The state of knowledge on technologies and their use for fall detection: A scoping review
Background Globally, populations are aging with increasing life spans. The normal aging process and the resulting disabilities increase fall risks. Falls are an important cause of injury, loss of independence and institutionalization. Technologies have been developed to detect falls and reduce their...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/22133
- Acceso en línea:
- https://doi.org/10.1016/j.ijmedinf.2017.12.015
https://repository.urosario.edu.co/handle/10336/22133
- Palabra clave:
- Controlled environment
Current technology
Falls
Implementation cost
Literature reviews
Older adults
Scoping review
Technology readiness levels
Wearable sensors
Algorithm
Assistive technology
Data mining
Fuzzy logic
Hidden Markov model
Internet
Journal impact factor
Knowledge
Medical information
Medical technology
Priority journal
Qualitative research
Review
Support vector machine
Aged
Attitude to health
Falling
Human
Quality of life
Statistics and numerical data
Accidental Falls
Aged
Biomedical Technology
Humans
Quality of Life
Falls
Older adults
Scoping review
Technology
Attitudes
Practice
Health Knowledge
- Rights
- License
- Abierto (Texto Completo)
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109c5245-fbf8-46dd-a93a-4fbc1fcb7b54-1a129ce83-41fe-41ee-92c5-c3bbdca6ba58-1dc42dfaf-7fb2-4603-9516-001a56e2500b-16ee62dd2-7802-4f09-a893-353f76f6d3c3-1c27105a4-92d5-4105-aa19-8bbf8d49ad11-1935e83f4-8cd7-47b1-ad5e-da30ceae2d38-12020-05-25T23:55:35Z2020-05-25T23:55:35Z2018Background Globally, populations are aging with increasing life spans. The normal aging process and the resulting disabilities increase fall risks. Falls are an important cause of injury, loss of independence and institutionalization. Technologies have been developed to detect falls and reduce their consequences but their use and impact on quality of life remain debatable. Reviews on fall detection technologies exist but are not extensive. A comprehensive literature review on the state of knowledge of fall detection technologies can inform research, practice, and user adoption. Objectives To examine the extent and the diversity of current technologies for fall detection in older adults. Methods A scoping review design was used to search peer-reviewed literature on technologies to detect falls, published in English, French or Spanish since 2006. Data from the studies were analyzed descriptively. Results The literature search identified 3202 studies of which 118 were included for analysis. Ten types of technologies were identified ranging from wearable (e.g., inertial sensors) to ambient sensors (e.g., vision sensors). Their Technology Readiness Level was low (mean 4.54 SD 1.25; 95% CI [4.31, 4.77] out of a maximum of 9). Outcomes were typically evaluated on technological basis and in controlled environments. Few were evaluated in home settings or care units with older adults. Acceptability, implementation cost and barriers were seldom addressed. Conclusions Further research should focus on increasing Technology Readiness Levels of fall detection technologies by testing them in real-life settings with older adults. © 2017 Elsevier B.V.application/pdfhttps://doi.org/10.1016/j.ijmedinf.2017.12.01513865056https://repository.urosario.edu.co/handle/10336/22133engElsevier7158International Journal of Medical InformaticsVol. 111International Journal of Medical Informatics, ISSN:13865056, Vol.111,(2018); pp. 58-71https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039709375&doi=10.1016%2fj.ijmedinf.2017.12.015&partnerID=40&md5=13f22f15c072ab07155c63926502e49dAbierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURControlled environmentCurrent technologyFallsImplementation costLiterature reviewsOlder adultsScoping reviewTechnology readiness levelsWearable sensorsAlgorithmAssistive technologyData miningFuzzy logicHidden Markov modelInternetJournal impact factorKnowledgeMedical informationMedical technologyPriority journalQualitative researchReviewSupport vector machineAgedAttitude to healthFallingHumanQuality of lifeStatistics and numerical dataAccidental FallsAgedBiomedical TechnologyHumansQuality of LifeFallsOlder adultsScoping reviewTechnologyAttitudesPracticeHealth KnowledgeThe state of knowledge on technologies and their use for fall detection: A scoping reviewarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Lapierre N.Neubauer N.Miguel-Cruz A.Rios Rincon A.Liu L.Rousseau J.10336/22133oai:repository.urosario.edu.co:10336/221332022-05-02 07:37:20.255011https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co |
dc.title.spa.fl_str_mv |
The state of knowledge on technologies and their use for fall detection: A scoping review |
title |
The state of knowledge on technologies and their use for fall detection: A scoping review |
spellingShingle |
The state of knowledge on technologies and their use for fall detection: A scoping review Controlled environment Current technology Falls Implementation cost Literature reviews Older adults Scoping review Technology readiness levels Wearable sensors Algorithm Assistive technology Data mining Fuzzy logic Hidden Markov model Internet Journal impact factor Knowledge Medical information Medical technology Priority journal Qualitative research Review Support vector machine Aged Attitude to health Falling Human Quality of life Statistics and numerical data Accidental Falls Aged Biomedical Technology Humans Quality of Life Falls Older adults Scoping review Technology Attitudes Practice Health Knowledge |
