Automatic quantification of tandem walking using a wearable device: New insights into dynamic balance and mobility in older adults

Natalie Ganz, Eran Gazit, Nir Giladi, Robert J. Dawe, Anat Mirelman, Aron S. Buchman, Jeffrey M. Hausdorff*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Wearable sensors are increasingly employed to quantify diverse aspects of mobility. We developed novel tandem walking (TW) metrics, validated these measures using data from community-dwelling older adults, and evaluated their association with mobility disability and measures of gait and postural control. Methods: Six hundred ninety-three community-dwelling older adults (age: 78.69 ± 7.12 years) wore a 3D accelerometer on their lower back while performing 3 tasks: TW, usual-walking, and quiet standing. Six new measures of TW were extracted from the sensor data along with the clinician’s conventional assessment of TW missteps (ie, trip other loss of balance in which recovery occurred to prevent a fall) and duration. Principal component analysis transformed the 6 new TW measures into 2 summary TW composite factors. Logistic regression models evaluated whether these TW factors were independently associated with mobility disability. Results: Both TW factors were moderately related to the TW conventional measures (r < 0.454, p < .001) and were mildly correlated with usual-walking (r < 0.195, p < .001) and standing, postural control (r < 0.119, p < .001). The TW frequency composite factor (p = .008), but not TW complexity composite factor (p = .246), was independently associated with mobility disability in a model controlling for age, sex, body mass index, race, conventional measures of TW, and other measures of gait and postural control. Conclusions: Sensor-derived TW metrics expand the characterization of gait and postural control and suggest that they reflect a relatively independent domain of mobility. Further work is needed to determine if these metrics improve risk stratification for other adverse outcomes (eg, falls and incident disability) in older adults.

Original languageEnglish
Pages (from-to)101-107
Number of pages7
JournalJournals of Gerontology - Series A Biological Sciences and Medical Sciences
Volume76
Issue number1
DOIs
StatePublished - 2021

Funding

FundersFunder number
National Institutes of HealthR01AG17917, R01NS78009, K25AG61254, RF1AG22018
National Institute on AgingR01AG056352

    Keywords

    • Aging
    • Disability
    • Gait
    • Mobility
    • Wearable sensors

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