Cerina: cognitive-behavioural therapy-based mobile application for managing GAD symptoms among Ulster University Students in Northern Ireland - a protocol for a pilot feasibility randomised controlled trial.

INTRODUCTION: University students are one of the most vulnerable populations for anxiety disorders worldwide. In Northern Ireland, anxiety disorders appear to be more common among the university student population due to the population demographics across the region. Despite the need, these students show less inclination to access the widely available on-campus well-being services and other external professional services. Digital cognitive-behavioural therapy (CBT) aims to bridge this gap between the need for psychological help and access to it. However, challenges such as limited reach, low adoption, implementation barriers and poor long-term maintenance are mainstay issues resulting in reduced uptake of digital CBT. As a result, the potential impact of digital CBT is currently restricted. The proposed intervention 'Cerina' is a scalable CBT-based mobile app with an interactive user interface that can be implemented in university settings if found to be feasible and effective. METHODS AND ANALYSIS: The study is a single-blind pilot feasibility randomised controlled trial aiming to test the feasibility and preliminary effects of Cerina in reducing Generalised Anxiety Disorder (GAD) symptoms. Participants are 90 Ulster University students aged 18 and above with self-reported GAD symptoms. They will be allocated to two conditions: treatment (ie, access to Cerina for 6 weeks) and a wait-list control group (ie, optional on-campus well-being services for 6 weeks). Participants in the wait-list will access Cerina 6 weeks after their randomisation and participants in both conditions will be assessed at baseline, at 3 (mid-assessment) and 6 weeks (postassessment). The primary outcome is the feasibility of Cerina (ie, adherence to the intervention, its usability and the potential to deliver a full trial in the future). The secondary outcomes include generalised anxiety, depression, worry and quality of life. Additionally, participants in both conditions will be invited to semistructured interviews for process evaluation. ETHICS AND DISSEMINATION: Ethical approval for the study has been granted by the Ulster University Research Ethics Committee (ID: FCPSY-22-084). The results of the study will be disseminated through publications in scientific articles and presentations at relevant conferences and/or public events. TRIAL REGISTRATION NUMBER: NCT06146530.

Stakeholder perspectives on contributors to delayed and inaccurate diagnosis of cardiovascular disease and their implications for digital health technologies: a UK-based qualitative study.

OBJECTIVE: The aim of this study is to understand stakeholder experiences of diagnosis of cardiovascular disease (CVD) to support the development of technological solutions that meet current needs. Specifically, we aimed to identify challenges in the process of diagnosing CVD, to identify discrepancies between patient and clinician experiences of CVD diagnosis, and to identify the requirements of future health technology solutions intended to improve CVD diagnosis. DESIGN: Semistructured focus groups and one-to-one interviews to generate qualitative data that were subjected to thematic analysis. PARTICIPANTS: UK-based individuals (N=32) with lived experience of diagnosis of CVD (n=23) and clinicians with experience in diagnosing CVD (n=9). RESULTS: We identified four key themes related to delayed or inaccurate diagnosis of CVD: symptom interpretation, patient characteristics, patient-clinician interactions and systemic challenges. Subthemes from each are discussed in depth. Challenges related to time and communication were greatest for both stakeholder groups; however, there were differences in other areas, for example, patient experiences highlighted difficulties with the psychological aspects of diagnosis and interpreting ambiguous symptoms, while clinicians emphasised the role of individual patient differences and the lack of rapport in contributing to delays or inaccurate diagnosis. CONCLUSIONS: Our findings highlight key considerations when developing digital technologies that seek to improve the efficiency and accuracy of diagnosis of CVD.

An Investigation of Surface EMG Shorts-Derived Training Load during Treadmill Running.

