Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk.

BACKGROUND: Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers. METHODS: We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer. RESULTS: We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. CONCLUSIONS: Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

Age-associated B cells predict impaired humoral immunity after COVID-19 vaccination in patients receiving immune checkpoint blockade.

Age-associated B cells (ABC) accumulate with age and in individuals with different immunological disorders, including cancer patients treated with immune checkpoint blockade and those with inborn errors of immunity. Here, we investigate whether ABCs from different conditions are similar and how they impact the longitudinal level of the COVID-19 vaccine response. Single-cell RNA sequencing indicates that ABCs with distinct aetiologies have common transcriptional profiles and can be categorised according to their expression of immune genes, such as the autoimmune regulator (AIRE). Furthermore, higher baseline ABC frequency correlates with decreased levels of antigen-specific memory B cells and reduced neutralising capacity against SARS-CoV-2. ABCs express high levels of the inhibitory FcγRIIB receptor and are distinctive in their ability to bind immune complexes, which could contribute to diminish vaccine responses either directly, or indirectly via enhanced clearance of immune complexed-antigen. Expansion of ABCs may, therefore, serve as a biomarker identifying individuals at risk of suboptimal responses to vaccination.

Accelerated waning of the humoral response to COVID-19 vaccines in obesity.

Obesity is associated with an increased risk of severe Coronavirus Disease 2019 (COVID-19) infection and mortality. COVID-19 vaccines reduce the risk of serious COVID-19 outcomes; however, their effectiveness in people with obesity is incompletely understood. We studied the relationship among body mass index (BMI), hospitalization and mortality due to COVID-19 among 3.6 million people in Scotland using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) surveillance platform. We found that vaccinated individuals with severe obesity (BMI > 40 kg/m2) were 76% more likely to experience hospitalization or death from COVID-19 (adjusted rate ratio of 1.76 (95% confidence interval (CI), 1.60-1.94). We also conducted a prospective longitudinal study of a cohort of 28 individuals with severe obesity compared to 41 control individuals with normal BMI (BMI 18.5-24.9 kg/m2). We found that 55% of individuals with severe obesity had unquantifiable titers of neutralizing antibody against authentic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus compared to 12% of individuals with normal BMI (P = 0.0003) 6 months after their second vaccine dose. Furthermore, we observed that, for individuals with severe obesity, at any given anti-spike and anti-receptor-binding domain (RBD) antibody level, neutralizing capacity was lower than that of individuals with a normal BMI. Neutralizing capacity was restored by a third dose of vaccine but again declined more rapidly in people with severe obesity. We demonstrate that waning of COVID-19 vaccine-induced humoral immunity is accelerated in individuals with severe obesity. As obesity is associated with increased hospitalization and mortality from breakthrough infections, our findings have implications for vaccine prioritization policies.

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.

The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.

DNA Repair Biomarker for Lung Cancer Risk and its Correlation With Airway Cells Gene Expression.

BACKGROUND: Improving lung cancer risk assessment is required because current early-detection screening criteria miss most cases. We therefore examined the utility for lung cancer risk assessment of a DNA Repair score obtained from OGG1, MPG, and APE1 blood tests. In addition, we examined the relationship between the level of DNA repair and global gene expression. METHODS: We conducted a blinded case-control study with 150 non-small cell lung cancer case patients and 143 control individuals. DNA Repair activity was measured in peripheral blood mononuclear cells, and the transcriptome of nasal and bronchial cells was determined by RNA sequencing. A combined DNA Repair score was formed using logistic regression, and its correlation with disease was assessed using cross-validation; correlation of expression to DNA Repair was analyzed using Gene Ontology enrichment. RESULTS: DNA Repair score was lower in case patients than in control individuals, regardless of the case's disease stage. Individuals at the lowest tertile of DNA Repair score had an increased risk of lung cancer compared to individuals at the highest tertile, with an odds ratio (OR) of 7.2 (95% confidence interval [CI] = 3.0 to 17.5; P < .001), and independent of smoking. Receiver operating characteristic analysis yielded an area under the curve of 0.89 (95% CI = 0.82 to 0.93). Remarkably, low DNA Repair score correlated with a broad upregulation of gene expression of immune pathways in patients but not in control individuals. CONCLUSIONS: The DNA Repair score, previously shown to be a lung cancer risk factor in the Israeli population, was validated in this independent study as a mechanism-based cancer risk biomarker and can substantially improve current lung cancer risk prediction, assisting prevention and early detection by computed tomography scanning.

Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor.

INTRODUCTION: Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. METHODS: Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. RESULTS: Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). CONCLUSIONS: Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

Reliability, Validity and Utility of Inertial Sensor Systems for Postural Control Assessment in Sport Science and Medicine Applications: A Systematic Review.

BACKGROUND: Recent advances in mobile sensing and computing technology have provided a means to objectively and unobtrusively quantify postural control. This has resulted in the rapid development and evaluation of a series of wearable inertial sensor-based assessments. However, the validity, reliability and clinical utility of such systems is not fully understood. OBJECTIVES: This systematic review aims to synthesise and evaluate studies that have investigated the ability of wearable inertial sensor systems to validly and reliably quantify postural control performance in sports science and medicine applications. METHODS: A systematic search strategy utilising the PRISMA guidelines was employed to identify eligible articles through ScienceDirect, Embase and PubMed databases. In total, 47 articles met the inclusion criteria and were evaluated and qualitatively synthesised under two main headings: measurement validity and measurement reliability. Furthermore, studies that investigated the utility of these systems in clinical populations were summarised and discussed. RESULTS: After duplicate removal, 4374 articles were identified with the search strategy, with 47 papers included in the final review. In total, 28 studies investigated validity in healthy populations, and 15 studies investigated validity in clinical populations; 13 investigated the measurement reliability of these sensor-based systems. CONCLUSIONS: The application of wearable inertial sensors for sports science and medicine postural control applications is an evolving field. To date, research has primarily focused on evaluating the validity and reliability of a heterogeneous set of assessment protocols, in a laboratory environment. While researchers have begun to investigate their utility in clinical use cases such as concussion and musculoskeletal injury, most studies have leveraged small sample sizes, are of low quality and use a variety of descriptive variables, assessment protocols and sensor-mounting locations. Future research should evaluate the clinical utility of these systems in large high-quality prospective cohort studies to establish the role they may play in injury risk identification, diagnosis and management. This systematic review was registered with the International Prospective Register of Systematic Reviews on 10 August 2018 (PROSPERO registration: CRD42018106363): https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=106363 .

Determining Interrater and Intrarater Levels of Agreement in Students and Clinicians When Visually Evaluating Movement Proficiency During Screening Assessments.

BACKGROUND: Biomechanical screening assessments are used to provide useful information about an athlete's movement proficiency. Clinically, movement proficiency is typically evaluated visually. This can result in low levels of agreement, leading to difficulties in ensuring consistent athlete assessment. OBJECTIVE: The objective was to determine levels of agreement within and between physical therapists and physical therapist students when visually evaluating athletes' movement proficiency during biomechanical screening assessments. DESIGN: This was an observational study. METHODS: Twenty-seven physical therapists and 20 physical therapist students assessed 100 video recordings of athletes performing 4 lower-extremity biomechanical screening assessments: squat, lunge, single leg squat, and deadlift. Analysis was completed on conditioned and unconditioned data. In the conditioned data, technique deviations were induced purposefully by the athletes. In the unconditioned data, deviations occurred naturally due to increased weight or movement complexity. In order to determine levels of agreement in the assessments, participants were required to classify the athletes' movement as acceptable or aberrant. Each participant assessed the same video recordings on 2 separate occasions at least 30 days apart. Agreement levels were determined using Cohen κ and Fleiss κ. RESULTS: Kappa scores at an interrater level ranged from 0.18 to 0.53, and intrarater agreement ranged from 0.38 to 0.62. Levels of agreement were higher in the conditioned data compared with the unconditioned data. Overall, the lunge and squat produced higher levels of agreement than the deadlift and single-leg squat. Students and physical therapists demonstrated similar levels of agreement. LIMITATIONS: Screening assessments were evaluated through the use of video analysis. CONCLUSIONS: Greater efforts are needed to ensure standardization of movement analysis.

