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TIHM: An open dataset for remote healthcare monitoring in dementia
Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressin...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492790/ https://www.ncbi.nlm.nih.gov/pubmed/37689815 http://dx.doi.org/10.1038/s41597-023-02519-y |
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author | Palermo, Francesca Chen, Yu Capstick, Alexander Fletcher-Loyd, Nan Walsh, Chloe Kouchaki, Samaneh True, Jessica Balazikova, Olga Soreq, Eyal Scott, Gregory Rostill, Helen Nilforooshan, Ramin Barnaghi, Payam |
author_facet | Palermo, Francesca Chen, Yu Capstick, Alexander Fletcher-Loyd, Nan Walsh, Chloe Kouchaki, Samaneh True, Jessica Balazikova, Olga Soreq, Eyal Scott, Gregory Rostill, Helen Nilforooshan, Ramin Barnaghi, Payam |
author_sort | Palermo, Francesca |
collection | PubMed |
description | Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal, observational, and measurement data, within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days. |
format | Online Article Text |
id | pubmed-10492790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104927902023-09-11 TIHM: An open dataset for remote healthcare monitoring in dementia Palermo, Francesca Chen, Yu Capstick, Alexander Fletcher-Loyd, Nan Walsh, Chloe Kouchaki, Samaneh True, Jessica Balazikova, Olga Soreq, Eyal Scott, Gregory Rostill, Helen Nilforooshan, Ramin Barnaghi, Payam Sci Data Data Descriptor Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal, observational, and measurement data, within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days. Nature Publishing Group UK 2023-09-09 /pmc/articles/PMC10492790/ /pubmed/37689815 http://dx.doi.org/10.1038/s41597-023-02519-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Palermo, Francesca Chen, Yu Capstick, Alexander Fletcher-Loyd, Nan Walsh, Chloe Kouchaki, Samaneh True, Jessica Balazikova, Olga Soreq, Eyal Scott, Gregory Rostill, Helen Nilforooshan, Ramin Barnaghi, Payam TIHM: An open dataset for remote healthcare monitoring in dementia |
title | TIHM: An open dataset for remote healthcare monitoring in dementia |
title_full | TIHM: An open dataset for remote healthcare monitoring in dementia |
title_fullStr | TIHM: An open dataset for remote healthcare monitoring in dementia |
title_full_unstemmed | TIHM: An open dataset for remote healthcare monitoring in dementia |
title_short | TIHM: An open dataset for remote healthcare monitoring in dementia |
title_sort | tihm: an open dataset for remote healthcare monitoring in dementia |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492790/ https://www.ncbi.nlm.nih.gov/pubmed/37689815 http://dx.doi.org/10.1038/s41597-023-02519-y |
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