View clinical trials related to Wearable Devices.
Filter by:The aim of this study is to examine the effect of the sock to be developed with wearable technology for patients who will undergo TUR surgery on the development of hypothermia and VTE. The population of the study will consist of patients who will undergo TUR surgery between 01 October 2023 and 01 October 2024. patients will be included in the study. The study was planned as a prospective, two-arm (1:1), randomised controlled, double-blind clinical trial. The data will be collected with the "Descriptive Characteristics Form" and "Hypothermia Monitoring Form". The hypothermia follow-up form includes "Shivering Level Diagnosis Form" and "Temperature Comfort Perception Scale" The descriptive variables of the patients included in the study will be expressed as mean ± standard deviation and median (maximum-minimum), percentage and frequency. In data analysis; dependent and independent t test will be used when parametric test preconditions are met. Changes in body temperature measurements obtained after wearing socks to be developed with wearable technology, repeated measurements, analysis of variance (Repeated ANOVA) if parametric, Friedman test if non-parametric, and post-hoc test will be used in intra-group multiple comparison analyses as further analysis. Post hoc power analysis will be performed after the sample size reaches 70.
Lifting loads can cause work-related musculoskeletal disorders. The National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency, and other geometrical characteristics of lifting. Body-worn inertial sensor technology provides a number of opportunities to advance the safety and health of workers engaged in physical work. Motion-tracking systems together with Machine learning (ML) algorithms are used in the ergonomic field for biomechanical risk assessment by means of data acquired by wearable inertial systems. The investigators posed the question whether it is possible to classify lifting tasks belonging to different risk classes according to the value of LI using a machine learning approach by means of features extracted from raw signals. Aim of this study was to develop and validate, through ML algorithms, a non-invasive detection system of kinetic-kinematic parameters using IMU and EMG sensors, for the ergonomic assessment of the risk associated with a load lifting activity.
The study approach is to leverage the most cutting-edge techniques of multi-omics biology, wearable physiology, and digital real-time psychology profiling and using machine learning models to understand the mechanisms underlying the strategies and techniques that enable participants the power to initiate and maintain sustainable behavior change. Over the years, millions of people worldwide have attended immersive personal development seminars aiming to improve participants' health behaviors and wellness. Nevertheless, there's a scarcity of large-scale studies to assess their effects on behavior change and investigate their mechanism of action. A recent publication by the Science of Behavior Change Program (SOBC), launched by the National Institute of Health (NIH), recognized that: "science has not yet delivered a unified understanding of basic mechanisms of behavior change across a broad range of health-related behaviors, limiting progress in the development and translation of effective and efficacious behavioral intervention." As such, understanding the mechanisms underlying sustainable behavior change is key. The Date With Destiny (DWD) seminar is among the largest worldwide, and tens of thousands of people have already attended and testified to its transformative effect. The main objective of the study is to uncover the underlying mechanism of behavior change through longitudinal data collection of psychometrics Ecological Momentary Assessments, physiology (wearables), and biology (multi-omics) in study participants. The study specific objectives include: (1) To evaluate the impact of DWD on sustainable behavior change; (2) To investigate the mechanism of behavior change by collecting longitudinal real-time measurements of psychometrics (e.g., Ecological Momentary Assessments [EMA]), physiological (e.g., heart rate, blood oxygen level, breathing rate, and EDA), and biological (multi-omics analyses) features in study participants; (3) To assess the effect of the DWD on professional fulfillment, resilience, and mental wellness.
As a necessary process of life, sleep is an important link for the body to recover, integrate and consolidate memory. However, the fast pace of life in modern society and people's bad living habits are easy to cause sleep disorders. Sleep disorders are often the main factors that induce or aggravate cardiovascular and cerebrovascular diseases. Sleep staging is an important basis for sleep quality assessment and related disease diagnosis. At present, electroencephalography (EEG) has become the gold standard for judging sleep stages. However, this kind of method requires long-term contact of multiple electrodes with the human body, which is easy to affect the natural sleep of the subjects, so it is not suitable for sleep monitoring in home environment. Studies have shown that sleep is related to the regulation of autonomic nervous system, and heart rate variability (HRV) in sleep also shows periodic changes similar to brain waves. Smartwatch/bracelet can continuously monitor the user's pulse wave and acceleration data comfortably and without feeling. HRV features can be extracted using pulse wave data, and then sleep staging can be realized based on the correlation between HRV and brain waves, and sleep quality can be evaluated. Therefore, healthy sleep research aims to use smart devices to achieve sleep monitoring in the home environment and improve people's sleep quality
Early ambulation of inpatients has been shown to be a key driver of decreased LOS and also reduced adverse events such as venous thromboembolism (VTE). We will test if a patient wearable device (pedometer) measuring steps and ambulation sessions decreases hospital LOS (primary outcome), decreases hospital LOS index (LOSI), decreases time to first ambulation, decreases time to first bowel movement (BM), decreases incidence of VTEs, and decreases costs (secondary outcomes). In a pilot randomized control trial, we will randomize 150 total adult patients admitted to UF Health Jacksonville in a 1:1 fashion to usual care and wearable pedometer or usual care. Patients randomized to the study intervention will receive a wearable pedometer upon admission, to be worn for the duration of their inpatient stay. Study outcome measures to be compared between the pedometer and no pedometer group include hospital LOS (primary outcome), hospital LOSI, time to first ambulation, time to first BM, incidence of VTEs, patient experience, and costs (secondary outcomes).