Publications by authors named "Rob Argent"

Objective: Given the well-accepted health benefits, it is important to identify scalable ways to support people with long-term conditions (LTCs) to remain physically active. This systematic review aimed to evaluate the effect of digital tools on the maintenance of physical activity (PA) amongst this population.

Methods: Electronic databases were searched for randomised controlled trials investigating the effect of digital tools on PA maintenance at least three months post-intervention compared with a non-digital control in participants with long-term conditions.

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Background: Consumer wearable technologies have become ubiquitous, with clinical and non-clinical populations leveraging a variety of devices to quantify various aspects of health and wellness. However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear.

Objective: To conduct a 'living' (i.

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Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple domains. Research is continuing to look for methods and tools to further revolutionise all aspects of health from prediction, diagnosis, treatment, and monitoring.

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Introduction: Vosoritide is the first approved pharmacological treatment for achondroplasia and is indicated for at-home injectable administration by a trained caregiver. This research aimed to explore parents' and children's experience of initiating vosoritide and administering this treatment at home.

Methods: Qualitative telephone interviews were conducted with parents of children being treated with vosoritide in France and Germany.

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Background: Brief interventions for lifestyle behaviour change are effective health promotion interventions. Primary care settings, including pharmacies, are the most frequently visited healthcare facilities and are well placed to provide brief health interventions. However, despite the evidence-based and policy guidance, barriers to brief interventions have limited their implementation.

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Introduction: A number of significant changes designed to reduce the spread of COVID-19 were introduced in primary care during the COVID-19 pandemic. In Ireland, these included fundamental legislative and practice changes such as permitting electronic transfer of prescriptions, extending duration of prescription validity, and encouraging virtual consultations. Although such interventions served an important role in preventing the spread of infection, their impact on practice and patient care is not yet clear.

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Advances in sensor technology have provided an opportunity to measure gait characteristics using body-worn inertial measurement units (IMUs). Whilst research investigating the validity of IMUs in reporting gait characteristics is extensive, research investigating the reliability of IMUs is limited. This study aimed to investigate the inter-session reliability of wireless IMU derived measures of gait (i.

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When using wearable sensors for measurement and analysis of human performance, it is often necessary to integrate and synchronise data from separate sensor systems. This paper describes a synchronization technique between IMUs attached to the shanks and insoles attached at the feet and aims to solve the need to compute the ankle joint angle, which relies on synchronized sensor data. This will additionally enable concurrent analysis using gait kinematic and kinetic features.

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Background: The number of mobile health (mHealth) apps released for musculoskeletal (MSK) injury treatment and self-management with home exercise programs (HEPs) has risen rapidly in recent years as digital health interventions are explored and researched in more detail. As this number grows, it is becoming increasingly difficult for users to navigate the market and select the most appropriate app for their use case. It is also unclear what features the developers of these apps are harnessing to support patient self-management and how they fit into clinical care pathways.

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Introduction: The COVID-19 pandemic has had a profound impact on the delivery of primary care around the world. In Ireland, the use of technologies such as virtual consultations and the electronic transfer of prescriptions became widespread in order to deliver care to patients while minimising infection risk. The impact of these changes on medication safety is not yet known.

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Background: Consumer wearables and smartphone devices commonly offer an estimate of energy expenditure (EE) to assist in the objective monitoring of physical activity to the general population. Alongside consumers, healthcare professionals and researchers are seeking to utilise these devices for the monitoring of training and improving human health. However, the methods of validation and reporting of EE estimation in these devices lacks rigour, negatively impacting on the ability to make comparisons between devices and provide transparent accuracy.

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Background: Technological advances have recently made possible the estimation of maximal oxygen consumption (VO) by consumer wearables. However, the validity of such estimations has not been systematically summarized using meta-analytic methods and there are no standards guiding the validation protocols.

Objective: The aim was to (1) quantitatively summarize previous studies investigating the validity of the VO estimated by consumer wearables and (2) provide best-practice recommendations for future validation studies.

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Background: The World Health Organisation's global strategy for digital health emphasises the importance of patient involvement. Understanding the usability and acceptability of wearable devices is a core component of this. However, usability assessments to date have focused predominantly on healthy adults.

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Machine learning models are being utilized to provide wearable sensor-based exercise biofeedback to patients undertaking physical therapy. However, most systems are validated at a technical level using lab-based cross validation approaches. These results do not necessarily reflect the performance levels that patients and clinicians can expect in the real-world environment.

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Article Synopsis
  • Segmenting physical movements is crucial for creating effective biofeedback systems in rehabilitation, focusing on detecting patterns in inertial data from exercises.
  • The paper proposes a new technique called ConvFSM, which utilizes Convolutional Neural Networks and Finite State Machines to identify movement patterns with minimal specialized knowledge.
  • The study also tests various sensor combinations to find the best setup for home-based rehabilitation systems, backed by experimental results from upper and lower limb exercise datasets.
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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.

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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.

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The majority of wearable sensor-based biofeedback systems used in exercise rehabilitation lack end-user evaluation as part of the development process. This study sought to evaluate an exemplar sensor-based biofeedback system, investigating the feasibility, usability, perceived impact and user experience of using the platform. Fifteen patients participated in the study having recently undergone knee replacement surgery.

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Objectives: This study explores the opinions of orthopaedic healthcare professionals regarding the opportunities and challenges of using wearable technology in rehabilitation. It continues to assess the perceived impact of an exemplar exercise biofeedback system that incorporates wearable sensing, involving the clinician in the user-centred design process, a valuable step in ensuring ease of implementation, sustained engagement and clinical relevance.

Design: This is a qualitative study consisting of one-to-one semi-structured interviews, including a demonstration of a prototype wearable exercise biofeedback system.

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Adherence to home exercise in rehabilitation is a significant problem, with estimates of nonadherence as high as 50%, potentially having a detrimental effect on clinical outcomes. In this viewpoint, we discuss the many reasons why patients may not adhere to a prescribed exercise program and explore how connected health technologies have the ability to offer numerous interventions to enhance adherence; however, it is hard to judge the efficacy of these interventions without a robust measurement tool. We highlight how well-designed connected health technologies, such as the use of mobile devices, including mobile phones and tablets, as well as inertial measurement units, provide us with the opportunity to better support the patient and clinician, with a data-driven approach that incorporates features designed to increase adherence to exercise such as coaching, self-monitoring and education, as well as remotely monitor adherence rates more objectively.

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