title_short |
The state of knowledge on technologies and their use for fall detection: A scoping review |
title_full |
The state of knowledge on technologies and their use for fall detection: A scoping review |
title_fullStr |
The state of knowledge on technologies and their use for fall detection: A scoping review |
title_full_unstemmed |
The state of knowledge on technologies and their use for fall detection: A scoping review |
title_sort |
The state of knowledge on technologies and their use for fall detection: A scoping review |
dc.subject.keyword.spa.fl_str_mv |
Controlled environment Current technology Falls Implementation cost Literature reviews Older adults Scoping review Technology readiness levels Wearable sensors Algorithm Assistive technology Data mining Fuzzy logic Hidden Markov model Internet Journal impact factor Knowledge Medical information Medical technology Priority journal Qualitative research Review Support vector machine Aged Attitude to health Falling Human Quality of life Statistics and numerical data Accidental Falls Aged Biomedical Technology Humans Quality of Life Falls Older adults Scoping review Technology |
topic |
Controlled environment Current technology Falls Implementation cost Literature reviews Older adults Scoping review Technology readiness levels Wearable sensors Algorithm Assistive technology Data mining Fuzzy logic Hidden Markov model Internet Journal impact factor Knowledge Medical information Medical technology Priority journal Qualitative research Review Support vector machine Aged Attitude to health Falling Human Quality of life Statistics and numerical data Accidental Falls Aged Biomedical Technology Humans Quality of Life Falls Older adults Scoping review Technology Attitudes Practice Health Knowledge |
dc.subject.keyword.eng.fl_str_mv |
Attitudes Practice Health Knowledge |
description |
Background Globally, populations are aging with increasing life spans. The normal aging process and the resulting disabilities increase fall risks. Falls are an important cause of injury, loss of independence and institutionalization. Technologies have been developed to detect falls and reduce their consequences but their use and impact on quality of life remain debatable. Reviews on fall detection technologies exist but are not extensive. A comprehensive literature review on the state of knowledge of fall detection technologies can inform research, practice, and user adoption. Objectives To examine the extent and the diversity of current technologies for fall detection in older adults. Methods A scoping review design was used to search peer-reviewed literature on technologies to detect falls, published in English, French or Spanish since 2006. Data from the studies were analyzed descriptively. Results The literature search identified 3202 studies of which 118 were included for analysis. Ten types of technologies were identified ranging from wearable (e.g., inertial sensors) to ambient sensors (e.g., vision sensors). Their Technology Readiness Level was low (mean 4.54 SD 1.25; 95% CI [4.31, 4.77] out of a maximum of 9). Outcomes were typically evaluated on technological basis and in controlled environments. Few were evaluated in home settings or care units with older adults. Acceptability, implementation cost and barriers were seldom addressed. Conclusions Further research should focus on increasing Technology Readiness Levels of fall detection technologies by testing them in real-life settings with older adults. © 2017 Elsevier B.V. |
publishDate |
2018 |
dc.date.created.spa.fl_str_mv |
2018 |
dc.date.accessioned.none.fl_str_mv |
2020-05-25T23:55:35Z |
dc.date.available.none.fl_str_mv |
2020-05-25T23:55:35Z |
dc.type.eng.fl_str_mv |
article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.spa.spa.fl_str_mv |
Artículo |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1016/j.ijmedinf.2017.12.015 |
dc.identifier.issn.none.fl_str_mv |
13865056 |
dc.identifier.uri.none.fl_str_mv |
https://repository.urosario.edu.co/handle/10336/22133 |
url |
https://doi.org/10.1016/j.ijmedinf.2017.12.015 https://repository.urosario.edu.co/handle/10336/22133 |
identifier_str_mv |
13865056 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.citationEndPage.none.fl_str_mv |
71 |
dc.relation.citationStartPage.none.fl_str_mv |
58 |
dc.relation.citationTitle.none.fl_str_mv |
International Journal of Medical Informatics |
dc.relation.citationVolume.none.fl_str_mv |
Vol. 111 |
dc.relation.ispartof.spa.fl_str_mv |
International Journal of Medical Informatics, ISSN:13865056, Vol.111,(2018); pp. 58-71 |
dc.relation.uri.spa.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039709375&doi=10.1016%2fj.ijmedinf.2017.12.015&partnerID=40&md5=13f22f15c072ab07155c63926502e49d |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.acceso.spa.fl_str_mv |
Abierto (Texto Completo) |
rights_invalid_str_mv |
Abierto (Texto Completo) http://purl.org/coar/access_right/c_abf2 |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Elsevier |
institution |
Universidad del Rosario |
dc.source.instname.spa.fl_str_mv |
instname:Universidad del Rosario |
dc.source.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
Repositorio institucional EdocUR |
repository.mail.fl_str_mv |
edocur@urosario.edu.co |
_version_ |
1814167624567750656 |