Strengthening family caregiving through innovative technology solutions

03/12/2019

Source: Innovations in Aging

Follow this link for the abstract

Date of publication: 3 November 2019

 Publication type: Journal article

In a nutshell: Technology has the potential to enhance the repertoire of tools for family caregiving to address the complexities of caring for older adults. There are examples of technology-enabled interventions helping older adults remain independent and safe in their home; easing the financial, physical, and psychological challenges of family caregiving; assisting in the management of chronic illness; improving socialization and support; offering information and resources on a “just in time” basis; and improving the quality of care and quality of life for both older adults and their family caregivers. This session will review eight evidence-based, technology-enabled solutions for family caregivers, including technology solutions that address medication adherence, falls prevention, personal emergency response, remote monitoring, telehealth, dementia tracking, social engagement, and care training. Key drivers for successful application of these interventions (e.g., technology, analytics, user experience design) as well as barriers to scaling (e.g., accessibility, affordability, regulation) will be reviewed.

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


When will my patient fall? Sensor-based in-home walking speed identifies future falls in older adults

25/06/2019

Source: The Journal of Gerontology series A

Follow this link for the abstract

Date of publication: 16 May 2019

 Publication type: Journal article

In a nutshell: Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall.

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

 


Fall classification by machine learning using mobile phones

06/12/2012

Source: PLoS ONE, 2012, 7 (5/e36556) p. 1-6

Follow this link for article

Date of publication: May 2012

Publication type: Journal article

In a nutshell: With falls being a common source of injury in elderly people and the ability to automatically detect falls allowing rapid responses to potential emergencies, techniques have been developed to reliably detect a fall as well as to automatically classify the type. This study demonstrated technology that enables machines to identify a fall and collect information to quickly classify the type to enable a more rapid response.

Length of publication: 6 pages