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1,303 results for “patient monitoring”
Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study
<p><strong>This repository contains raw data relating to: </strong>Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study Mueller A., Hoefling H., Nuritdinow T., et al. DOI: 10.1159/000490919</p> <p><strong>Metadata and processed data derived from the raw data deposited here is available here:</strong> https://github.com/Novartis/mueller_et_al_2018</p> <p><strong>Article Abstract</strong></p> <p>Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with > 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.</p>
ATOPE+Breast, Continuous Monitoring of Training Load in Patients with Breast Cancer during Therapeutic Exercise Intervention
<p>The ATOPE+Breast dataset (ATOPE+ for patients with breast cancer) describes the daily status of 23 patients with breast cancer during therapeutic exercise intervention with daily measures of HRV, self-reported wellness, physical activity, and sleep. Besides, the ATOPE+Breast dataset contains information about training sessions, such as intensity recorded, demographic data, treatment details, initial evaluations of quality of life, physical activity levels, previous medical conditions, and risk factors. ATOPE+Breast was recorded using the ATOPE+ mHealth system, whose usability [1] and reliability [2] were successfully validated.</p> <p>References</p> <ul> <li>[1] S. Moreno-Gutierrez et al., “ATOPE+: An mHealth System to Support Personalized Therapeutic Exercise Interventions in Patients With Cancer,” IEEE Access, vol. 9, pp. 16878–16898, 2021, doi: 10.1109/ACCESS.2021.3049398.</li> <li>[2] P. Postigo-Martin et al., “mHealth system (ATOPE+) to support exercise prescription in breast cancer survivors: A validity and reliability, cross-sectional observational study (ATOPE study) (Preprint),” JMIR mHealth and uHealth, Preprint, Jan. 2022. doi: 10.2196/preprints.36733.</li> </ul>
Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)
<p>This video is the fifth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)</p> <p>Bio: Beth Harvey is currently a master’s student in digital health and data analytics at the Henley Business School. Having completed her bachelor’s in biomedical science, Beth has then gone on to work in the medical device and IVD regulatory sector with a consultancy firm in Vancouver. After moving back to the UK in 2019 Beth worked with multiple UK manufacturers in the digital health space which spurred her interest in the field she is now studying. Beth’s research interests are in healthcare innovation remote patient monitoring, and data analysis. She is currently finishing her dissertation on the opportunities and challenges for remote blood monitoring in oncology in collaboration with the Future Blood Network.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/8pbO3N9IQKM</p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
Comparison of Glucose Values and Variability Between TOUJEO and TRESIBA During Continuous Glucose Monitoring in Type 1 Diabetes Patients
ClinicalTrials.gov study NCT04075513. IPD Sharing: YES. Countries: 7. Publications: 1.
A Study of Limited Heart Monitoring During Non-anthracycline Trastuzumab-based Therapy in Breast Cancer Patients
ClinicalTrials.gov study NCT03983382. IPD Sharing: YES. Countries: 1. Publications: 1.
Monitoring melanoma patients on treatment reveals a distinct macrophage population driving targeted therapy resistance
<p><span>Resistance to targeted therapy remains a major clinical challenge in melanoma patients. To uncover resistance mechanisms, we performed single cell RNA sequencing of fine needle aspirates from resistant and responding tumors from melanoma patients before and during BRAFi/MEKi treatment. Among the genes most differentially expressed between malignant cells from resistant vs. responding tumors was POSTN encoding the secreted factor periostin. POSTN was predicted to predominantly signal to a macrophage population associated with targeted therapy resistance (TTR). Accordingly, tumors from patients with fast disease progression after targeted therapy exhibited high POSTN expression levels as well as high numbers of TTR macrophages, and POSTN was able to polarize macrophages towards a TTR phenotype. In a mouse model in vivo, POSTN expression protected melanoma from targeted therapy, which was associated with a phenotype change of intratumroal macrophages. Finally, polarized TTR macrophages protected melanoma cells from MEKi-induced killing through CD44 receptor expression on melanoma cells. Thus, interfering with the protective activity of TTR macrophages might represent a strategy to overcome resistance to targeted therapy in melanoma.</span></p>
Effectiveness of self-management of medication and self-monitoring of blood pressure, diet, and physical exercise on blood pressure in patients with poorly controlled hypertension (MEDICHY study): randomized and controlled trial
<p>Dataset study medichy ISRCTN144433778</p>
Feasibility of Monitoring Health Data in Pediatric Patients Undergoing Chemotherapy
ClinicalTrials.gov study NCT04134429. IPD Sharing: YES. Countries: 1. Publications: 1.
Safety and Efficacy Study of IPG Patient With Home Monitoring
ClinicalTrials.gov study NCT01523704. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Full Early Atrial Diagnostics in Single Chamber ICD Patients Using the DX Lead and Home Monitoring in Brazil.
ClinicalTrials.gov study NCT04869527. IPD Sharing: NO. Countries: 1. Publications: 0.
Prolongation of the Interval Between Monitoring of Warfarin in Stable Patients
ClinicalTrials.gov study NCT00356759. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of Remote Patient Monitoring on Chronic Disease Management
ClinicalTrials.gov study NCT03127852. IPD Sharing: NO. Countries: 1. Publications: 2.
An Integrated Consent Model Study to Compare Two Standard of Care Schedules for Monitoring Cardiac Function in Patients Receiving Trastuzumab for Early Stage Breast Cancer
ClinicalTrials.gov study NCT02696707. IPD Sharing: NO. Countries: 1. Publications: 1.
Continuous Glucose Monitoring in Patients With End-Stage Kidney Disease and Burnt-Out Diabetes
ClinicalTrials.gov study NCT05741489. IPD Sharing: NO. Countries: 1. Publications: 1.
Remote Glucose Monitoring System in Hospitalized Patients With Diabetic Ketoacidosis (DKA)
ClinicalTrials.gov study NCT05439928. IPD Sharing: NO. Countries: 1. Publications: 11.
CardioMEMS Heart Sensor Allows Monitoring of Pressure to Improve Outcomes in NYHA Class III Heart Failure Patients
ClinicalTrials.gov study NCT00531661. IPD Sharing: Not stated. Countries: 1. Publications: 8.
BIO monitorinG in Patients With Preserved Left ventricUlar Function AfteR Diagnosed Myocardial Infarction
ClinicalTrials.gov study NCT02341534. IPD Sharing: Not stated. Countries: 14. Publications: 2.
Study to Monitor the Occurrence of Viral Variants in Patients With Compromised Immune Systems Being Treated for COVID-19
ClinicalTrials.gov study NCT05305651. IPD Sharing: YES. Countries: 1. Publications: 1.
Remote Monitoring of Lung Cancer Patient-Reported Outcomes Using Moovcare®
ClinicalTrials.gov study NCT05011890. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.