 3 years, participated in this study. The dataset contains 33 nights of polysomnographic (PSG) and electrocardiographic (ECG) measurements. After a habituation night, the subjects underwent a reference night without interventions, followed by some nights with interventions. These included noise, light, physical and cognitive interventions. Nighttime awakenings were subdivided in to awakenings (>15 s) and short awakenings (<15 s). The overall number of awakenings was 18.5 (+/-10.5) and short awakenings 13.2 (+/-10.5). The number of nighttime awakenings did not differ significantly between the reference and intervention nights; a repeated measures ANOVA resulted in a p value of 0.66 for awakenings and 0.57 for short awakenings. As much as 5 reference nights were used as training set, 28 as validation set. The algorithm detects the awakening periods with a sensitivity of 80.5% (confidence interval 77.9-82.9%). Heart rate is an adequate measure that allows for detection of nighttime awakenings and hence sleep quality.

Acquiring a dataset of labeled video images showing discomfort in demented elderly.

One of the effects of late-stage dementia is the loss of the ability to communicate verbally. Patients become unable to call for help if they feel uncomfortable. The first objective of this article was to record facial expressions of bedridden demented elderly. For this purpose, we developed a video acquisition system (ViAS) that records synchronized video coming from two cameras. Each camera delivers uncompressed color images of 1,024 x 768 pixels, up to 30 frames per second. It is the first time that such a system has been placed in a patient's room. The second objective was to simultaneously label these video recordings with respect to discomfort expressions of the patients. Therefore, we developed a Digital Discomfort Labeling Tool (DDLT). This tool provides an easy-to-use software representation on a tablet PC of validated "paper" discomfort scales. With ViAS and DDLT, 80 different datasets were obtained of about 15 minutes of recordings. Approximately 80% of the recorded datasets delivered the labeled video recordings. The remainder were not usable due to under- or overexposed images and due to the patients being out of view as the system was not properly replaced after care. In one of 6 observed patients, nurses recognized a higher discomfort level that would not have been observed without the DDLT.

Time-series analysis for online recognition and localization of sick pig (Sus scrofa) cough sounds.

This paper considers the online localization of sick animals in pig houses. It presents an automated online recognition and localization procedure for sick pig cough sounds. The instantaneous energy of the signal is initially used to detect and extract individual sounds from a continuous recording and their duration is used as a preclassifier. Autoregression (AR) analysis is then employed to calculate an estimate of the sound signal, and the parameters of the estimated signal are subsequently evaluated to identify the sick cough sounds. It is shown that the distribution of just three AR parameters provides an adequate classifier for sick pig coughs. A localization technique based on the time difference of arrival is evaluated on field data and is shown that it is of acceptable accuracy for this particular application. The algorithm is applied on continuous recordings from a pig house to evaluate its effectiveness. The correct identification ratio ranged from 73% (27% false positive identifications) to 93% (7% false positive identifications) depending on the position of the microphone that was used for the recording. Although the false negative identifications are about 50% it is shown that this accuracy can be enough for the purpose of this tool. Finally, it is suggested that the presented application can be used to online monitor the welfare in a pig house, and provide early diagnosis of a cough hazard and faster treatment of sick animals.

Wireless Tri-Axial Trunk Accelerometry Detects Deviations in Dynamic Center of Mass Motion Due to Running-Induced Fatigue.

Small wireless trunk accelerometers have become a popular approach to unobtrusively quantify human locomotion and provide insights into both gait rehabilitation and sports performance. However, limited evidence exists as to which trunk accelerometry measures are suitable for the purpose of detecting movement compensations while running, and specifically in response to fatigue. The aim of this study was therefore to detect deviations in the dynamic center of mass (CoM) motion due to running-induced fatigue using tri-axial trunk accelerometry. Twenty runners aged 18-25 years completed an indoor treadmill running protocol to volitional exhaustion at speeds equivalent to their 3.2 km time trial performance. The following dependent measures were extracted from tri-axial trunk accelerations of 20 running steps before and after the treadmill fatigue protocol: the tri-axial ratio of acceleration root mean square (RMS) to the resultant vector RMS, step and stride regularity (autocorrelation procedure), and sample entropy. Running-induced fatigue increased mediolateral and anteroposterior ratios of acceleration RMS (p < .05), decreased the anteroposterior step regularity (p < .05), and increased the anteroposterior sample entropy (p < .05) of trunk accelerometry patterns. Our findings indicate that treadmill running-induced fatigue might reveal itself in a greater contribution of variability in horizontal plane trunk accelerations, with anteroposterior trunk accelerations that are less regular from step-to-step and are less predictable. It appears that trunk accelerometry parameters can be used to detect deviations in dynamic CoM motion induced by treadmill running fatigue, yet it is unknown how robust or generalizable these parameters are to outdoor running environments.

Modelling the dynamics of expiratory airflow to describe chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation, but current diagnostic criteria only consider flow till the first second and are therefore strongly debated. We aimed to develop a data-based individualized model for flow decline and to explore the relationship between model parameters and COPD presence. A second-order transfer function model was chosen and the model parameters (namely the two poles and the steady state gain (SSG)) from 474 individuals were correlated with COPD presence. The capability of the model to predict disease presence was explored using 5 machine learning classifiers and tenfold cross-validation. Median (95% CI) poles in subjects without disease were 0.9868 (0.9858-0.9878) and 0.9333 (0.9256-0.9395), compared with 0.9929 (0.9925-0.9933) and 0.9082 (0.9004-0.9140) in subjects with COPD (p < 0.001 for both poles). A significant difference was also found when analysing the SSG, being lower in COPD group 3.8 (3.5-4.2) compared with 8.2 (7.8-8.7) in subjects without (p < 0.0001). A combination of all three parameters in a support vector machines corresponded with highest sensitivity of 85%, specificity of 98.1% and accuracy of 88.2% to COPD diagnosis. The forced expiration of COPD can be modelled by a second-order system which parameters identify most COPD cases. Our approach offers an additional tool in case FEV1/FVC ratio-based diagnosis is doubted.

Automatic sleep stage classification based on easy to register signals as a validation tool for ergonomic steering in smart bedding systems.

Ergonomic sleep studies benefit from long-term monitoring in the home environment to cope with daily variations and habituation effects. Polysomnography allows to asses sleep accurately, but is costly, time-consuming and possibly disturbing for the sleeper. Actigraphy is cheap and user friendly, but for many studies lacks accuracy and detailed information. This proof-of-concept study investigates Least-Squares Support Vector Machines as a tool for automatic sleep stage classification (Wake-N1-Rem to N2-N3 separation), using automatic trainingset-specific filtered features as derived from three easy to register signals, namely heart rate, breathing rate and movement. The algorithms are trained and validated using 20 nights out of a 600 night database from over 100 different healthy persons. Different training and test set strategies were analyzed leading to different results. The more person-specific the training nights to the test nights, the better the classification accuracy as validated against the hypnograms scored by experts from the full polysomnograms. In the limit of complete person-specific training, the accuracy of the algorithm on the test set reached 94%. This means that this algorithm could serve its use in long-term monitoring sleep studies in the home environment, especially when prior person-specific polysomnographic training is performed.

Effects of pre-sleep simulated on-call instructions on subsequent sleep.

Nightly interventions, prevalent to on-call situations, can have negative consequences for those involved. We investigated if intervention-free-on-call-nights would also mean disturbance-free-sleep for people on-call. 16 healthy sleepers spent three nights in the laboratory: after a habituation night, reference and on-call night were counterbalanced. Subjects were instructed to react to a sound, presented at unpredictable moments during the night. Participants were unaware of the fact that the sound would never be presented. These vigilance instructions resulted in more subjective wake after sleep onset (WASO), lower subjective sleep efficiency and significantly lower experienced sleep quality. Objectively, a longer sleep onset, an increased amount of WASO and significantly lower sleep efficiency were observed. During deep sleep, significantly more beta activity was recorded. Apart from real nightly interventions increased vigilance during the night causes sleep to be less efficient and less qualitative as shown by an increase in wake-activity and a distorted sleep perception.

Elevated variance in heart rate during slow-wave sleep after late-night physical activity.

This study investigates the effect of mild physical activity before bedtime on the sleep pattern and heart rate during the night. Nine healthy subjects underwent a habituation night, a reference night, and a physical induction night. The physical induction night did not alter the sleep pattern. Physical activity before bedtime resulted in higher heart rate variance during slow-wave sleep. The low-frequency/high-frequency component (LF/HF) ratio during slow-wave sleep in the physical induction night was significantly higher than during the reference night. Increased mean heart rate and higher LF/HF ratio are related to decreased parasympathetic dominance. Exercise up to 1 h before bedtime thus seems to modify the quality of sleep.