Capturing concussion related changes in dynamic balance using the Quantified Y Balance Test - a case series of six elite rugby union players.

Concussion is one of the most common injuries reported across a myriad of sports. Recent evidence suggests that individuals may possess sensorimotor deficits beyond clinical recovery, predisposing them to further injury. This preliminary prospective case series aimed to determine if an inertial sensor instrumented Y balance test can capture changes in dynamic balance, regardless of apparent `clinical recovery', in six concussed elite rugby union players. The findings from this case series demonstrate that the inertial sensor-based measures can detect clinically meaningful changes in dynamic balance performance, not captured by the traditional clinical scoring methods, 48-hours post-injury and at the point of `clinical recovery' (return to play). Further research should investigate the role such instrumented dynamic balance assessments may play in the management of sports-related concussion.

Association of Dynamic Balance With Sports-Related Concussion: A Prospective Cohort Study.

BACKGROUND: Concussion is one of the most common sports-related injuries, with little understood about the modifiable and nonmodifiable risk factors. Researchers have yet to evaluate the association between modifiable sensorimotor function variables and concussive injury. PURPOSE: To investigate the association between dynamic balance performance, a discrete measure of sensorimotor function, and concussive injuries. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 3. METHODS: A total of 109 elite male rugby union players were baseline tested in dynamic balance performance while wearing an inertial sensor and prospectively followed during the 2016-2017 rugby union season. The sample entropy of the inertial sensor gyroscope magnitude signal was derived to provide a discrete measure of dynamic balance performance. Logistic regression modeling was then used to investigate the association among the novel digital biomarker of balance performance, known risk factors of concussion (concussion history, age, and playing position), and subsequent concussive injury. RESULTS: Participant demographic data (mean ± SD) were as follows: age, 22.6 ± 3.6 years; height, 185 ± 6.5 cm; weight, 98.9 ± 12.5 kg; body mass index, 28.9 ± 2.9 kg/m2; and leg length, 98.8 ± 5.5 cm. Of the 109 players, 44 (40.3%) had a history of concussion, while 21 (19.3%) sustained a concussion during the follow-up period. The receiver operating characteristic analysis for the anterior sample entropy demonstrated a statistically significant area under the curve (0.64; 95% CI, 0.52-0.76; P < .05), with the cutoff score of anterior sample entropy ≥1.2, which maximized the sensitivity (76.2%) and specificity (53.4%) for identifying individuals who subsequently sustained a concussion. Players with suboptimal balance performance at baseline were at a 2.81-greater odds (95% CI, 1.02-7.74) of sustaining a concussion during the rugby union season than were those with optimal balance performance, even when controlling for concussion history. CONCLUSION: Rugby union players who possess poorer dynamic balance performance, as measured by a wearable inertial sensor during the Y balance test, have a 3-times-higher relative risk of sustaining a sports-related concussion, even when controlling for history of concussion. These findings have important implications for research and clinical practice, as it identifies a potential modifiable risk factor. Further research is required to investigate this association in a large cohort consisting of males and females across a range of sports.

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review.

BACKGROUND: Analysis of lower limb exercises is traditionally completed with four distinct methods: (1) 3D motion capture; (2) depth-camera-based systems; (3) visual analysis from a qualified exercise professional; and (4) self-assessment. Each method is associated with a number of limitations. OBJECTIVE: The aim of this systematic review is to synthesise and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. DATA SOURCES: A systematic review of studies identified through the databases of PubMed, ScienceDirect and Scopus was conducted. STUDY ELIGIBILITY CRITERIA: Articles written in English and published in the last 10 years which investigated an IMU system for the analysis of repetition-based targeted lower limb exercises were included. STUDY APPRAISAL AND SYNTHESIS METHODS: The quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised into three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. RESULTS: From the 2452 articles that were identified with the search strategies, 47 papers are included in this review. Twenty-six of the 47 included studies were deemed as being of high quality. CONCLUSIONS: Wearable inertial sensor systems for analysing lower limb exercises is a rapidly growing field of research. Research over the past 10 years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date.

A Wearable Sensor-Based Exercise Biofeedback System: Mixed Methods Evaluation of Formulift.

