Subjective Metrics Research Articles

Advanced framework for enhancing ultrasound images through an optimized hybrid search algorithm and a novel motion compounding processing chain

Ultrasound imaging is a fast, widespread, and essential diagnostic technique for examining the body’s internal anatomy to find abnormal tissues or diseases. However, speckle noise in ultrasound imaging corrupts fine details and edges and degrades the image’s resolution and contrast, making diagnosing more difficult. In this work, a speckle reduction method using motion compounding is proposed. The objective of this work is ultrasound image enhancement keeping all the diagnostics details and edges. The proposed method uses the pre-locations frames that the sonographer generates before locating the required diagnostic frame. These frames are applied to the proposed optimized Modified Three-Step Search (O-MTSS) algorithm to enhance the final processed frame. The O-MTSS algorithm is a hybrid between the Three Step Search algorithm (TSS) and the New Diamond Search Algorithm (NDS). The method requires a scoring layer for storing additional frames to select optimal frames that will be used for enhancement. The proposed methodology is tested on synthetic and real ultrasound images. The outcomes produce considerable speckle reduction while maintaining the image edges with good computational time for real-time scanning. The result is evaluated by subjective physicians, radiologists, and image evaluation metrics. According to the percentages of the subjective analysis, there is a remarkable improvement in the processed image. The proposed algorithm result is compared with existing algorithms; It is observed that the improvement in terms of signal to noise ratio, peak signal to noise ratio, mean square error, root mean square error and structural similarity index values of the proposed method are 4.6%, 3.32%, 12.02%, 6.2% and 1.57% respectively over Non-Local Low-Rank (NLLR) method. According to qualitative and quantitative analysis, the suggested method outperforms existing speckle reduction techniques regarding edge and fine detail preservation.

On the relevance of (the New) Phenomenology to an ethics of health promotions: toward a prudent balance of understanding and explanation

The field of health promotions faces considerable ethical and programmatic challenge – and we believe opportunity – in addressing the relative normativity of the concept(s) of health and its professional handling. To date, distinctions of objective and subjective indicants of “health” have fostered normative tension(s) within the utilitarian ethics of health promotions, which we opine to be anathema to the ultimate goal(s) of attaining and sustaining healthy individuals and societies. Objective and subjective metrics and values should be reconciled, as reciprocal and complementary on both idiosyncratic and systemic levels. In this light, we propose that a philosophical and ethical approach, based primarily upon Schmitz’s New Phenomenology and its specific understanding of subjectivity could afford epistemological bases for non-normative engagement of health promotion within a structural–functional framework of ethics. We dialectically address its potential benefit, limitations and value for health promotion and health care ethics and present an approach which points towards a more substantial encouragement of diversity through the legitimation of subjectivity.

An effective digital image watermarking scheme incorporating DCT, DFT and SVD transformations.

Image watermarking prevents copyright infringements by attaching visible/invisible watermark images as authentication identities in the owner's documents. The article made analysis on the advantages of different transformations for choosing better combinations to make the watermark embedding process and observed that watermarking techniques incorporating discrete cosine transform (DCT) provide better resistance towards JPEG based potential attacks, discrete Fourier transform (DFT) has strong energy compaction with geometrical invariance properties to resist geometric attacks while singular value decomposition (SVD) provides stability, proportion invariance and rotation invariance properties and it provides strong resistance against noise based attacks. Considering these advantages of different transformations, the article presents a new non-blind watermarking algorithm by utilizing advantages of DFT, DCT and SVD transforms while attaching secret contents in cover images. The algorithm starts by applying DFT followed by onion peel decomposition (OPD) for decomposing Fourier domain carrier image to four frequency sub images. The scheme then applies DCT on the frequency bands and orders them in zigzag fashion to form four individual frequency arrays. In the final step of embedding process, it embeds four copies of watermark singular value contents in DFT domain with the carrier image singular value contents to produce the watermarked image. The experimental results based on subjective and objective metrics on various test images from standard image databases with different test conditions demarcate the stability of new algorithm in producing high quality watermarked images with fewer distortions even when the watermarked images are extremely distorted by potential attacks.

Knowledge-Enriched Attention Network With Group-Wise Semantic for Visual Storytelling.