The purpose of this study was two-fold: (1) to determine the sensitivity of the sEMG shorts-derived training load (sEMG-TL) during different running speeds; and (2) to investigate the relationship between the oxygen consumption, heart rate (HR), rating of perceived exertion (RPE), accelerometry-based PlayerLoadTM (PL), and sEMG-TL during a running maximum oxygen uptake (V˙O2max) test. The study investigated ten healthy participants. On day one, participants performed a three-speed treadmill test at 8, 10, and 12 km·h-1 for 2 min at each speed. On day two, participants performed a V˙O2max test. Analysis of variance found significant differences in sEMG-TL at all three speeds (p < 0.05). A significantly weak positive relationship between sEMG-TL and %V˙O2max (r = 0.31, p < 0.05) was established, while significantly strong relationships for 8 out of 10 participants at the individual level (r = 0.72-0.97, p < 0.05) were found. Meanwhile, the accelerometry PL was not significantly related to %V˙O2max (p > 0.05) and only demonstrated significant correlations in 3 out of 10 participants at the individual level. Therefore, the sEMG shorts-derived training load was sensitive in detecting a work rate difference of at least 2 km·h-1. sEMG-TL may be an acceptable metric for the measurement of internal loads and could potentially be used as a surrogate for oxygen consumption.

Stakeholder-led understanding of the implementation of digital technologies within heart disease diagnosis: a qualitative study protocol.

INTRODUCTION: Cardiovascular diseases are highly prevalent among the UK population, and the quality of care is being reduced due to accessibility and resource issues. Increased implementation of digital technologies into the cardiovascular care pathway has enormous potential to lighten the load on the National Health Service (NHS), however, it is not possible to adopt this shift without embedding the perspectives of service users and clinicians. METHODS AND ANALYSIS: A series of qualitative studies will be carried out with the aim of developing a stakeholder-led perspective on the implementation of digital technologies to improve holistic diagnosis of heart disease. This will be a decentralised study with all data collection being carried out online with a nationwide cohort. Four focus groups, each with 5-6 participants, will be carried out with people with lived experience of heart disease, and 10 one-to-one interviews will be carried out with clinicians with experience of diagnosing heart diseases. The data will be analysed using an inductive thematic analysis approach. ETHICS AND DISSEMINATION: This study received ethical approval from the Sciences and Technology Cross Research Council at the University of Sussex (reference ER/FM409/1). Participants will be required to provide informed consent via a Qualtrics survey before being accepted into the online interview or focus group. The findings will be disseminated through conference presentations, peer-reviewed publications and to the study participants.

Implementation of virtual reality in the rehabilitation of patients suffering from axial spondyloarthritis.

Introduction (including aim): Axial spondyloarthritis (axSpA) is a chronic inflammatory and rheumatic disease that causes inflammation and structural changes to the skeleton. Patients with axSpA suffer from neck pain and stiffness and have severe and permanent restrictions in movement. Patients are advised to carry out prescribed exercises to maintain mobility, but most do not comply with this advice due to the unnatural nature of head and neck stretching exercises. Clinicians currently only test cervical rotation of patients with axSpA a few times per year. Pain and stiffness can fluctuate between appointments, and there is a need to accurately measure the patient's spinal mobility at home. METHODS: Virtual Reality (VR) headsets have been proven to be accurate and reliable when measuring neck movement. We are using VR to aid relaxation and promote mindfulness, whilst moving the participant's head to visual and auditory cues to enable completion of exercises. In this ongoing study, we are testing whether a smartphone-enabled VR system could be feasible for the measurement of cervical movement at home. RESULTS: The ongoing research will have a positive impact on the lives of patients suffering from axSpA. Regular measurement and assessment of spinal mobility at home will be beneficial to patients and clinicians for objective mobility measurement. DISCUSSION: Implementing VR as both a distractive and rehabilitation encouragement technique could improve patient engagement whilst simultaneously collecting granular mobility data. Additionally, implementing VR rehabilitation using smartphone technology will offer an inexpensive method of exercise and effective rehabilitation.

Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices.

RATIONALE: Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders. AIMS: This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system. METHODOLOGY: a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018-2022, focusing primarily on mental health management using technology. RESULTS: We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study.

BACKGROUND: The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. OBJECTIVE: This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. METHODS: Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants' intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. RESULTS: The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants' age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. CONCLUSIONS: This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.

Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

The Cardiorespiratory Demands of Treadmill Walking with and without the Use of Ekso GT™ within Able-Bodied Participants: A Feasibility Study.

Individuals with neurological impairments tend to lead a predominantly sedentary lifestyle due to impaired gait function and mobility. This may be detrimental to health by negatively impacting cardiorespiratory fitness and muscular strength, and increasing the risk of developing secondary health problems. Powered exoskeletons are assistive devices that may aid neurologically impaired individuals in achieving the World Health Organisation's (WHO) physical activity (PA) guidelines for health. Increased PA should elicit a sufficient cardiorespiratory stimulus to provide health benefits to exoskeleton users. This study examined the cardiorespiratory demands of treadmill walking with and without the Ekso GT™ among able-bodied participants. The Ekso GT™ is a powered exoskeleton that enables individuals with neurological impairments to walk by supporting full body mass with motors attached at the hip and knee joints to generate steps. This feasibility study consisted of one group of healthy able-bodied individuals (n = 8). Participants completed two 12 min treadmill walking assessments, one with and one without the Ekso GT™ at the same fixed speed. Throughout each walking bout, various cardiorespiratory parameters, namely, volume of oxygen per kilogram (kg) of body mass (V˙O2·kg-1), volume of carbon dioxide per kg of body mass (V˙CO2·kg-1), respiratory exchange ratio (RER), ventilation (V˙E), heart rate (HR), and rate of perceived exertion (RPE), were recorded. Treadmill walking with Ekso GT™ elevated all recorded measurements to a significantly greater level (p ≤ 0.05) (except RER at 1 km·h-1; p = 0.230) than treadmill walking without the Ekso GT™ did at the same fixed speed. An increased cardiorespiratory response was recorded during treadmill walking with the exoskeleton. Exoskeleton walking may, therefore, be an effective method to increase PA levels and provide sufficient stimulus in accordance with the PA guidelines to promote cardiorespiratory fitness and subsequently enhance overall health.

Improving Data Glove Accuracy and Usability Using a Neural Network When Measuring Finger Joint Range of Motion.

Data gloves capable of measuring finger joint kinematics can provide objective range of motion information useful for clinical hand assessment and rehabilitation. Data glove sensors are strategically placed over specific finger joints to detect movement of the wearers' hand. The construction of the sensors used in a data glove, the number of sensors used, and their positioning on each finger joint are influenced by the intended use case. Although most glove sensors provide reasonably stable linear output, this stability is influenced externally by the physical structure of the data glove sensors, as well as the wearer's hand size relative to the data glove, and the elastic nature of materials used in its construction. Data gloves typically require a complex calibration method before use. Calibration may not be possible when wearers have disabled hands or limited joint flexibility, and so limits those who can use a data glove within a clinical context. This paper examines and describes a unique approach to calibration and angular calculation using a neural network that improves data glove repeatability and accuracy measurements without the requirement for data glove calibration. Results demonstrate an overall improvement in data glove measurements. This is particularly relevant when the data glove is used with those who have limited joint mobility and cannot physically complete data glove calibration.

The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration.

BACKGROUND: Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. OBJECTIVE: This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. METHODS: This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. RESULTS: Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. CONCLUSIONS: Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease-related health barriers and the users' confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults.

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the "Healthy Ageing Initiative" study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults.

Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an Area Under the ROC Curve (AUC-ROC) of 0.882, a feature subset which only includes demographics, self-report health and lifestyle data, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and suggests its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults.

Review of Wearable Devices and Data Collection Considerations for Connected Health.

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer's physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient's functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

State-of-the-Art Sensors for Remote Care of People with Dementia during a Pandemic: A Systematic Review.

