Gait variability and fall risk in community-living older adults: a 1-year prospective study.

By Jeffrey M. Hausdorff, D. Rios, H. Edelberg. Archives of Physical Medicine and Rehabilitation, 2001

2,431 citations recorded by the source corpus.

Subfields: Geriatrics and Gerontology, Physical Therapy, Sports Therapy and Rehabilitation, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/bec63fa4b39ac9eee704989463f0104b0b7af44f

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Citations in the corpus

Normative Spatiotemporal Gait Parameters in Older Adults (2011, background): “Variability parameters used to examine these capacities have included variability in stride speed [20,21], stride time [18], stride length [4], step time [22], step length [22], swing time [4,23] and step width [22,24].”
Gait Analysis Using a Shoe-Integrated Wireless Sensor System (2008, background): “[4], [5] developed a system capable of detecting several of the temporal gait parameters with two FSRs positioned under the heel and in the general area under the toes and metatarsals, connected to a circuit board and battery pack worn on the ankle, which has been used to evaluate the likelihood of ”
Quantitative gait markers and incident fall risk in older adults. (2009, background): “The incremental validity from gait variables reported in this and other studies over traditional risk factors and clinical tests of gait and balance support further exploration of these measures ( 20 , 39 ).”
Dual tasking, gait rhythmicity, and Parkinson's disease: Which aspects of gait are attention demanding? (2005, methodology): “The following gait parameters were determined from the force record using previously described methods (Hausdorff et al., 2000; Hausdorff et al., 2001; Hausdorff et al., 2003; Schaafsma et al., 2003; Frenkel-Toledo et al., 2005): average stride time, swing time (%), stride time variability, and swin”
Kinematic variability and local dynamic stability of upper body motions when walking at different speeds. (2006, neutral): “Increased walking variability might indicate the presence of some other underlying pathology (Hausdorff et al., 2001a, b), or may indicate only that patients are compensating by slowing down (DeVita and Hortobagyi, 2000).”
Full body gait analysis with Kinect (2012, methodology): “We follow standard practice (see, for example, [4], [3], [11]) and define stride time as the time from initial contact”
Risk factors for falls among older adults: a review of the literature. (2013, background): “About one-third of community-dwelling older adults above age 5 fall every year [2,24] while 40% of those over age 80 experince one or more falls [25].”
Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review (2016, background): “dation, assessing fall risks, and preventing falls for the elderly [10], [11].”
The Promise of mHealth: Daily Activity Monitoring and Outcome Assessments by Wearable Sensors (2011, methodology): “For example, the coefficient of variation of stride-to-stride fluctuations can be assessed with an accelerometer on each ankle,(90) Other promising methods for modeling fluctuations in spatiotemporal and foot pressure measurements during stance and gait,(105,106) such as high stride and swing time v”
3D gait assessment in young and elderly subjects using foot-worn inertial sensors. (2010, background): “Furthermore, these measures do not allow evaluating specific spatio-temporal gait parameters that have been associated with frequent geriatric syndromes, such as falls, dementia, or frailty (Hausdorff et al., 2001; Kressig et al., 2004; Seematter-Bagnoud et al., 2009).”