As a technically challenging topic, visual storytelling aims at generating an imaginary and coherent story with narrative multi-sentences from a group of relevant images. Existing methods often generate direct and rigid descriptions of apparent image-based contents, because they are not capable of exploring implicit information beyond images. Hence, these schemes could not capture consistent dependencies from holistic representation, impairing the generation of reasonable and fluent stories. To address these problems, a novel knowledge-enriched attention network with group-wise semantic model is proposed. Three main novel components are designed and supported by substantial experiments to reveal practical advantages. First, a knowledge-enriched attention network is designed to extract implicit concepts from external knowledge system, and these concepts are followed by a cascade cross-modal attention mechanism to characterize imaginative and concrete representations. Second, a group-wise semantic module with second-order pooling is developed to explore the globally consistent guidance. Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion. Substantial experiments on the visual storytelling datasets with both objective and subjective evaluation metrics demonstrate the superior performance of the proposed scheme as compared with other state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn.

An Escalated Eavesdropping Attack on Mobile Devices via Low-Resolution Vibration Signals

With the global prevalence of mobile devices, concerns about mobile devices regarding privacy breaches and data leakage are rising. Although sensor permissions are required for mobile applications to access outputs of built-in sensors, motion sensors (e.g., accelerometer and gyroscope) can be visited directly without permission requirement. Extant studies have shown that motion sensors may cause breaches of confidential information, such as passwords, digits, and voice-based commands, but whether it is possible to synthesize intelligible speech waveforms from low-resolution motion sensors has been understudied. In this paper, we present an escalated side-channel attack of built-in speakers by synthesizing intelligible speech waveforms from low-resolution vibration signals. Opposite to traditional classification problems, we formulate this task as a generative problem and introduce an end-to-end synthesis framework dubbed as <i>AccMyrinx</i> to eavesdrop on the speaker via the low-resolution vibration signals. In <i>AccMyrinx</i>, we introduce the data alignment solution to provide the pair-wise voice-vibration sequences and present wavelet-based MelGAN (WMelGAN) with multi-scale time-frequency domain discriminators to generate intelligible acoustic waveforms. We conducted intensive experiments and demonstrated the feasibility of synthesizing the intelligible acoustic signals from low-resolution solid-borne vibration signals. Compared with existing synthesis solutions, our proposed solution outperforms the baselines in both subject and object metrics with the smoothed word error rate of 42.67&#x0025; and the Mel-Cepstral distortion of 0.298. In addition, the quality of synthetic speeches could be impacted by several factors, including gender, speech rate, volume, and sampling frequency.

Is Robotic Console Time a Surrogate for Resident Operative Autonomy?

Robotic-assisted surgery is an increasing part of general surgery training, but resident autonomy on the robotic platform can be hard to quantify. Robotic console time (RCT), the percentage of time the resident controls the console, may be an appropriate measure of resident operative autonomy. This study aims to characterize the correlation between objective resident RCT and subjectively scored operative autonomy. Using a validated resident performance evaluation instrument, we collected resident operative autonomy ratings from residents and attendings performing robotic cholecystectomy (RC) and robotic inguinal hernia repair (IH) at a university-based general surgery program between 9/2020-6/2021. We then extracted RCT data from the Intuitive surgical system. Descriptive statistics, t-tests and ANOVA were performed. A total of 31 robotic operations (13 RC, 18 IH) performed by 4 attending surgeons and 8 residents (4 junior, 4 senior) were matched and included. 83.9% of cases were scored by both attending and resident. The average RCT per case was 35.6%(95% CI 13.0%,58.3%) for junior residents (PGY 2-3) and 59.7%(CI 51.1%,68.3%) for senior residents (PGY 4-5). The mean autonomy evaluated by residents was 3.29(CI 2.85,3.73) out of a maximum score of 5, while the mean autonomy evaluated by attendings was 4.12(CI 3.68,4.55). RCT significantly correlated with subjective evaluations of resident autonomy (r=0.61, p=0.0003). RCT also moderately correlated with resident training level (r=0.5306, p<0.0001). Neither attending robotic experience nor operation type significantly correlated with RCT or autonomy evaluation scores. Our study suggests that resident console time is a valid surrogate for resident operative autonomy in robotic cholecystectomy and inguinal hernia repair. RCT may be a valuable measure in objective assessment of residents' operative autonomy and training efficiency. Future investigation into how RCT correlates with subjective and objective autonomy metrics such as verbal guidance or distinguishing critical operative steps is needed to validate the study findings further.

