Association of physiological factors with grip and leg extension strength: tohoku medical megabank community-based cohort study

BACKGROUND

Upper and lower extremity muscle strength can be used to predict health outcomes. However, the difference between the relation of upper extremity muscle and of lower extremity muscle with physiological factors is unclear. This study aimed to evaluate the association between physiological data and muscle strength, measured using grip and leg extension strength, among Japanese adults.

METHODS

We conducted a cross-sectional study of 2,861 men and 6,717 women aged ≥ 20 years living in Miyagi Prefecture, Japan. Grip strength was measured using a dynamometer. Leg extension strength was measured using a hydraulic isokinetic leg press machine. Anthropometry and physiological data, including blood pressure, calcaneal ultrasound bone status, pulmonary function, carotid echography, and blood information, were assessed. We used a general linear model adjusted for age, body composition, and smoking status to evaluate the association between muscle strength and physiological factors.

RESULTS

Grip and leg extension strength were positively associated with bone area ratio, vital capacity, forced vital capacity, forced expiratory volume in one second, and estimated glomerular filtration rate, and negatively associated with waist circumference and percentage body fat mass in both the sexes. Diastolic blood pressure was positively associated with grip strength in both the sexes and leg extension strength in men, but not women. High-density lipoprotein cholesterol and red blood cell counts were positively associated with grip and leg extension strength in women, but not men. In both the sexes, pulse rate, total cholesterol, and uric acid were consistently associated with only leg extension strength, but not grip strength. In women, glycated hemoglobin demonstrated negative and positive associations with grip and leg extension strength, respectively.

CONCLUSIONS

Grip and leg extension strength demonstrated similar associations with anthropometry, pulmonary function, and estimated glomerular filtration rate, but the associations with the other factors were not always consistent.

Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm Piloting

To improve the safety and the performance of operators involved in risky and demanding missions (like drone operators), human-machine cooperation should be dynamically adapted, in terms of dialogue or function allocation. To support this reconfigurable cooperation, a crucial point is to assess online the operator’s ability to keep performing the mission. The article explores the concept of Operator Functional State (OFS), then it proposes to operationalize this concept (combining context and physiological indicators) on the specific activity of drone swarm monitoring, carried out by 22 participants on simulator SUSIE. With the aid of supervised learning methods (Support Vector Machine, k-Nearest Neighbors, and Random Forest), physiological and contextual are classified into three classes, corresponding to different levels of OFS. This classification would help for adapting the countermeasures to the situation faced by operators.

Concluding Notes and Future Directions

The articles in this issue include experimental research and clinical studies on the bidirectional process of connecting experience and words, which we term the Referential Process (RP). These concluding notes focus on new questions and new directions for research. Studies now under way include characterization and measurement of the Arousal function of the referential process, which involves how people talk when the connection to specific ideas is not yet fully developed, and new research on paralinguistic features of interpersonal communication. Further work in these areas will involve automatic transcription technology to incorporate pitch, rate of speech and loudness, as well as development of a Time-DAAP program to enable such assessment. Research is also needed to investigate the relationship between language and underlying physiological and neurological mechanisms. These relationships can be examined using physiological measures such as galvanic skin response (GSR) and changes in heart rate and respiration. While fMRI scans are not compatible with tasks requiring speech production, fMRI compatible tablet systems are available for writing tasks. Participants may also be scanned while reading literary passages that show differences in RP functions. A major goal of our research program is the application of RP measures in large scale treatment efficacy and effectiveness studies evaluating particular treatment forms. The computerized referential process procedures have the potential to study whole trajectories of large numbers of treatments; and also to identify important turning points within treatments and within sessions. The interpretation of these measures in the context of a systematic theory of treatment process has the value of enabling results that are not only statistically powerful but clinically significant as well. Other potential areas of study include application of the language measures in large scale studies of political, religious and literary discourse.

BWDAT: A research tool for analyzing the consumption of VOD content at home

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BWDAT is a reliable tool that facilitates the study of viewing experience on VOD platforms.

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Collects users’ physiological data and users’ interactions with Netflix interface.

