Literature DB >> 1413966

Falls and lying helpless in the elderly.

O P Ryynänen1, S L Kivelä, R Honkanen, P Laippala.   

Abstract

Twelve percent of men and 19% of women aged 65 years and over who sought medical attention after a fall, lie where they fell for 15 min or more after falling. The occurrence of a fall with a long period of lying helpless was associated in bivariate analyses with severe injury, an intrinsic or unknown mechanism of falling, falling indoors, poor functional capacity, use of walking aids, body temperature 37.5 degrees C or over, and serum potassium concentration under 3.5 mmol/l. A log-linear model showed that a fall with a lie of this kind was related independently to high body temperature, low serum potassium concentration, and severe injury. The occurrence of such a fall due to an extrinsic mechanism was related to poor functional capacity, but no similar relationship could be found when the fall was due to an intrinsic or unknown mechanism.

Entities:  

Mesh:

Year:  1992        PMID: 1413966

Source DB:  PubMed          Journal:  Z Gerontol        ISSN: 0044-281X


  6 in total

1.  Frequency of and Factors Associated with a Proxy for Critical Falls Among People Aging with Multiple Sclerosis: An Exploratory Study.

Authors:  Etienne J Bisson; Elizabeth W Peterson; Marcia Finlayson
Journal:  Int J MS Care       Date:  2017 Mar-Apr

2.  Smartphone-based solutions for fall detection and prevention: challenges and open issues.

Authors:  Mohammad Ashfak Habib; Mas S Mohktar; Shahrul Bahyah Kamaruzzaman; Kheng Seang Lim; Tan Maw Pin; Fatimah Ibrahim
Journal:  Sensors (Basel)       Date:  2014-04-22       Impact factor: 3.576

Review 3.  Involvement of older people in the development of fall detection systems: a scoping review.

Authors:  Friederike J S Thilo; Barbara Hürlimann; Sabine Hahn; Selina Bilger; Jos M G A Schols; Ruud J G Halfens
Journal:  BMC Geriatr       Date:  2016-02-11       Impact factor: 3.921

4.  Involvement of the end user: exploration of older people's needs and preferences for a wearable fall detection device - a qualitative descriptive study.

Authors:  Friederike Js Thilo; Selina Bilger; Ruud Jg Halfens; Jos Mga Schols; Sabine Hahn
Journal:  Patient Prefer Adherence       Date:  2016-12-20       Impact factor: 2.711

5.  Older Adults-Potential Users of Technologies.

Authors:  Vita Lesauskaitė; Gytė Damulevičienė; Jurgita Knašienė; Egidijus Kazanavičius; Agnius Liutkevičius; Audronė Janavičiūtė
Journal:  Medicina (Kaunas)       Date:  2019-06-07       Impact factor: 2.430

6.  Fall classification by machine learning using mobile phones.

Authors:  Mark V Albert; Konrad Kording; Megan Herrmann; Arun Jayaraman
Journal:  PLoS One       Date:  2012-05-07       Impact factor: 3.240

  6 in total

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