0673 Sleep in PTSD: A Subjective Concern

Abstract Introduction Sleep complaints are a hallmark of posttraumatic stress disorder (PTSD). Prior studies examining sleep abnormalities in PTSD are inconsistent, with some reporting objective sleep changes including associated obstructive sleep apnea (OSA), possibly due to moderating variables including advancing age, traumatic brain injury (TBI), and medications. We sought to compare sleep disorders and objective and subjective sleep metrics in service members with and without PTSD while controlling for these potential confounders. Methods Case-controlled observational cohort study performed at an academic military sleep center. Detailed sleep history, sleep-related questionnaires, trauma history, and video-polysomnography was performed. Patients with a history of TBI, abnormal neuroimaging, or psychoactive medication use (antidepressants, antipsychotics, anxiolytics) were excluded. Results Patients (N=169, 124 males) were active duty service members (median age 37.1 years) reporting a sleep complaint. Of these, 43 (25.4%) had PTSD vs 126 (74.6%) without PTSD. In patients with PTSD vs no PTSD, there was no significant difference in the rate of OSA, mean total sleep time, sleep efficiency, mean apnea-hypopnea index, sleep stage percentage, or any other polysomnogram (PSG) variable. Stage N3 sleep was non-significantly increased in patients with PTSD (18% vs 14%, p=0.078). Subjective sleepiness was similar between those with and without PTSD (Epworth Sleepiness Score 12.9 vs 11.9, p=0.35). However, subjective insomnia symptoms (Insomnia Severity Index 20.4 vs 15.2, p&amp;lt; 0.0001) and perceived sleep quality (Pittsburgh Sleep Quality Index 15.4 vs 11.1, p&amp;lt; 0.0001) were worse in those with PTSD vs no PTSD. Conclusion In a relatively young active duty cohort with no history of psychoactive medication use or TBI, patients with PTSD had more subjective sleep complaints than those without PTSD but objective sleep parameters, including PSG variables and OSA rate, did not differ significantly. The higher stage N3 (slow wave) sleep percentage, though not statistically significant, suggests that PTSD patients sleep better in a monitored sleep lab than in their habitual sleeping environment. Support (if any)

A joint sparse representation and low rank prior regularization model for image deblocking

When the compression quality factor (QF) is low, JPEG images are usually accompanied by very severe block artifacts. The conventional image deblocking models based on low-rank regularization focus on eliminating block artifacts, but lack the capacity of recovering texture details on image edges. In this paper, an image deblocking model which combines sparse representation and low-rank regularization based on fractional norm is proposed. To avoid the abrupt increase in computational complexity, multiple orthogonal dictionary learning is proposed to enhance the ability to sparsify diverse image structures and eliminate correlations among the atoms in the overcomplete dictionary. To further improve the image deblocking effect, a quantization constraint and fractional norm are used to more effectively enforce the low-rank property. Finally, an alternating minimization strategy is applied to solve the derived optimization problem. The model proposed in this paper preserves more texture details while effectively removing image block artifacts compared with the mainstream image deblocking model utilizing low-rank regularization alone. Experimental results show that the proposed method outperforms the current state-of-the-art image deblocking models in terms of both subjective metrics and objective perception.

CUCN: continuously updated connection network for low-light image enhancement

Low-light (LL) images make subsequent computer vision tasks difficult due to its low contrast. To solve this problem, the LL image enhancement problem is regarded as narrowing the gap between LL images and normal-light images in the process of iterative learning. A continuously updated connection network (CUCN) that is inspired by the recent development of convolutional neural networks is proposed. The proposed CUCN is composed of a continuously updated 4-units module (CU4UM), a feature fusion module (FFM), and a color enhancement module (CEM). The CU4UM adopts a connection method to combine four U-shaped structures with continuously updated parameters. To retain the global and local details while brightening an image, the FFM effectively combines the features of different layers in different U-shaped structures and assigns appropriate weights to each channel. The CEM takes the residuals of multiple features of different scales and performs iterative weighting to obtain more natural color information. The proposed method is evaluated on different public datasets, and the results show that the proposed CUCN method is superior to other state-of-the-art methods in terms of both subjective and objective metrics.

