View clinical trials related to Wearables.
Filter by:Background: Measuring what people eat is a challenge in nutrition research. Traditional methods, like food diaries, rely on self-reporting of individuals, and suffer from poor accuracy and recall bias. Aims: This project aims to identify physiological biomarkers related to food and energy intake, which may be used to develop an objective tool to estimate individuals' food intake in future. Eating behaviours are accompanied by significant physiological changes such as skin temperature, blood oxygen saturation, pulse rate etc. The investigators intend to investigate whether monitoring these physiological changes can help us estimate eating behaviour, such as meal size, eating speed, and duration of meals. Study design: Ten healthy adults will be invited for two study visits at NIHR Imperial Clinical Research Facility. Each visit will last for approximately 2 hr. They will consume a high- and low-calorie meal designed by nutritional researchers in a randomised order. During eating events, the investigators will track their physiological changes via a bedside monitor and wearable sensors. Blood samples will be taken from participants to measure their glycaemic response. Associations between energy load, glycaemic response, and physiological changes will be investigated. Our findings may promote an accelerated development of a wearable tool for dietary assessment in future.
Mental health disorders are one of the most challenging chronic conditions to identify, treat and manage. This is largely due to the fact that diagnoses are almost entirely based on the patient's recall of current and past subjective experiences of symptoms; and then further interpreted by a healthcare professional introducing multiple layers of information biases in the formulation of a diagnosis. Accordingly, mental health conditions remain prevalent with high rates of misdiagnosis, inappropriate treatment and delayed intervention. In light of the heterogeneity across and within mental health conditions, a personalized interventional approach holds merit, yet the tools to effectively track an individual's day to day objective and subjective experience needed to achieve an individualized care approach have not until recently existed. Digital technologies such as passive and active sensing from smartphones and from wearable devices are shedding light on the capabilities of tracking new objective measures of health that could translate to key symptoms of mental health conditions. 'Multimodal data' approaches are those that attempt to translate a variety of electrical signals from digital devices to relevant health outcomes. The combination of digital devices to detect multimodal measures of mental health symptoms offers a unique opportunity to take a ground up approach in understanding the fluidity of mental health symptoms occurring at the individual level that might lend insight into new phenotypes of mental health illnesses that could have a physiological underpinning. The Study Investigators aim to characterize the multiplexing and fluid nature of mental health symptoms across individuals experiencing mental health symptoms and conditions using digital tools (i.e., wearables and mobile apps) and additional context information collected from virtual study support calls. The Investigators hope to know how objective measures from sensor data translate to core symptoms, episodes and flares across mental health disorders, and develop new (or new applications of) machine learning anomaly detection approaches and determine whether anomalies in expected symptom portraits can be reliably detected and enhanced by the addition of objectively measured data.
Pregnancy is a commonly occurring medical event. Women who are pregnant may experience pregnancy-related symptoms and complications. However, there is a relative lack of multi-dimensional data on large populations of pregnant patients. The Study Investigators aim to derive novel insights and deeper understanding of maternal physiology and pathology through the analysis of an unprecedented breadth and depth of data collected from connected devices (i.e., wearables, smart home scale, mobile apps, etc.), additional virtual study assessments and support calls, and information derived from standard of care clinical visits. They will share these insights to empower patients to better care for themselves. The Investigators hope to know how leveraging the data collected from connected devices in addition to information obtained from routine clinical care helps researchers and clinicians better understand pregnancy related symptoms, conditions, and complications.
The novel coronavirus (COVID-19) pandemic has caused an unprecedented stress on healthcare systems in affected countries, and in particular, on the healthcare workers at the frontline working directly with COVID-19 positive patients. Numerous lines of evidence support the damaging impact of stress on our immune systems which increases susceptibility to infection. Yet, the accurate measurement of immediate stress responses in real time and in naturalistic settings has so far been a challenge, limiting our understanding of how different facets of acute or sustained stress increases susceptibility. This study utilizes wearable technologies including an Oura smart ring as well as semi-continuous passive and active biometric measurements carried out using individuals' own smartphones equipped with applications to track and transmit key data to measure frontline workers stress and recovery during a uniquely stressful and high-risk work environment.
Pilot study of a wearable gas and sweat skin sensor