Role of the Upper Limb in Limiting Head Impact During Laboratory-Induced Falls in at Fall-Risk Older Adults

Overview

Journal J Gerontol A Biol Sci Med Sci

Specialty Geriatrics

Date 2024 Nov 6

PMID 39501431

Authors

Lingjun Chen

Tobia Zanotto

James Fang

Ethan Scharf

Nathanael Garcia

Andrew Luzania

Rishav Mukherjee

Neil B Alexander

Jacob J Sosnoff

Affiliations

Soon will be listed here.

Abstract

Background: Fall-related head impact is the leading cause of traumatic brain injury in older adults. There is limited understanding of factors related to fall-related head impact. This investigation examined characteristics of upper limb movements during standing-height falls and examined their association with fall-related head impact in older adults at risk for falls.

Methods: Older adults (n = 29) at risk for fall-related injuries underwent experimentally induced falls in multiple directions (backwards and sideways). To characterize the upper limb movements and their association with head impact, a standardized analysis tool was used to analyze a total of 164 video-recorded falls. The association between upper limb movements (and their characteristics) and head impact was analyzed through logistic regression.

Results: Nearly 80% of falls involved upper limb movements. Absence of upper limb movements significantly increased head impact odds by approximately 4-fold. The odds of head impact were reduced in falls with energy absorption at the forearm (0.013-fold) and upper arm (0.018-fold), compared to falls without upper limb energy absorption. Backwards falls showed significantly higher odds of head impact (more than 4-fold).

Conclusions: Upper limb movements are common during fall descent and are associated with lower odds of experiencing head impact. Energy absorption with the upper limb seems to be an important protective mechanism. Future work should explore if these movements can be augmented with targeted training.

Citing Articles

Estimating hip impact velocity and acceleration from video-captured falls using a pose estimation algorithm.

Michaels R, Barreira T, Robinovitch S, Sosnoff J, Moon Y Sci Rep. 2025; 15(1):1558.

PMID: 39789212 PMC: 11717977. DOI: 10.1038/s41598-025-85934-y.

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