LL-CSFormer: A Novel Image Denoiser for Intensified CMOS Sensing Images under a Low Light Environment

Intensified complementary metal-oxide semiconductor (ICMOS) sensors can capture images under extremely low-light conditions (≤0.01 lux illumination), but the results exhibit spatially clustered noise that seriously damages the structural information. Existing image-denoising methods mainly focus on simulated noise and real noise from normal CMOS sensors, which can easily mistake the ICMOS noise for the latent image texture. To solve this problem, we propose a low-light cross-scale transformer (LL-CSFormer) that adopts multi-scale and multi-range learning to better distinguish between the noise and signal in ICMOS sensing images. For multi-scale aspects, the proposed LL-CSFormer designs parallel multi-scale streams and ensures information exchange across different scales to maintain high-resolution spatial information and low-resolution contextual information. For multi-range learning, the network contains both convolutions and transformer blocks, which are able to extract noise-wise local features and signal-wise global features. To enable this, we establish a novel ICMOS image dataset of still noisy bursts under different illumination levels. We also designed a two-stream noise-to-noise training strategy for interactive learning and data augmentation. Experiments were conducted on our proposed ICMOS image dataset, and the results demonstrate that our method is able to effectively remove ICMOS image noise compared with other image-denoising methods using objective and subjective metrics.

0759 Multidimensional Sleep Health is Associated with Emotional Well-being in Adolescents

Abstract Introduction Multidimensional sleep health (MSH) is a population-based approach to capture the 24-hour experience of sleep by incorporating measures of nighttime sleep and daytime functioning that promote physical and mental well-being. We have updated the existing adolescent MSH model to incorporate objective and subjective sleep measures and, in the present study, examined its utility in identifying associations between MSH and behavioral and emotional well-being. Methods We studied 377 adolescents (16.4 ± 2.3 yr; 46.4% female; 21.5% racial/ethnic minority) from the Penn State Child Cohort, a randomly-selected population-based sample. Using the RU-SATED framework – regularity, satisfaction, alertness, timing, efficiency, and duration – we derived an MSH composite score based on actigraphy-measured and self-reported sleep data. We examined the associations between MSH and scores on both the adolescent version of the Child Behavior Checklist (CBCL) and on the Pediatric Behavior Scale (PBS). CBCL’s internalizing, externalizing, thought and attention problems scores were self-reported, while similar behavioral and emotional cluster scores on the PBS were parent-reported. Results Higher MSH scores were associated with lower thought (r=-0.21, p&amp;lt; 0.001), externalizing (r=-0.18, p&amp;lt; 0.001), attention (r=-0.16, p&amp;lt; 0.01) or internalizing (r=-0.12, p&amp;lt; 0.05) problems. Higher MSH scores were associated with lower scores on the rule-breaking (r=-0.27, p&amp;lt; 0.001) and aggressive (r=-0.21, p&amp;lt; 0.001) externalizing subscales. For the internalizing subscales, higher MSH scores were associated with lower anxious-depressed (r = -0.17, p&amp;lt; 0.01), withdrawn-depressed (r=-0.14, p&amp;lt; 0.01), and somatic complaints (r=-0.12, p&amp;lt; 0.05) scores. Analyses based on parent reports on the more fine-grained PBS cluster scores replicated CBCL’s associations; for example, higher MSH scores were associated with lower depression (r=-0.19, p&amp;lt; 0.01) and inattention (r=-0.12, p&amp;lt; 0.05) as observed by parents. Conclusion Optimal sleep health, when considered as a multidimensional construct, is associated with emotional well-being and better behavioral outcomes in adolescents. Our MSH model, derived from objective and subjective sleep metrics, is able to capture similar trends from self-report and parent-report measures of emotion and behavior. Support (if any) National Institutes of Health (R01HL136587, UL1TR000127)

Be prepared: How training and emergency type affect evacuation behaviour

AbstractBackgroundVideo‐based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video‐based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness.ObjectivesThis study examines whether video‐based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies.MethodsWe simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video‐based training.Results and ConclusionsThe results revealed that participants with video‐based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self‐efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations.ImplicationsOur results suggested that video‐based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video‐based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type.

Mentorship is Critical: An Analysis of the 2022 Plastic Surgery Match.

As the second cycle impacted by COVID-19, the 2022 Plastic and Reconstructive Surgery (PRS) Match maintained virtual interviews while offering a modest lift of subinternship restrictions. The residency application process continues to evolve, with changes such as pass/fail United States Medical Licensing Examination Step 1 reporting prompting programs to reconsider metrics of applicant success. It is critical to address the impact of board scores, mentorship, and resource availability on a diverse applicant population in the PRS Match. A survey was electronically administered to students applying to a single institutional PRS residency program. The survey inquired about demographics, application statistics, mentorship experience, and match outcomes. Logistic regressions were modeled to assess for odds of matching into plastic surgery. In total, 151 responses were analyzed, a 49.7% response rate. Most participants were female (52.3%), White (68.9%), and not Hispanic/Latino (84.8%). The largest percentage of respondents had a faculty mentor only from their home institution (55.0%) and a resident mentor from only their home institution (32.3%). Participants with a faculty mentor from both a home and outside institution had 7.4 times the odds of matching into PRS ( P = 0.02) than students with no faculty mentorship. Students with dual-institution resident mentorship had 18.5 times higher the odds of matching compared with students with no resident mentorship ( P < 0.001). Subjective metrics, rather than objective scores, had the most influence on successfully matching into plastic surgery. As the PRS Match continues to become increasingly competitive, it behooves programs to provide equitable access to resources such as mentorship.

Development of acoustic denoising learning network for communication enhancement in construction sites

This paper presents a novel denoising approach based on deep learning and signal processing to improve communication efficiency. Construction activities take place when different trades come to the site for overlapped periods to perform their works, which may easily produce hazardous noise levels. The existence of noise affects workers' health issues, especially hearing and rhythm of the heart, and impacts communication efficiency between workers. The proposed approach employs signal processing technique to transform the noisy audio into image and utilize neural networks to extract noisy features and denoise the image. The denoised image is then converted to obtain the denoised audio. Experiments on reducing the side effect of several common noises in construction sites were conducted, compared with the performance of denoising using conventional wavelet transform. Standard objective measures, such as signal-to-noise ratio (SNR), and subjective measures, such as listening tests are used for evaluations. Our experimental results show that the proposed algorithm achieved significant improvements over the traditional method, as evidenced by the following quantitative results of median value: MSE of 0.002, RMSE of 0.049, SNR of 5.7 dB, PSNR of 25.8 dB, and SSR of 8.Results indicate that the proposed algorithm outperforms conventional denoising methods in terms of both objective and subjective evaluation metrics and have the potential to facilitate communication between site workers when facing different noise sources inevitably.

АНАЛІЗ СУБ’ЄКТИВНИХ МЕТРИК І ЕКСПЕРТНИХ МЕТОДІВ ОЦІНЮВАННЯ ЯКОСТІ ЗОБРАЖЕНЬ

An analysis of subjective metrics and expert methods of image quality assessment is provided

Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application

Transmission of high volume of data in a restricted wireless sensor network (WSN) has come up as a challenge due to high-energy consumption and larger bandwidth requirement. To address the issues of high-energy consumption and efficient data transmission adaptive block compressive sensing (ABCS) is one of the optimum solution. ABCS framework is well capable to adapt the sampling rate depending on the block’s features information that offers higher sampling rate for less compressible blocks and lower sampling rate for more compressible blocks In this paper, we have proposed a novel fuzzy rule based adaptive compressive sensing approach by leveraging the saliency and the edge features of the image making the sampling rate selection completely automatic. Adaptivity of the block sampling ratio has been decided based on the fuzzy logic system (FLS) by considering two important features i.e., edge and saliency information. The proposed framework is experimented on standard dataset, Kodak data set, CCTV images and the Set5 data set images. It achieved an average PSNR of 34.26 and 33.2 and an average SSIM of 0.87 and 0.865 for standard images and CCTV images respectively. Again for high resolution Kodak data set and Set 5 dataset images, it achieved an average PSNR of 32.95 and 31.72 and SSIM of 0.832 and 0.8 respectively. The experiments and the result analysis show that proposed method is efficacious than the state of the art methods in both subjective and objective evaluation metrics.

