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This study aimed to compare the predictive accuracy of absolute and relative external load indices (ELI) across three machine learning models, and predict the rating of perceived exertion (RPE) of elite Gaelic football players using ELI, personal characteristics, wellness scores, and training workloads. ELI and related variables were collected from 49 elite Gaelic football players over three competitive seasons resulting in 1617 observations. ELI included total distance, high speed running distance (≥ 4.72 m · s -1 ), and number of accelerations and decelerations (n ± 3 m · s -2 ), expressed in both absolute and relative terms. Variables related to personal characteristics, wellness scores, and training workloads were also included. Data were analysed using decision tree, random forest (RF), and bootstrap aggregation (BS) models. The RF model had the highest predictive accuracy using absolute and relative ELI only, at 54.3% and 48.3%, respectively. Total and relative distance were the strongest predictors of RPE in the RF model, accounting for 38.8% and 27.9% of the normalised importance. The BS model had the highest accuracy at 67.0% and 65.2% for absolute and relative ELI when performed in conjunction with the related variables, respectively. The current models demonstrate potential to predict RPE and subsequently optimise training load in Gaelic football., Competing Interests: No conflict of interest, financial or otherwise, is declared by the authors., (Copyright © Institute of Sport – National Research Instutite.)

Objectives: To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts., Methods: Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement. Insufficient or duplicate referrals were discarded. The inter-rater agreement among radiographers and consultants was computed. A random split (4:1) was performed to apply machine learning (ML) and deep learning (DL) techniques to unstructured clinical indications to automate retrospective justification auditing with multi-class classification. The accuracy and macro-averaged F1 score of the best-performing classifier of each type on the training set were computed on the test set., Results: 42 referrals were ignored. 1909 (64.5%) referrals were justified, 811 (27.4%) were potentially justified, and 238 (8.1%) were unjustified. The agreement between radiographers (κ = 0.268) was lower than radiologists (κ = 0.460). The best-performing ML model was the bag-of-words-based gradient-boosting classifier achieving a 94.4% accuracy and a macro F1 of 0.94. DL models were inferior, with bi-directional long short-term memory achieving 92.3% accuracy, a macro F1 of 0.92, and outperforming multilayer perceptrons., Conclusion: Interpreting unstructured clinical indications is challenging necessitating clinical decision support. ML and DL can generalise across multiple clinical sites, outperform human experts, and be used as an artificial intelligence-based iGuide interpreter when retrospectively vetting radiology referrals., Clinical Relevance Statement: Healthcare vendors and clinical sites should consider developing and utilising artificial intelligence-enabled systems for justifying medical exposures. This would enable better implementation of imaging referral guidelines in clinical practices and reduce population dose burden, CT waiting lists, and wasteful use of resources., Key Points: Significant variations exist among human experts in interpreting unstructured clinical indications/patient presentations. Machine and deep learning can automate the justification analysis of radiology referrals according to iGuide categorisation. Machine and deep learning can improve retrospective and prospective justification auditing for better implementation of imaging referral guidelines., (© 2024. The Author(s).)

This study investigated the test-retest reliability of running economy (RE) and metabolic and cardiorespiratory parameters related to endurance running performance using a multistage incremental treadmill test. On two occasions separated by 21-28 days, 12 male middle- and long-distance runners ran at 10, 11, 12, 13, and 14 km/hr for 8 min each stage, immediately followed by a ramp test to volitional exhaustion. Carbohydrate (10% maltodextrin solution) was consumed before and during the test to provide ∼1 g/min of exercise. RE, minute ventilation (V˙E), oxygen consumption (V˙O2), carbon dioxide production (V˙CO2), respiratory exchange ratio (RER), heart rate (HR), ratings of perceived exertion (RPE), and blood glucose and lactate concentrations were recorded for each stage and at volitional exhaustion. Time-to-exhaustion (TTE) and peak oxygen consumption (V˙O2peak) during the ramp test were also recorded. Absolute reliability, calculated as the coefficient of variation (CV) between repeated measures, ranged from 2.3% to 3.1% for RE, whereas relative reliability, calculated as the intraclass correlation coefficient (ICC), ranged from .42 to .79. V˙E, V˙O2, V˙O2peak, V˙CO2, RER, and HR had a CV of 1.1%-4.3% across all stages. TTE and RPE had a CV of 7.2% and 2.3%-10.8%, respectively, while glucose and lactate had a CV of 4.0%-17.8%. All other parameters, except for blood glucose, were demonstrated to have good-to-excellent relative reliability assessed by ICC. Measures of RE, V˙O2peak, and TTE were reliable during this two-phase multistage incremental treadmill test in a cohort of trained and highly trained male middle- and long-distance runners.

