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952 results for “self management”
GERONTE H2020 project - GERDAT004 - Dataset of self-management recommendations
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of self-management recommendations that can be used in the care for older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>Patient empowerment by supporting self-management is an important component of the Geronte care pathway. As patients will be monitoring themselves at home, to register side-effects of treatment, decompensation of comorbidities and signs of functional decline, they will also be faced with questions about how to deal with the issues that they are having. While one important component of the care pathway is early signalling of complications to allow for early intervention by health care professionals, there is also a lot that patients can do for themselves at home to enhance their life-style, decrease burden of signs and symptoms or to improve outcomes.</p> <p>This led to the composition of a dataset to be included in the GERONTE care pathway, which is presented here.</p>
GERDAT014 Self-management and self-monitoring literature search
<p>Dataset used for the literature on suitable self-monitoring or self-management tools or applications that could be used for older patients with cancer and/or multimorbidity</p>
Self-contained 4-BSS's dataset of spectrum management in WLANs
<p>This folder contains the self-contained dataset of 4 BSS's analyzed in the thesis by <em>Sergio Barrachina-Muñoz, "Responsive Spectrum Management for Wireless Local Area Networks: from Heuristic-based Policies to Model-Free Reinforcement Learning", 2020</em>.</p> <p>-------------------------------------------------------------<br> <strong>*** General info ***</strong></p> <p>The dataset has been generated simulating all the spectrum management configurations (including primary channel and maximum bandwidth) in a 4-BSS's deploymment. Simulations have been performed with the Komondor wireless network simulator (<a href="https://github.com/wn-upf/Komondor">https://github.com/wn-upf/Komondor</a>).</p> <p><strong>*** Dataset structure ***</strong></p> <p>The dataset is composed of 1 file, dataset.csv, containing all the combinations of spectrum management configurations.</p> <p><strong>*** File format ***</strong></p> <p>The dataset.csv file is composed of 53 columns and 1679616 rows. <br> - Each column is a parameter or performance metric of the global spectrum management configuration, i.e., the configuration of all the BSS's.<br> - Each row is a realization of the global configuration.<br> - Column sim_code refers to the simulation code.</p> <p>The colums for each BSS are (only showing for BSS A):<br> - bss_A_code: code of the BSS<br> - action_ix_A: action (or BSS configuration) index<br> - status_ix_A: status index (combination of action and traffic load)<br> - primary_A: primary channel of the BSS<br> - max_bw_ix_A: index of the maximum allowed bandwidth of the BSS<br> - load_ix_A: traffic load index of the BSS<br> - load_A: traffic load [pkt/s] of the BSS<br> - thr_A: throughput [Mbps] of the BSS<br> - d_A: packet delay [ms] of the BSS<br> - rts_lost_A: number of RTS lost by BSS A<br> - rts_sent_A: number of RTS sent by BSS A<br> - frames_lost_A: number of frames lost by BSS A<br> - frames_sent_A: number of frames sent by BSS A</p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</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/ChdbggScUgo</p>
Cognitive Training for Diabetes Self-Management
ClinicalTrials.gov study NCT04831775. IPD Sharing: NO. Countries: 1. Publications: 2.
Mobile Self-Management Program for Stress Reduction in Young Adults
ClinicalTrials.gov study NCT07174544. IPD Sharing: NO. Countries: 1. Publications: 0.
Log2Lose: Incenting Weight Loss and Dietary Self-monitoring in Real-time to Improve Weight Management Among Adults With Obesity
ClinicalTrials.gov study NCT04770909. IPD Sharing: YES. Countries: 1. Publications: 2.
Enhancing Survivorship Care Planning for Patients With Localized Prostate Cancer Using A Couple-focused Web-based Tailored Symptom Self-management Program
ClinicalTrials.gov study NCT04350788. IPD Sharing: YES. Countries: 1. Publications: 1.
Self-Management Interventions for Chronic Pain Relief With Cancer Survivors
ClinicalTrials.gov study NCT03867760. IPD Sharing: YES. Countries: 1. Publications: 1.
Integrating REBT and ACT : An intervention study for managing academic self-handicapping behaviour among young adults
<p>The data consists of pre and post interventions scores of 25 participants who were selected based on their ratings on Academic self -handicapping scale (Geetika & Gupta, 2020) . Thirteen participants were randomly assigned to the experimental group which was exposed to 8 hours of intervention that combined REBT and ACT. Twelve participants were randomly assigned to the control group and did not receive any intervention. The pre-intervention scores on Academic self -handicapping scale (Geetika & Gupta, 2020) were assessed at the beginning of the intervention and at two weeks since the beginning of the intervention. Sub scale scores on Behavioural and Claimed self -handicapping were recorded and overall score by adding the two were also obtained. </p>
Verification of the effects of a YouTube-based home-based (self-managed intervention) training system developed for frailty prevention―A pilot study ―
<p><em>Background and Objectives</em>: Resistance training is considered the most effective intervention for increasing older people’s muscle mass and strength. We devised a self-administered training system (squat + balance training, sukubara®) that incorporates a new low-load exercise. This study hypothesizes that introducing sukubara® affects skeletal muscle mass and physical function positively, and we first conducted a preliminary verification in healthy non-elderly participants.</p> <p><em>Materials and Methods</em>: This study’s participants were non-elderly healthy hospital personnel. Applicants were randomly assigned to two groups, a resistance training group that performed an exercise program (sukubara®) and a control group that did not, and they received a 12-week intervention. This study’s primary endpoint was change in skeletal muscle mass; the secondary endpoints were knee extension strength and one-leg standing time with eyes closed.</p> <p><em>Results</em>: An analysis of Tthe 18 participants (10 in the resistance training group and 8 in the control group), who were 18 analyzed this study’s results was performed. The results of changes in variables between both groups during the intervention period were as follows: skeletal muscle mass, knee extension strength, and one-leg standing time were significantly improved or tended to be significantly higher in a resistance training group than in a control group. <em>Conclusions</em>: A self-administered training system (sukubara®) incorporating low-load exercise resulted in muscle hypertrophy and improvement in physical function.</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>
Enhancing Patient Ability to Understand and Utilize Complex Information Concerning Medication Self-management
ClinicalTrials.gov study NCT02820038. IPD Sharing: YES. Countries: 1. Publications: 1.
Self-Management Addressing Heart Disease Risk Trial
ClinicalTrials.gov study NCT00499096. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Promoting Benzodiazepine Cessation Through an Electronically-delivered Patient Self-management Intervention
ClinicalTrials.gov study NCT04572750. IPD Sharing: NO. Countries: 1. Publications: 3.
Chronic-disease Self-management Program in Patients Living With Long-COVID in Puerto Rico
ClinicalTrials.gov study NCT06208696. IPD Sharing: NO. Countries: 1. Publications: 2.
Assess and Adapt to the Impact of COVID-19 on CVD Self-Management and Prevention Care in Adults Living With HIV
ClinicalTrials.gov study NCT04661813. IPD Sharing: YES. Countries: 1. Publications: 1.
Stroke Self-Management: Effect on Function and Stroke Specific Quality of Life
ClinicalTrials.gov study NCT01507688. IPD Sharing: YES. Countries: 1. Publications: 5.
Effects of an Individual and Family Self-Management of Fall Prevention Program on Balance Ability and Fall-related Self-efficacy Among Chinese Post-Stroke Individuals
ClinicalTrials.gov study NCT06577662. IPD Sharing: YES. Countries: 1. Publications: 1.
Computer Based Treatment for Cognitive Behavioral Therapy and Cooperative Pain Education and Self-Management
ClinicalTrials.gov study NCT05204576. 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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.