Literature DB >> 33540510

Directing and Orienting ICT Healthcare Solutions to Address the Needs of the Aging Population.

Nada Fares1, R Simon Sherratt1, Imad H Elhajj2.   

Abstract

BACKGROUND: With an aging population, it is essential to maintain good health and autonomy for as long as possible. Instead of hospitalisation or institutionalisation, older people with chronic conditions can be assisted in their own home with numerous "smart" devices that support them in their activities of daily living, manage their medical conditions, and prevent fall incidents. Information and Communication Technology (ICT) solutions facilitate the monitoring and management of older people's health to improve quality of life and physical activity with a decline in caregivers' burden.
METHOD: The aim of this paper was to conduct a systematic literature review to analyse the state of the art of ICT solutions for older people with chronic conditions, and the impact of these solutions on their quality of life from a biomedical perspective.
RESULTS: By analysing the literature on the available ICT proposals, it is shown that different approaches have been deployed by noticing that the more cross-interventions are merged then the better the results are, but there is still no evidence of the effects of ICT solutions on older people's health outcomes. Furthermore, there are still unresolved ethical and legal issues.
CONCLUSION: While there has been much research and development in healthcare ICT solutions for the aging population, ICT solutions still need significant development in order to be user-oriented, affordable, and to manage chronic conditions in the aging wider population.

Entities:  

Keywords:  activities of daily life; assistive living; assistive/home monitoring technologies; caregivers; chronic diseases; fall detection; fall prediction; fall prevention; older people

Year:  2021        PMID: 33540510      PMCID: PMC7912863          DOI: 10.3390/healthcare9020147

Source DB:  PubMed          Journal:  Healthcare (Basel)        ISSN: 2227-9032


  65 in total

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Journal:  Sensors (Basel)       Date:  2017-01-12       Impact factor: 3.576

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10.  Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk.

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Journal:  Healthcare (Basel)       Date:  2022-07-05
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