In the last decade, there has been a significant increase in the number of people diagnosed with dementia. With diminishing public health and social care resources, there is substantial need for assistive technology-based devices that support independent living. However, existing devices may not fully meet these needs due to fears and uncertainties about their use, educational support, and finances. Further challenges have been created by COVID-19 and the need for improved safety and security. We have performed a systematic review by exploring several databases describing assistive technologies for dementia and identifying relevant publications for this review. We found there is significant need for appropriate user testing of such devices and have highlighted certifying bodies for this purpose. Given the safety measures imposed by the COVID-19 pandemic, this review identifies the benefits and challenges of existing assistive technologies for people living with dementia and their caregivers. It also provides suggestions for future research in these areas.

Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings.

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings.

Older Adults' Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis.

BACKGROUND: Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. OBJECTIVE: This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. METHODS: We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a "line-of-argument" was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. RESULTS: In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user's life. The user's needs and the support structure around the device-aspects that are often overlooked-seem to play a crucial role in long-term adoption. Our "line-of-argument" model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user's life, which subsequently predicts whether the device will be integrated into the user's life. CONCLUSIONS: The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user's preferences.

Measuring Spinal Mobility Using an Inertial Measurement Unit System: A Reliability Study in Axial Spondyloarthritis.

The objectives of this study were to evaluate the reliability of wearable inertial motion unit (IMU) sensors in measuring spinal range of motion under supervised and unsupervised conditions in both laboratory and ambulatory settings. A secondary aim of the study was to evaluate the reliability of composite IMU metrology scores (IMU-ASMI (Amb)). Forty people with axSpA participated in this clinical measurement study. Participant spinal mobility was assessed by conventional metrology (Bath Ankylosing Spondylitis Metrology Index, linear version-BASMILin) and by a wireless IMU sensor-based system which measured lumbar flexion-extension, lateral flexion and rotation. Each sensor-based movement test was converted to a normalized index and used to calculate IMU-ASMI (Amb) scores. Test-retest reliability was evaluated using intra-class correlation coefficients (ICC). There was good to excellent agreement for all spinal range of movements (ICC > 0.85) and IMU-ASMI (Amb) scores (ICC > 0.87) across all conditions. Correlations between IMU-ASMI (Amb) scores and conventional metrology were strong (Pearson correlation ≥ 0.85). An IMU sensor-based system is a reliable way of measuring spinal lumbar mobility in axSpA under supervised and unsupervised conditions. While not a replacement for established clinical measures, composite IMU-ASMI (Amb) scores may be reliably used as a proxy measure of spinal mobility.

Trailgazers: A Scoping Study of Footfall Sensors to Aid Tourist Trail Management in Ireland and Other Atlantic Areas of Europe.

This paper examines the current state of the art of commercially available outdoor footfall sensor technologies and defines individually tailored solutions for the walking trails involved in an ongoing research project. Effective implementation of footfall sensors can facilitate quantitative analysis of user patterns, inform maintenance schedules and assist in achieving management objectives, such as identifying future user trends like cyclo-tourism. This paper is informed by primary research conducted for the EU funded project TrailGazersBid (hereafter referred to as TrailGazers), led by Donegal County Council, and has Sligo County Council and Causeway Coast and Glens Council (NI) among the 10 project partners. The project involves three trails in Ireland and five other trails from Europe for comparison. It incorporates the footfall capture and management experiences of trail management within the EU Atlantic area and desk-based research on current footfall technologies and data capture strategies. We have examined 6 individual types of sensor and discuss the advantages and disadvantages of each. We provide key learnings and insights that can help to inform trail managers on sensor options, along with a decision-making tool based on the key factors of the power source and mounting method. The research findings can also be applied to other outdoor footfall monitoring scenarios.

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion.

Capturi ng hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person's finger joint. Due to the modular design, the ECSMS's sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.