Background: Sensorimotor dysfunction is thought to occur following anterior cruciate ligament (ACL) injury which may have implications on future reinjury risk. Dysfunction has been demonstrated within the efferent component of the sensorimotor system. However, no reviews have examined the two main components of the afferent system: the visual and somatosensory systems., Hypothesis/purpose: This study aimed to report differences in function (central processing and local processing) within the (1) somatosensory and (2) visual systems between individuals following anterior cruciate ligament reconstruction (ACLR) and healthy controls (between-subject). The study also aimed to report differences in function within the two systems between the two limbs of an individual following ACLR (within-subject)., Study Design: Scoping review., Methods: A search was conducted in PubMed, SPORTDiscus, CINAHL, Medline and Embase up until September 2021. Level I-IV studies assessing somatosensory and visual systems were included if they compared ACLR limbs to the uninjured contralateral limb (within-subject) or a healthy control limb (between-group). The function of somatosensory and visual systems was assessed across both central processing (processing of information in the central cortex) and local processing (all other assessments outside of central processing of information)., Results: Seventy studies were identified (52 somatosensory, 18 visual). Studies examining somatosensory central processing demonstrated significant differences; 66% of studies exhibited within-subject differences and 100% of the studies exhibited between-group differences. Studies examining local somatosensory processing had mixed findings; 40% of the 'joint position sense (JPS)' and 'threshold to detect motion (TTDM)' studies showed significant within-subject differences (JPS=0.8°-3.8° and TTDPM=0.2°-1.4°) and 42% demonstrated significant between-group differences (JPS=0.4°-5° and TTDPM=0.3°-2.8°). Eighty-three percent of visual central processing studies demonstrated significant dysfunction between-groups with no studies assessing within-subject differences. Fifty percent of the studies examining local visual processing demonstrated a significant between-group difference., Conclusion: Significant differences in central processing exist within somatosensory and visual systems following ACLR. There is mixed evidence regarding local somatosensory and visual processing. Increased compensation by the visual system and local visual processing dysfunction may occur in conjunction with somatosensory dysfunction., Competing Interests: No conflicts of interest exist for the authors., (© The Author(s).)

This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8-99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.

Dairy is an economically significant industry that caters to the huge demand for food products in people's lives. To remain profitable, farmers need to manage their farms and the health of the dairy cows in their herds. There are, however, many risks to cow health that can lead to significant challenges to dairy farm management and have the potential to lead to significant losses. Such risks include cow udder infections (i.e., mastitis) and cow lameness. As automation and data recording become more common in the agricultural sector, dairy farms are generating increasing amounts of data. Recently, these data are being used to generate insights into farm and cow health, where the objective is to help farmers manage the health and welfare of dairy cows and reduce losses from cow health issues. Despite the level of data generation on dairy farms, this information is often difficult to access due to a lack of a single, central organization to collect data from individual farms. The prospect of such an organization, however, raises questions about data ownership, with some farmers reluctant to share their farm data for privacy reasons. In this study, we describe a new data mesh architecture designed for the dairy industry that focuses on facilitating access to data from farms in a decentralized fashion. This has the benefit of keeping the ownership of data with dairy farmers while bringing data together by providing a common and uniform set of protocols. Furthermore, this architecture will allow secure access to the data by research groups and product development groups, who can plug in new projects and applications built across the data. No similar framework currently exists in the dairy industry, and such a data mesh can help industry stakeholders by bringing the dairy farms of a country together in a decentralized fashion. This not only helps farmers, dairy researchers, and product builders but also facilitates an overview of all dairy farms which can help governments to decide on regulations to improve the dairy industry at a national level., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Pakrashi, Wallace, Mac Namee, Greene and Guéret.)

Background: Placenta accreta spectrum (PAS) is a rare, life-threatening complication of pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We aim to explore whether radiomic analysis of T2-weighted magnetic resonance imaging (MRI) can predict severe cases by distinguishing between histopathological subtypes antenatally., Methods: This was a bi-centre retrospective analysis of a prospective cohort study conducted between 2018 and 2022. Women who underwent MRI during pregnancy and had histological confirmation of PAS were included. Radiomic features were extracted from T2-weighted images. Univariate regression and multivariate analyses were performed to build predictive models to differentiate between non-invasive (International Federation of Gynecology and Obstetrics [FIGO] grade 1 or 2) and invasive (FIGO grade 3) PAS using R software. Prediction performance was assessed based on several metrics including sensitivity, specificity, accuracy and area under the curve (AUC) at receiver operating characteristic analysis., Results: Forty-one women met the inclusion criteria. At univariate analysis, 0.64 sensitivity (95% confidence interval [CI] 0.0-1.00), specificity 0.93 (0.38-1.0), 0.58 accuracy (0.37-0.78) and 0.77 AUC (0.56-.097) was achieved for predicting severe FIGO grade 3 PAS. Using a multivariate approach, a support vector machine model yielded 0.30 sensitivity (95% CI 0.18-1.0]), 0.74 specificity (0.38-1.00), 0.58 accuracy (0.40-0.82), and 0.53 AUC (0.40-0.85)., Conclusion: Our results demonstrate a predictive potential of this machine learning pipeline for classifying severe PAS cases., Relevance Statement: This study demonstrates the potential use of radiomics from MR images to identify severe cases of placenta accreta spectrum antenatally., Key Points: • Identifying severe cases of placenta accreta spectrum from imaging is challenging. • We present a methodological approach for radiomics-based prediction of placenta accreta. • We report certain radiomic features are able to predict severe PAS subtypes. • Identifying severe PAS subtypes ensures safe and individualised care planning for birth., (© 2023. European Society of Radiology (ESR).)