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Non-intrusive and easy to use, successfully used in long-term projects with more than 200 users.

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Includes a graphical display of the viewing sessions to help researchers visualize the data.

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Includes an automatic report generator and data exporter for multiple platforms.

Introduction

New approaches to the study of the binge-watching phenomenon require new technology, leading to the development of a non-intrusive and low-cost analytical research software that facilitates a holistic understanding of binge-watching in an uncontrolled environment remotely (e.g., the home). BWDAT was developed to allow the collection of three types of data: users’ physiological data gathered from a smartwatch, users’ interactions from video-on-demand interfaces, and self-reported data. This tool offers the possibility to generate automatic data analysis reports, facilitating researchers’ data analysis tasks.

Methods

Two trial studies and a long-term study were used to evaluate the design and the technical implementation of the BWDAT tool. The metrics used were the BWDAT smartwatch’s App data coverage of the viewing sessions, and the data’s reliability of the viewer’s interactions with the Netflix interface, collected by the BWDAT Chrome Extension.

Results

High percentages of data coverage and content coverage were verified in the sessions collecting the smartwatch’s data. The reporting system developed proved to be useful in the collection and synchronization of physiological and users’ interaction data with Netflix interface, both generated in uncontrolled environments. Furthermore, the BWDAT tool facilitated the analysis of a large amount of nuanced data.

Conclusion

The results obtained confirm the reliability, accuracy, and usability of BWDAT. This tool has the potential to help researchers shed new light on the field of media and audience studies, and in particular on binge-watching.

Towards an anxiety and stress recognition system for academic environments based on physiological features

BACKGROUND AND OBJECTIVE

Traditional methods to determine stress and anxiety in academic environments consist of the application of questionnaires, but the main disadvantage is that the results depend on the students' self-perception. Being able to detect anxiety-related stress levels in a simple and objective way contributes greatly to dealing with low performance and school drop-out by students.

METHODS

The main contribution of this study is to identify the physiological features that could be used as predictors of stressful activities and states of anxiety in academic environments using an Arduino board and low-cost sensors. A test with 21 students was conducted, and a stress-inducing protocol was proposed and 21 physiological features of five signals were analyzed. In addition, the State-Trait Anxiety Inventory (STAI) was used to assess the level of anxiety for each student. Four classifiers were compared to find the physiological feature subset that provides the best accuracy to identify states of stress and anxiety.

RESULTS

The stress due to activities performed by students can be identified with an accuracy greater than 90% (Kappa = 0.84) using the k-Nearest Neighbors classifier, using data from heart rate, skin temperature and oximetry signals and four physiological features. Meanwhile, the identification of anxiety was achieved with an accuracy greater than 95% (Kappa = 0.90) using the SVM classifier with data from the galvanic skin response (GSR) signal and three physiological features.

CONCLUSIONS

The results provide a clue that anxiety detection in academic environments could be done using the analysis of physiological signals instead of STAI test scores. Besides, the results suggest that physiological features could be used to develop stress recognition systems to help teachers to identify the stressful tasks in an academic environment or to develop anxiety recognition systems to help students to control their level of anxiety when they are performing either academic tasks or exams.

In a heart beat: Using driver's physiological changes to determine the quality of a takeover in highly automated vehicles

Developing conditionally automated driving systems is on the rise. Vehicles with full longitudinal and latitudinal control will allow drivers to engage in secondary tasks without monitoring the roadway, but users may be required to resume vehicle control to handle critical hazards. The loss of driver's situational awareness increases the potential for accidents. Thus, the automated systems need to estimate the driver's ability to resume control of the driving task. The aim of this study was to assess the physiological behaviour (heart rate and pupil diameter) of drivers. The assessment was performed during two naturalistic secondary tasks. The tasks were the email and the twenty questions task in addition to a control group that did not perform any tasks. The study aimed at finding possible correlations between the driver's physiological data and their responses to a takeover request. A driving simulator study was used to collect data from a total of 33 participants in a repeated measures design to examine the physiological changes during driving and to measure their takeover quality and response time. Secondary tasks induced changes on physiological measures and a small influence on response time. However, there was a strong observed correlation between the physiological measures and response time. Takeover quality in this study was assessed using two new performance measures called PerSpeed and PerAngle. They are identified as the mean percentage change of vehicle's speed and heading angle starting from a take-over request time. Using linear mixed models, there was a strong interaction between task, heart rate and pupil diameter and PerSpeed, PerAngle and response time. This, in turn, provided a measurable understanding of a driver's future responses to the automated system based on the driver's physiological changes to allow better decision making. The present findings of this study emphasised the possibility of building a driver mental state model and prediction system to determine the quality of the driver's responses in a highly automated vehicle. Such results will reduce accidents and enhance the driver's experience in highly automated vehicles.