The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough?

Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown.

MOFA: A novel dataset for Multi-modal Image Fusion Applications

Multi-modal image fusion is widely used in various fields, especially in military, medical, industrial detection and other fields. Image fusion can integrate redundant and complementary information of two or more multi-modal images into one image, so that the fused image contains more useful information. In this paper, we construct a novel dataset for Multi-modal Image Fusion Applications (MOFA), including four modals: visible, near-infrared (NIR), long-wavelength infrared (LWIR) and polarization. The MOFA dataset contains 1062 images of 118 groups, in which 450 are indoor and 612 are outdoor. The dataset is applied to different image fusion applications, including general multi-modal image fusion, fusion based image super-resolution and image restoration. Multiple image fusion methods are compared and analyzed on this dataset with a qualitative assessment of subjective and objective metrics. Based on the experiments, the advantages and disadvantages of different methods are discussed. Moreover, the challenging problems of image fusion are concluded.

Defining and quantifying fatigue in the rugby codes.

The rugby codes (i.e., rugby union, rugby league, rugby sevens [termed 'rugby']) are team-sports that impose multiple complex physical, perceptual, and technical demands on players which leads to substantial player fatigue post-match. In the post-match period, fatigue manifests through multiple domains and negatively influences recovery. There is, however, currently no definition of fatigue contextualised to the unique characteristics of rugby (e.g., locomotor and collision loads). Similarly, the methods and metrics which practitioners consider when quantifying the components of post-match fatigue and subsequent recovery are not known. The aims of this study were to develop a definition of fatigue in rugby, to determine agreement with this common definition of fatigue, and to outline which methods and metrics are considered important and feasible to implement to quantify post-match fatigue. Subject matter experts (SME) undertook a two-round online Delphi questionnaire (round one; n = 42, round two; n = 23). SME responses in round one were analysed to derive a definition of fatigue, which after discussion and agreement by the investigators, obtained 96% agreement in round two. The SME agreed that fatigue in rugby refers to a reduction in performance-related task ability which is underpinned by time-dependent negative changes within and between cognitive, neuromuscular, perceptual, physiological, emotional, and technical/tactical domains. Further, there were 33 items in the neuromuscular performance, cardio-autonomic, or self-report domains achieved consensus for importance and/or feasibility to implement. Highly rated methods and metrics included countermovement jump force/power (neuromuscular performance), heart rate variability (cardio-autonomic measures), and soreness, mood, stress, and sleep quality (self-reported assessments). A monitoring system including highly-rated fatigue monitoring objective and subjective methods and metrics in rugby is presented. Practical recommendations of objective and subjective measures, and broader considerations for testing and analysing the resulting data in relation to monitoring fatigue are provided.

Expressing appreciation is linked to interpersonal closeness and inter-brain coherence, both in person and over Zoom.

Expressing appreciation is essential for establishing interpersonal closeness, but virtual interactions are increasingly common and create social distance. Little is known about the neural and inter-brain correlates of expressing appreciation and the potential effects of virtual videoconferencing on this kind of interaction. Here, we assess inter-brain coherence with functional near-infrared spectroscopy while dyads expressed appreciation to one another. We scanned 36 dyads (72 participants) who interacted in either an in-person or virtual (Zoom®) condition. Participants reported on their subjective experience of interpersonal closeness. As predicted, expressing appreciation increased interpersonal closeness between dyad partners. Relative to 3 other cooperation tasks (i.e. problem-solving task, creative-innovation task, socio-emotional task), we observed increased inter-brain coherence in socio-cognitive areas of the cortex (anterior frontopolar area, inferior frontal gyrus, premotor cortex, middle temporal gyrus, supramarginal gyrus, and visual association cortex) during the appreciation task. Increased inter-brain coherence in socio-cognitive areas during the appreciation task was associated with increased interpersonal closeness. These findings support the perspective that expressing appreciation, both in-person and virtually, increases subjective and neural metrics of interpersonal closeness.