The purpose of this study was to compare the activity profile of elite Gaelic football referees (GFR) between the National Football League (NFL) and the All-Ireland Championship (AIC), and across the four divisions of the NFL and three phases of the AIC. Match activity data was collected during 125 NFL and 201 AIC games using 10-Hz global positioning system technology from 41 elite GFR. Game duration, total distance, very low-speed movement (<0.70 m·s -1 ), walking (≥0.70-1.65 m·s -1 ), low-speed running (≥1.66-3.27 m·s -1 ), moderate-speed running (≥3.28-4.86 m·s -1 ), high-speed running (≥4.87-6.48 m·s -1 ), very high-speed running (≥6.49 m·s -1 ) distance, and peak running speed were compared between competitions. Games in the AIC were longer than in the NFL (ES = 0.59) but the total distance was similar between the NFL (119.6 ± 9.5 m·min -1 ) and AIC (122.6 ± 8.4 m·min -1 , ES = 0.11). No other differences were found between the NFL and AIC or across the four divisions of the NFL and three phases of the AIC, except for a higher peak running speed during the All-Ireland Series (6.93 ± 0.52 m·s -1 ) than the All-Ireland Qualifiers (6.65 ± 0.46 m·s -1 , ES = 0.35). This information can be used to design specific conditioning programmes to ensure optimal physical development of GFR at all competitive levels.

This research presents a novel stand-alone device for the autonomous measurement of gas pressure levels on an active landfill site, which enables the real-time monitoring of gas dynamics and supports the early detection of critical events. The developed device employs advanced sensing technologies and wireless communication capabilities, enabling remote data transmission and access via the Internet. Through extensive field experiments, we demonstrate the high sampling rate of the device and its ability to detect significant events related to gas generation dynamics in landfills, such as flare shutdowns or blockages that could lead to hazardous conditions. The validation of the device's performance against a high-end analytical system provides further evidence of its reliability and accuracy. The developed technology herein offers a cost-effective and scalable solution for environmental landfill gas monitoring and management. We expect that this research will contribute to the advancement of environmental monitoring technologies and facilitate better decision-making processes for sustainable waste management.

Physical literacy provides a foundation for lifelong engagement in physical activity, resulting in positive health outcomes. Direct pathways between physical literacy and health have not yet been investigated thoroughly. Associations between physical literacy and well-being in children (n = 1073, mean age 10.86 ± 1.20 years) were analysed using machine learning. Motor competence (TGMD-3 and BOT-2) and health-related fitness (PACER and plank) were assessed in the physical competence domain. Motivation (adapted-Behavioural Regulation in Exercise Questionnaire) and confidence (modified-Physical Activity Self-Efficacy Scale) were assessed in the affective domain. Well-being was measured using the KIDSCREEN-27. Accuracy of predicting well-being from physical literacy was investigated using five machine learning classifiers (decision tree, random forest, XGBoost, AdaBoost, k-nearest neighbour) in the full sample and across subgroups (sex, socioeconomic status [SES], age). XGBoost predicted well-being from physical literacy with an accuracy of 87% in the full sample. Predictive accuracy was lowest in low SES participants. Contribution of physical literacy features differed substantially across subgroups. Physical literacy predicts well-being in children but the relative contribution of physical literacy features to well-being differs substantially between subgroups., (© 2023 The Authors. Applied Psychology: Health and Well-Being published by John Wiley & Sons Ltd on behalf of International Association of Applied Psychology.)

This data article describes two groups of datasets which capture, firstly - 10-minutes air temperature (T a ) and relative humidity (RH) data from 27 urban and non-urban sites over a period of 3.5 years covering 2014-2018; and secondly - hourly T a data from 12 urban sites over a period of 2 years covering 2016 and 2017. Both datasets are from urban meteorological network located in the Novi Sad city (Serbia). These datasets have 2 different types of information in the collection: one type provides details about the monitoring sites at which the T a and RH sensors are placed, while the second type contains T a and RH data at all sensor locations. In all, the 10-minutes dataset contains about 185,000 instances of T a and RH data, and the hourly datasets contain 17,544 instances of T a data. The 10-minutes datasets were not quality controlled, but the hourly T a data has been cleaned and gap-filled so there are 24 measures at each site for each day. There are multiple potential uses, where this data can be applied. It can provide insights in understanding intra-urban and inter-urban research, urban climate modeling on local or micro scales, heat-related public health investigations and urban environment inquiries. It can also be used in machine learning experiments, for example, to test the accuracy of classification algorithms or to build and validate spatio-temporal machine learning functions, either for classification purposes or for gap filling. These datasets are directly citable through its DOIs and available for download from the Zenodo platform or from the Fair Micromet Portal., (© 2023 The Author(s).)

Objective: The NEX project has developed an integrated Internet of Things (IoT) system coupled with data analytics to offer unobtrusive health and wellness monitoring supporting older adults living independently at home. Monitoring involves visualising a set of automatically detected activities of daily living (ADLs) for each participant. ADL detection allows the incorporation of additional participants whose ADLs are detected without system re-training., Methods: Following a user needs and requirements study involving 426 participants, a pilot trial and a friendly trial of the deployment, an action research cycle (ARC) trial was completed. This involved 23 participants over a 10-week period each with ∼ 20 IoT sensors in their homes. During the ARC trial, participants took part in two data-informed briefings which presented visualisations of their own in-home activities. The briefings also gathered training data on the accuracy of detected activities. Association rule mining was used on the combination of data from sensors and participant feedback to improve the automatic ADL detection., Results: Association rule mining was used to detect a range of ADLs for each participant independently of others and then used to detect ADLs across participants using a single set of rules for each ADL. This allows additional participants to be added without the necessity of them providing training data., Conclusions: Additional participants can be added to the NEX system without the necessity to re-train the system for automatic detection of their ADLs., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

The matrix profile (MP) is a data structure computed from a time series which encodes the data required to locate motifs and discords, corresponding to recurring patterns and outliers respectively. When the time series contains noisy data then the conventional approach is to pre-filter it in order to remove noise but this cannot apply in unsupervised settings where patterns and outliers are not annotated. The resilience of the algorithm used to generate the MP when faced with noisy data remains unknown. We measure the similarities between the MP from original time series data with MPs generated from the same data with noisy data added under a range of parameter settings including adding duplicates and adding irrelevant data. We use three real world data sets drawn from diverse domains for these experiments Based on dissimilarities between the MPs, our results suggest that MP generation is resilient to a small amount of noise being introduced into the data but as the amount of noise increases this reslience disappears., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Hehir, Smeaton. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

There is a paucity of studies describing the physical match demands of elite international women's rugby union, which limits coaches' ability to effectively prepare players for the physical demands required to compete at the elite level. Global positioning system technologies were used to measure the physical match demands of 53 international female rugby union players during three consecutive Women's Six Nations Championships (2020-2022), resulting in 260 individual match performances. Mixed-linear modelling was used to investigate differences in physical match demands between positions. Significant effects ( p < 0.05) of the position were observed for all variables, with the exception of relative distances (m.min -1 ) at velocities of 1.01-3.00 m·s -1 ( p = 0.094) and 3.01-5.00 m·s -1 ( p = 0.216). This study provides valuable data on the physical match demands of elite international women's rugby union match play that may aid practitioners in the physical preparation of players to compete at this level. Training methodologies for elite-level female rugby union players should consider the unique demands across positional groups with specific considerations of high-velocity running and collision frequency.

The purpose of this study was to examine the activity profile of elite Gaelic football referees (GFR) and to examine temporal changes between the first and second half and across the four quarters. Global positioning systems technology (10-Hz) was used to collect activity data during 202 competitive games from 23 elite GFR. Relative distance, peak running speed and relative distance covered in six movement categories [very low-speed movement (VLSM) (<0.70 m·s -1 ), walking (≥0.70-1.65 m·s -1 ), low-speed running (LSR) (≥1.66-3.27 m·s -1 ), moderate-speed running (MSR) (≥3.28-4.86 m·s -1 ), high-speed running (HSR) (≥4.87-6.48 m·s -1 ), very high-speed running (VHSR) (≥6.49 m·s -1 )] were examined during the full game, first and second half, and across the four quarters. The relative distance covered was 122.6 ± 8.4 m·min -1 , with 13.1 ± 4.9 m·min -1 of HSR and VHSR. The peak running speed was 6.75 ± 0.49 m·s -1 . The relative (ES=0.60), MSR (ES=0.50) and HSR (ES=0.14) distance was higher in the first half than the second half. A higher relative (ES=0.62-0.91) and HSR (ES=0.51-0.61) distance was found in the first quarter than any other period. No differences in HSR distance were found between the second, third and fourth quarters (ES=0.04-0.10). This study provides, for the first time, a detailed insight into the activity profile of elite GFR during competitive games and demonstrates the demanding, intermittent nature of elite refereeing in Gaelic football. This information may be used as a framework for coaches to design training programmes specific to GFR.

Sit-to-stand can be defined as a set of movements that allow humans to rise from a sitting position to a bipedal standing pose. These movements, often categorized as four distinct kinematic phases, must be coordinated for assuring personal autonomy and can be compromised by ageing or physical impairments. To solve this, rehabilitation techniques and assistive devices demand proper description of the principles that lead to the correct completion of this motor task. While the muscular dynamics of the sit-to-stand task have been analysed, the underlying neural activity remains unknown and largely inaccessible for conventional measurement systems. Predictive simulations can propose motor controllers whose plausibility is evaluated through the comparison between simulated and experimental kinematics. In the present work, we modelled an array of reflexes that originate muscle activations as a function of proprioceptive and vestibular feedback. This feedback encodes torso position, displacement velocity and acceleration of a modelled human body with 7 segments, 9 degrees of freedom, and 50 actuators. We implemented two controllers: a four-phases controller where the reflex gains and composition vary depending on the kinematic phase, and a simpler two-phases controller, where three of the kinematic phases share the same reflex gains. Gains were optimized using Covariance Matrix Adaptation. The results of the simulations reveal, for both controllers, human-like sit-to-stand movement, with joint angles and muscular activity comparable to experimental data. The results obtained with the simplified two-phases controller indicate that a simple set of reflexes could be sufficient to drive this motor task., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Muñoz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Athletic groin pain (AGP) can lead to altered movement patterns during rapid deceleration and acceleration. However, the effect of AGP on movement variability and loading patterns during such actions remains less clear., Purpose: To investigate, using a continuous lateral hurdle hop task, how movement variability and magnitude measures of 3-dimensional (3D) kinematic, kinetic, and vertical ground-reaction force (vGRF) variables are (1) affected by AGP (AGP vs uninjured controls [CON]) and (2) changed after successful rehabilitation (AGP prerehabilitation vs AGP postrehabilitation vs CON)., Study Design: Controlled laboratory study., Methods: A total of 36 athletes diagnosed with AGP and 36 uninjured CON athletes matched on age (18-35 years), level (subelite), and type of sports played (multidirectional field sport) performed a continuous lateral hurdle hop test that involved 10 side-to-side hops over a 15-cm hurdle. The 3D joint kinematic, kinetic, and vGRF variables (total, eccentric, and concentric; ground contact time, peak force, and impulse; and eccentric rate of force development) were examined. The AGP and CON groups were tested at baseline, and the AGP group was retested after participants successfully completed a standardized, exercise-based rehabilitation program targeting intersegmental control., Results: There were no differences in baseline characteristics between the AGP (mean ± SD: age, 27.5 ± 4.8 years; height, 179.8 ± 6.3 cm; mass, 80.3 ± 7.1 kg) and CON (mean ± SD: age, 24.1 ± 4.5 years; height, 181.0 ± 5.8 cm; mass, 80.4 ± 8.2 kg) groups. At baseline, athletes with AGP demonstrated altered loading patterns in the vGRF (longer ground contact times, reduced peak force, and reduced rate of force development) compared with CON athletes, while no significant difference in any movement variability variables was evident. After rehabilitation, the athletes with AGP demonstrated significant changes in transverse and coronal plane hip and trunk kinematics, with no significant differences in vGRF variables compared with the CON group., Conclusion: The differences in baseline vGRF measures between the AGP and CON groups were no longer evident after athletes with AGP underwent rehabilitation. No differences in movement variability were evident between the AGP and CON groups, either before or after rehabilitation., Clinical Relevance: Rehabilitation programs should consider targeting intersegmental hip and trunk movement patterns to positively influence loading patterns in athletes with AGP., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: This study was funded by the Sports Surgery Clinic as an industrial partner of Insight Data Centre of Analytics, Dublin City University, and Science Foundation Ireland (grant SFI/12/RC/2289). AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto., (© The Author(s) 2022.)

Circadian rhythms are a process of the sleep-wake cycle that regulates the physical, mental and behavioural changes in all living beings with a period of roughly 24 h. Wearable accelerometers are typically used in livestock applications to record animal movement from which we can estimate the activity type. Here, we use the overall movement recorded by accelerometers worn on the necks of newborn calves for a period of 8 weeks. From the movement data, we calculate 24 h periodicity intensities corresponding to circadian rhythms, from a 7-day window that slides through up to 8-weeks of data logging. The strength or intensity of the 24 h periodicity is computed at intervals as the calves become older, which is an indicator of individual calf welfare. We observe that the intensities of these 24 h periodicities for individual calves, derived from movement data, increase and decrease synchronously in a herd of 19 calves. Our results show that external factors affecting the welfare of the herd can be observed by processing and visualising movement data in this way and our method reveals insights that are not observable from movement data alone.

Background: In a rapidly aging population, new and efficient ways of providing health and social support to older adults are required that not only preserve independence but also maintain quality of life and safety., Objective: The NEX project aims to develop an integrated Internet of Things system coupled with artificial intelligence to offer unobtrusive health and wellness monitoring to support older adults living independently in their home environment. The primary objective of this study is to develop and evaluate the technical performance and user acceptability of the NEX system. The secondary objective is to apply machine learning algorithms to the data collected via the NEX system to identify and eventually predict changes in the routines of older adults in their own home environment., Methods: The NEX project commenced in December 2019 and is expected to be completed by August 2022. Mixed methods research (web-based surveys and focus groups) was conducted with 426 participants, including older adults (aged ≥60 years), family caregivers, health care professionals, and home care workers, to inform the development of the NEX system (phase 1). The primary outcome will be evaluated in 2 successive trials (the Friendly trial [phase 2] and the Action Research Cycle trial [phase 3]). The secondary objective will be explored in the Action Research Cycle trial (phase 3). For the Friendly trial, 7 older adult participants aged ≥60 years and living alone in their own homes for a 10-week period were enrolled. A total of 30 older adult participants aged ≥60 years and living alone in their own homes will be recruited for a 10-week data collection period (phase 3)., Results: Phase 1 of the project (n=426) was completed in December 2020, and phase 2 (n=7 participants for a 10-week pilot study) was completed in September 2021. The expected completion date for the third project phase (30 participants for the 10-week usability study) is June 2022., Conclusions: The NEX project has considered the specific everyday needs of older adults and other stakeholders, which have contributed to the design of the integrated system. The innovation of the NEX system lies in the use of Internet of Things technologies and artificial intelligence to identify and predict changes in the routines of older adults. The findings of this project will contribute to the eHealth research agenda, focusing on the improvement of health care provision and patient support in home and community environments., International Registered Report Identifier (irrid): DERR1-10.2196/35277., (©Claire M Timon, Emma Heffernan, Sophia M Kilcullen, Hyowon Lee, Louise Hopper, Joe Quinn, David McDonald, Pamela Gallagher, Alan F Smeaton, Kieran Moran, Pamela Hussey, Catriona Murphy. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 05.05.2022.)