A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier

PURPOSE

Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.

METHODS

A data-set consisting of high-frequency physiological data from 1161 critically ill patients was analyzed. 377 patients had developed sepsis, and had data at least 3 h prior to the onset of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients in real-time using a total of 132 features extracted from a moving time-window. The model was trained on 80% of the patients and was tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 h prior to onset.

RESULTS

The model that used continuous physiological data alone resulted in sensitivity and F1 score of up to 80% and 67% one hour before sepsis onset. On average, these models were able to predict sepsis 294.19 ± 6.50 min (5 h) before the onset.

CONCLUSIONS

The use of machine learning algorithms on continuous streams of physiological data can allow for early identification of at-risk patients in real-time with high accuracy.

An optimal two-bout strategy with phase change material cooling vests to improve comfort in hot environment

Cooling vests incorporating phase change material (PCM) packets are used to improve comfort of workers in hot environments. This work aims to investigate by modeling and experimentation the effect of dividing the working duration into two bouts, where different PCM melting temperatures are used in each bout. An integrated bio-heat and fabric-PCM model predictions of physiological and subjective votes are validated via active human subject testing at hot conditions. A parametric study is performed to select, at two conditions (40°C and 45°C), the optimal PCM melting temperatures of the two bouts that would result with similar thermal comfort and sensation to the optimal single-bout case. The optimal case achieves most reductions in energy use for PCM regeneration, PCM carried weight and material cost. The results of the parametric study showed that heat storage is reduced in the second bout due to wearing the second vest with lower PCM melting temperature, thus thermal comfort and sensation are significantly improved. The optimal case at the 40°C environment uses a vest with 21°C PCMs in the first bout and a vest with 21°C PCMs in the second bout (V21→V21). At 45°C, the optimal case is V18→V10 with significant PCM weight reductions from the reference single bout case by a minimum of 47%. Thus, the issue of extra carried weight that affect metabolism and ease of movement when applying continuous cooling during work have been mitigated by using the two-bout strategy.

Simultaneous oxidation of ammonium and cresol isomers in a sequencing batch reactor: physiological and kinetic study

The aim of this study was to evaluate the physiological and kinetic capacities of a nitrifying consortium to simultaneously oxidize ammonium (138 mg N/L day), m-cresol, o-cresol, and p-cresol (180 mg C/L day in mixture) in a sequencing batch reactor (SBR). A 1-L SBR was firstly operated without cresol addition (phase I) for stabilizing the nitrification respiratory process with ammonium consumption efficiencies close to 100 % and obtaining nitrate as the main end product. When cresols were added (phase II m-cresol (10, 20, and 30 mg C/L); phase III m-cresol (30 mg C/L) and o-cresol (10, 20, and 30 mg C/L); phase IV a mixture of three isomers (30 mg C/L each one)), inhibitory effects were evidenced by decreased values of the specific rates of nitrification compared with values from phase I. However, the inhibition diminished throughout the operation cycles, and the overall nitrifying physiological activity of the sludge was not altered in terms of efficiency and nitrate yield. The different cresols were totally consumed, being o-cresol the most recalcitrant. The use of SBR allowed a metabolic adaptation of the consortium to oxidize the cresols as the specific rates of consumption increased throughout the cycles, showing that this type of reactor can be a good alternative for treating industrial effluents in a unique reactor.