BACKGROUND: Formulift is a newly developed mobile health (mHealth) app that connects to a single inertial measurement unit (IMU) worn on the left thigh. The IMU captures users' movements as they exercise, and the app analyzes the data to count repetitions in real time and classify users' exercise technique. The app also offers feedback and guidance to users on exercising safely and effectively. OBJECTIVE: The aim of this study was to assess the Formulift system with three different and realistic types of potential users (beginner gym-goers, experienced gym-goers, and qualified strength and conditioning [S&C] coaches) under a number of categories: (1) usability, (2) functionality, (3) the perceived impact of the system, and (4) the subjective quality of the system. It was also desired to discover suggestions for future improvements to the system. METHODS: A total of 15 healthy volunteers participated (12 males; 3 females; age: 23.8 years [SD 1.80]; height: 1.79 m [SD 0.07], body mass: 78.4 kg [SD 9.6]). Five participants were beginner gym-goers, 5 were experienced gym-goers, and 5 were qualified and practicing S&C coaches. IMU data were first collected from each participant to create individualized exercise classifiers for them. They then completed a number of nonexercise-related tasks with the app. Following this, a workout was completed using the system, involving squats, deadlifts, lunges, and single-leg squats. Participants were then interviewed about their user experience and completed the System Usability Scale (SUS) and the user version of the Mobile Application Rating Scale (uMARS). Thematic analysis was completed on all interview transcripts, and survey results were analyzed. RESULTS: Qualitative and quantitative analysis found the system has "good" to "excellent" usability. The system achieved a mean (SD) SUS usability score of 79.2 (8.8). Functionality was also deemed to be good, with many users reporting positively on the systems repetition counting, technique classification, and feedback. A number of bugs were found, and other suggested changes to the system were also made. The overall subjective quality of the app was good, with a median star rating of 4 out of 5 (interquartile range, IQR: 3-5). Participants also reported that the system would aid their technique, provide motivation, reassure them, and help them avoid injury. CONCLUSIONS: This study demonstrated an overall positive evaluation of Formulift in the categories of usability, functionality, perceived impact, and subjective quality. Users also suggested a number of changes for future iterations of the system. These findings are the first of their kind and show great promise for wearable sensor-based exercise biofeedback systems.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

BACKGROUND: Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. OBJECTIVE: The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. METHODS: We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. RESULTS: With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. CONCLUSIONS: The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds.

Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation.

BACKGROUND: Biofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers. OBJECTIVE: We aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation. METHODS: A tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated. RESULTS: The tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh. CONCLUSIONS: A tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The personalized classification model displayed excellent system accuracy even when assessing acute deviations in compound exercises with a single IMU.

Technology in Rehabilitation: Comparing Personalised and Global Classification Methodologies in Evaluating the Squat Exercise with Wearable IMUs.

BACKGROUND: The barbell squat is a popularly used lower limb rehabilitation exercise. It is also an integral exercise in injury risk screening protocols. To date athlete/patient technique has been assessed using expensive laboratory equipment or subjective clinical judgement; both of which are not without shortcomings. Inertial measurement units (IMUs) may offer a low cost solution for the objective evaluation of athlete/patient technique. However, it is not yet known if global classification techniques are effective in identifying naturally occurring, minor deviations in barbell squat technique. OBJECTIVES: The aims of this study were to: (a) determine if in combination or in isolation, IMUs positioned on the lumbar spine, thigh and shank are capable of distinguishing between acceptable and aberrant barbell squat technique; (b) determine the capabilities of an IMU system at identifying specific natural deviations from acceptable barbell squat technique; and (c) compare a personalised (N=1) classifier to a global classifier in identifying the above. METHODS: Fifty-five healthy volunteers (37 males, 18 females, age = 24.21 +/- 5.25 years, height = 1.75 +/- 0.1 m, body mass = 75.09 +/- 13.56 kg) participated in the study. All participants performed a barbell squat 3-repetition maximum max strength test. IMUs were positioned on participants' lumbar spine, both shanks and both thighs; these were utilized to record tri-axial accelerometer, gyroscope and magnetometer data during all repetitions of the barbell squat exercise. Technique was assessed and labelled by a Chartered Physiotherapist using an evaluation framework. Features were extracted from the labelled IMU data. These features were used to train and evaluate both global and personalised random forests classifiers. RESULTS: Global classification techniques produced poor accuracy (AC), sensitivity (SE) and specificity (SP) scores in binary classification even with a 5 IMU set-up in both binary (AC: 64%, SE: 70%, SP: 28%) and multi-class classification (AC: 59%, SE: 24%, SP: 84%). However, utilising personalised classification techniques even with a single IMU positioned on the left thigh produced good binary classification scores (AC: 81%, SE: 81%, SP: 84%) and moderate-to-good multi-class scores (AC: 69%, SE: 70%, SP: 89%). CONCLUSIONS: There are a number of challenges in developing global classification exercise technique evaluation systems for rehabilitation exercises such as the barbell squat. Building large, balanced data sets to train such systems is difficult and time intensive. Minor, naturally occurring deviations may not be detected utilising global classification approaches. Personalised classification approaches allow for higher accuracy and greater system efficiency for end-users in detecting naturally occurring barbell squat technique deviations. Applying this approach also allows for a single-IMU set up to achieve similar accuracy to a multi-IMU setup, which reduces total system cost and maximises system usability.