This study examined the relationship between fundamental movement skills (FMS) and health related fitness (HRF) components in children. A cross section of Irish primary school children across all age groups participated in this study (n=2098, 47% girls, age 5-12 years of age, mean age 9.2 ± 2.04). FMS were measured using the Test of Gross Motor Development (TGMD-3), along with two additional assessments of vertical jump and balance. All HRF components were also assessed: body composition through BMI and waist circumference, muscular strength (MS) using a hand dynamometer, muscular endurance (ME) through the plank test, flexibility with back-saver sit-and-reach, and cardiovascular endurance (CVE) using the 20 m PACER test. Hierarchal multiple regressions were used to measure associations between the HRF components and overall FMS and the FMS subtests: locomotor, object control and balance skills. Results show significant positive relationships between FMS and MS ( R 2  = 0.25, β = -0.19), ME ( R 2  = 0.11, β  = 0.34), flexibility ( R 2  = 0.13, β  = 0.14) and CVE ( R = -0.19). The data presented reinforces the position that the relationship between FMS and HRF is dynamic, and predominantly strengthens with age through the course of childhood. Findings suggest that developing FMS as a child may be important to developing HRF across childhood and into adolescence.2  = 0.17, β  = 0.39), and an inverse relationship between FMS and body composition ( R 2  = 0.25, β = -0.19). The data presented reinforces the position that the relationship between FMS and HRF is dynamic, and predominantly strengthens with age through the course of childhood. Findings suggest that developing FMS as a child may be important to developing HRF across childhood and into adolescence.

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2021 The Author(s).)

The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images, but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7,987 meats cuts extracted from semimembranosus muscles (i.e., Topside muscle), post editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions), precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network and residual network. The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Can methods from computational models of decision-making be used to build a predictive model to identify individuals most likely to be nonadherent to personal fitness goals? Such a model may have significant value in the global battle against obesity. Despite growing awareness of the impact of physical inactivity on human health, sedentary behavior is increasingly linked to premature death in the developed world. The annual impact of sedentary behavior is significant, causing an estimated 2 million deaths. From a global perspective, sedentary behavior is one of the 10 leading causes of mortality and morbidity. Annually, considerable funding and countless public health initiatives are applied to promote physical fitness, with little impact on sustained behavioral change. Predictive models developed from multimodal methodologies combining data from decision-making tasks with contextual insights and objective physical activity data could be used to identify those most likely to abandon their fitness goals. This has the potential to enable development of more targeted support to ensure that those who embark on fitness programs are successful., Objective: The aim of this study is to determine whether it is possible to use decision-making tasks such as the Iowa Gambling Task to help determine those most likely to abandon their fitness goals. Predictive models built using methods from computational models of decision-making, combining objective data from a fitness tracker with personality traits and modeling from decision-making games delivered via a mobile app, will be used to ascertain whether a predictive algorithm can identify digital personae most likely to be nonadherent to self-determined exercise goals. If it is possible to phenotype these individuals, it may be possible to tailor initiatives to support these individuals to continue exercising., Methods: This is a siteless study design based on a bring your own device model. A total of 200 healthy adults who are novice exercisers and own a Fitbit (Fitbit Inc) physical activity tracker will be recruited via social media for this study. Participants will provide consent via the study app, which they will download from the Google Play store (Alphabet Inc) or Apple App Store (Apple Inc). They will also provide consent to share their Fitbit data. Necessary demographic information concerning age and sex will be collected as part of the recruitment process. Over 12 months, the scheduled study assessments will be pushed to the subjects to complete. The Iowa Gambling Task will be administered via a web app shared via a URL., Results: Ethics approval was received from Dublin City University in December 2020. At manuscript submission, study recruitment was pending. The expected results will be published in 2022., Conclusions: It is hoped that the study results will support the development of a predictive model and the study design will inform future research approaches., Trial Registration: ClinicalTrials.gov NCT04783298; https://clinicaltrials.gov/ct2/show/NCT04783298., (©Marie McCarthy, Lili Zhang, Greta Monacelli, Tomas Ward. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 26.11.2021.)

Microbial volatilomics is a rapidly growing field of study and has shown great potential for applications in food, farming, and clinical sectors in the future. Due to the varying experimental methods and growth conditions employed in microbial volatilomic studies as well as strain-dependent volatilomic differences, there is limited knowledge regarding the stability of microbial volatilomes. Consequently, cross-study comparisons and validation of results and data can be challenging. In this study, we investigated the stability of the volatilomes of multiple strains of Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli across three frequently used nutrient-rich growth media. Volatilomic stability was assessed based on media-, time- and strain-dependent variation across the examined bacterial volatilomes. Strain-level specificity of the observed volatilomes of E. coli and P. aeruginosa strains was further investigated by comparing the emission of selected compounds at varying stages of cell growth. Headspace solid phase microextraction (HS-SPME) sampling coupled with gas chromatography mass spectrometry (GC-MS) was used to analyze the volatilome of each strain. The whole volatilomes of the examined strains demonstrate a high degree of stability across the three examined growth media. At the compound-level, media dependent differences were observed particularly when comparing the volatilomes obtained in glucose-containing brain heart infusion (BHI) and tryptone soy broth (TSB) growth media with the volatilomes obtained in glucose-free Lysogeny broth (LB) media. These glucose-dependent volatilomic differences were primarily seen in the emission of primary metabolites such as alcohols, ketones, and acids. Strain-level differences in the emission of specific compounds in E. coli and P. aeruginosa samples were also observed across the media. These strain-level volatilomic differences were also observed across varying phases of growth of each strain, therefore confirming that these strains had varying core and accessory volatilomes. Our results demonstrate that, at the species-level, the examined bacteria have a core volatilome that exhibits a high-degree of stability across frequently-used growth media. Media-dependent differences in microbial volatilomes offer valuable insights into identifying the cellular origin of individual metabolites. The observed differences in the core and accessory volatilomes of the examined strains illustrate the complexity of microbial volatilomics as a study while also highlighting the need for more strain-level investigations to ultimately elucidate the whole volatilomic capabilities of microbial species in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Fitzgerald, Holland and Morrin.)

Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390)., (© 2021. The Author(s).)

Background: Exercise-based rehabilitation targeting intersegmental control has high success rates and fast recovery times in the management of athletic groin pain (AGP). The influence of this approach on hip strength and lower limb reactive strength and how these measures compare with uninjured athletes (CON) remain unknown. Additionally, the efficacy of this program after return to play (RTP) has not been examined., Purpose: First, to examine differences in isometric hip strength, reactive strength, and the Hip and Groin Outcome Score (HAGOS) between the AGP and CON cohorts and after rehabilitation; second, to examine the relationship between the change in HAGOS and the change in strength variables after rehabilitation; last, to track HAGOS for 6 months after RTP., Study Design: Cohort study; Level of evidence, 2., Methods: A total of 42 athletes diagnosed with AGP and 36 matched controls completed baseline testing: isometric hip strength, lower limb reactive strength, and HAGOS. After rehabilitation, athletes with AGP were retested, and HAGOS was collected at 3 and 6 months after RTP., Results: In total, 36 athletes with AGP completed the program with an RTP time of 9.8 ± 3.0 weeks (mean ± SD). At baseline, these athletes had significantly lower isometric hip strength (abduction, adduction, flexion, extension, external rotation: d = - 0.67 to -1.20), single-leg reactive strength ( d = - 0.73), and HAGOS ( r = - 0.74 to -0.89) as compared with the CON cohort. Hip strength ( d = -0.83 to -1.15) and reactive strength ( d = - 0.30) improved with rehabilitation and were no longer significantly different between groups at RTP. HAGOS improvements were maintained or improved in athletes with AGP up to 6 months after RTP, although some subscales remained significantly lower than the CON group ( r = - 0.35 to -0.51). Two linear regression features (hip abduction and external rotation) explained 11% of the variance in the HAGOS Sports and Recreation subscale., Conclusion: Athletes with AGP demonstrated isometric hip strength and reactive strength deficits that resolved after an intersegmental control rehabilitation program; however, improved hip strength explained only 11% of improvement in the Sports and Recreation subscale. HAGOS improvements after pain-free RTP were maintained at 6 months.

Increased screen time has been found to be associated with a number of negative health and wellbeing indicators in youth populations. An increasing number of studies have investigated the association between screen time and wellbeing in adolescents, but evidence in younger children is still emerging. This 2017 study explored the effect of leisure screen time and gender on dimensions of wellbeing (measured using KIDSCREEN-27) in a national sample of 897 Irish primary school children aged 8-12 years. Participants had a mean age of 10.9 ± 1.16 years and were 47.7% female. Just over 30% of the sample accumulated 2 h or more of leisure screen time daily. Results show that there was no significant interaction between screen time category (<2 h/2 h + daily) and gender on overall wellbeing, while controlling for BMI. Children who self-reported <2 h of leisure screen time scored significantly higher on four dimensions of wellbeing: physical, parental, peers, and school, but not psychological. This study supports the growing evidence of the impact that leisure screen time has on health. Further longitudinal research investigating the impact of sub-categories of leisure screen time behaviour on wellbeing is warranted.

Introduction: Novel therapies for pulmonary hypertension (PH) have improved survival and slowed disease progression. However, patients still present with symptoms of exertional dyspnoea and fatigue, which impacts their ability to perform activities of daily living, reduces exercise tolerance and impairs their quality of life (QoL). Exercise training has shown to be safe and effective at enhancing QoL and physical function in PH patients, yet it remains an underused adjunct therapy. Most exercise training for PH patients has been offered through hospital-based programmes. Home-based exercise programmes provide an alternative model that has the potential to increase the availability and accessibility of exercise training as an adjunct therapy in PH. The purpose of this study is to investigate the feasibility, acceptability, utility and safety of a novel remotely supervised home-based PH exercise programme., Methods: Single arm intervention with a pre/post comparisons design and a follow-up maintenance phase will be employed. Eligible participants (n=25) will be recruited from the Mater Misericordiae University Hospital PH Unit. Participants will undergo a 10-week home-based exercise programme, with induction training, support materials, telecommunication support and health coaching sessions followed by a 10-week maintenance phase. The primary outcomes are feasibility, acceptability, utility and safety of the intervention. Secondary outcomes will include the impact of the intervention on exercise capacity, physical activity, strength, health-related QoL and exercise self-efficacy., Ethics and Dissemination: Ethics approval has been obtained from the Mater Misericordiae Institutional Review Board REF:1/378/2032 and Dublin City University Research Ethics DCUREC/2018/246. A manuscript of the results will be submitted to a peer-reviewed journal and results will be presented at conferences, community and consumer forums and hospital research conferences., Trial Registration Number: ISRCTN83783446; Pre-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: To explore how the efficacy of product trials for skin cosmetics can be improved through the use of consumer-level images taken by volunteers using a conventional smartphone., Materials and Methods: 12 women aged 30-60 years participated in a product trial and had close-up images of the cheek and temple regions of their faces taken with a high-resolution Antera 3D CS camera at the start and end of a 4-week period. Additionally, they each had "selfies" of the same regions of their faces taken regularly throughout the trial period. Automatic image analysis to identify changes in skin colour used three kinds of colour normalisation and analysis for wrinkle composition identified edges and calculated their magnitude., Results: Images taken at the start and end of the trial acted as baseline ground truth for normalisation of smartphone images and showed large changes in both colour and wrinkle magnitude during the trial for many volunteers., Conclusions: Results demonstrate that regular use of selfie smartphone images within trial periods can add value to interpretation of the efficacy of the trial., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Assistive technologies (ATs) aimed at improving the life quality of persons with Autism Spectrum Disorder and/or Intellectual Disability (ASD/ID) is an important research area. Few have examined how this population use and experience AT or their vision for future uses of AT. The present study aimed to update and extend previous research and provides insight from caregivers, and other stakeholders ( n  = 96), living in Ireland and the United Kingdom, on their experiences of assistive technology (AT) for ASD/ID. Caregiver and professional responses to an anonymous online survey showed that focus individuals were rated low in terms of independent and self-management skills, with scheduling and planning and communication identified as desirable future AT functions. Overall, positive experiences of AT were reported, with AT use more than doubling in recent years.

Background: Dementia is a neurodegenerative chronic condition characterized by a progressive decline in a person's memory, thinking, learning skills, and the ability to perform activities of daily living. Previous research has indicated that there are many types of technology interventions available in the literature that have shown promising results in improving disease progression, disease management, and the well-being of people with dementia (PwD) and their informal caregiver, thus facilitating dementia care and living. Technology-driven home care interventions, such as Connected Health (CH), could offer a convenient and low-cost alternative to traditional home care, providing an informal caregiver with the support they may need at home while caring for a PwD, improving their physical and mental well-being., Objective: This study aimed (1) to create a multidimensional profile for evaluating the well-being progression of the PwD-informal caregiver dyad for a year during their use of a CH platform, designed for monitoring PwD and supporting their informal caregivers at home, and (2) to conduct a long-term follow-up using the proposed well-being profile at different time-interval evaluations., Methods: The PwD-informal caregiver well-being profile was created based on the World Health Organization International Classification of Functioning considering the following outcomes: functional status, cognitive status, and quality of life for the PwD and mental well-being, sleeping quality, and burden for the informal caregiver. Over a year, comprehensive assessments of these outcomes were conducted every 3 months to evaluate the well-being of PwD-informal caregivers, using international and standardized validated questionnaires. Participants' demographic information was analyzed using descriptive statistics and presented as means and SDs. A nonparametric Friedman test was used to analyze the outcome changes and the progression in the PwD-caregiver dyads and to determine if those changes were statistically significant., Results: There were no significant changes in the well-being of PwD or their caregivers over the year of follow-up, with the majority of the PwD-caregiver dyads remaining stable. The only instances in which significant changes were observed were the functional status in the PwD and sleep quality in their caregivers. In each of these measures, post hoc pairwise comparisons did not indicate that the changes observed were related to the deployment of the CH platform., Conclusions: The follow-up of this population of PwD and their informal caregivers has shown that disease progression and physical and mental well-being do not change significantly during the time, being a slow and gradual process. The well-being profile created to analyze the potential impact of the CH platform on the PwD-informal caregiver dyad well-being, once validated, could be used as a future tool to conduct the same analyses with other CH technologies for this population., International Registered Report Identifier (irrid): RR2-10.2196/13280., (©Estefania Guisado-Fernandez, Catherine Blake, Laura Mackey, Paula Alexandra Silva, Dermot Power, Diarmuid O'Shea, Brian Caulfield. Originally published in JMIR Aging (http://aging.jmir.org), 23.07.2020.)

The imitation of natural systems to produce effective antifouling materials is often referred to as "biomimetics". The world of biomimetics is a multidisciplinary one, needing careful understanding of "biological structures", processes and principles of various organisms found in nature and based on this, designing nanodevices and nanomaterials that are of commercial interest to industry. Looking to the marine environment for bioinspired surfaces offers researchers a wealth of topographies to explore. Particular attention has been given to the evaluation of textures based on marine organisms tested in either the laboratory or the field. The findings of the review relate to the numbers of studies on textured surfaces demonstrating antifouling potential which are significant. However, many of these are only tested in the laboratory, where it is acknowledged a very different response to fouling is observed.

The aim of this study is to explore participants' views and experiences of an eHealth phase 3 cardiac rehabilitation (CR) intervention: Physical Activity Towards Health (PATHway). Sixty participants took part in the PATHway intervention. Debriefs were conducted after the six-month intervention. All interviews were audio recorded and transcribed verbatim. Transcripts were analysed with Braun and Clarke's thematic analysis. Forty-four (71%) debriefs were conducted (n = 34 male, mean (SD) age 61 (10) years). Five key themes were identified: (1) Feedback on the components of the PATHway system, (2) Motivation, (3) Barriers to using PATHway, (4) Enablers to using PATHway, and (5) Post programme reflection. There were a number of subthemes within each theme, for example motivation explores participants motivation to take part in PATHway and participants motivation to sustain engagement with PATHway throughout the intervention period. Participant engagement with the components of the PATHway system was variable. Future research should focus on optimising participant familiarisation with eHealth systems and employ an iterative approach to development and evaluation., Competing Interests: The authors have declared that no competing interests exist.