Out-of-hospital cardiac arrests in the older population in Ireland.

INTRODUCTION: Age influences survival from an out-of-hospital cardiac arrest (OHCA) but it is unclear to what extent. Improved understanding of the impact of increasing age may be helpful in improving decision making on who should receive attempted resuscitation to optimise outcomes and minimise inappropriate end-of-life management. Our aim is to describe the demographics, characteristics and outcomes following resuscitation attempts in OHCA patients aged 70 years and older in Ireland. METHODS: Data were extracted from the national OHCA Register. Patient and event characteristics were compared across three age categories (70-79; 80-89; ≥90 years). Multivariable logistic regression was used to determine the predictors of the primary outcome (survival to hospital discharge). RESULTS: A total of 2281 patients aged 70 years and older were attended by emergency medical services and had resuscitation attempted between 2012 and 2014. Overall survival to hospital discharge was 2.9%. For those aged 70-79 years, 80-89 years, 90 years and older survival to hospital discharge in each age group was 4.0%, 1.8% and 1.4%, respectively. Older age (adjusted OR (AOR) 0.95 95% CI 0.90 to 0.99) and having an arrest in the subjects own home (AOR 0.14 95% CI 0.07 to 0.28) were independent predictor associated with reduced odds of survival to hospital discharge. An initial shockable rhythm (AOR 17.9. 95% CI 8.19 to 39.2) and having a bystander witnessed OHCA (AOR 3.98. 95% CI 1.38 to 11.50) were independent predictors associated with increased odds of survival to hospital discharge. CONCLUSION: In those aged 70 years and older, the rate of survival to hospital discharge declined with increasing age group. Younger age, an initial shockable rhythm and witnessed arrest were independent predictors of survival to hospital discharge.

Classification of lunge biomechanics with multiple and individual inertial measurement units.

Lunges are a common, compound lower limb resistance exercise. If completed with aberrant technique, the increased stress on the joints used may increase risk of injury. This study sought to first investigate the ability of inertial measurement units (IMUs), when used in isolation and combination, to (a) classify acceptable and aberrant lunge technique (b) classify exact deviations in lunge technique. We then sought to investigate the most important features and establish the minimum number of top-ranked features and decision trees that are needed to maintain maximal system classification efficacy. Eighty volunteers performed the lunge with acceptable form and 11 deviations. Five IMUs positioned on the lumbar spine, thighs, and shanks recorded these movements. Time and frequency domain features were extracted from the IMU data and used to train and test a variety of classifiers. A single-IMU system achieved 83% accuracy, 62% sensitivity, and 90% specificity in binary classification and a five-IMU system achieved 90% accuracy, 80% sensitivity, and 92% specificity. A five-IMU set-up can also detect specific deviations with 70% accuracy. System efficiency was improved and classification quality was maintained when using only 20% of the top-ranked features for training and testing classifiers.

Classification of deadlift biomechanics with wearable inertial measurement units.

The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes.