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“Just like fingerprints, every person’s walk pattern is different,” explains Julia Hania, OneStep Data Scientist. Now imagine a world in which your walk could talk – what would it say about you? Advances in gait analysis research are shedding light on the valuable insight providers can obtain from the way their patients walk, yet the implementation of thorough and standardized gait analysis protocols in treatment settings is lacking.
When it comes to fall prevention, identifying trends in gait that have shown a correlation with increased risk of falls is a necessity. A complete fall risk assessment must incorporate precise and ongoing gait analysis to identify changes in gait parameters and functional mobility so that providers can assess more accurately and intervene proactively.
Gait parameters to assess fall risk
Clinical research shows there are multiple gait parameters that are correlated with an increased risk for falls. The ability to objectively identify these key parameters and proactively notify a provider of a change status helps the clinician preempt a potential fall and create a treatment plan customized to address the factors that have influenced the change and risk profile. While gait can be complex, at OneStep, we help both patients and providers understand what someone’s walk is saying. We use clinically validated technology to support peer-reviewed research to detect and inform fall risk. The science is clear:
Gait speed
Decreased gait speed, particularly at or below 0.8 m/s, has been correlated with an increased risk of falls.1-6 It is also interesting to note one study demonstrated a U-shaped relationship between gait speed and falls, with gait speed below normal ranges correlated more so with indoor falls and gait speed above normal ranges correlated more so with outdoor falls in the older adult population.3
Increased stride length variability
Increased variability in stride length has been correlated with an increased risk of falls.4,5
Step length
There is research to suggest that those who have a history of falling take smaller steps, demonstrating decreased step length.2
Step length variability
Increased step length variability has been correlated with an increased risk of falls.2,4
Step time variability
Increased step time variability has been correlated with an increased risk of falls.4 An additional study supports this finding and observed that those who have a history of falling demonstrated increased step time variability.2
Double support stance variability
Variability in double support stance has been correlated with an increase in fall risk.4
Double support phase
There is a correlation between an increase in double support stance phase and fall risk.5 Additionally, those with a history of falling have demonstrated an increase in double support stance phase during the gait cycle.2
Cadence
There is a U-shaped correlation between gait speed and cadence, with the optimal range for cadence being 80-110 steps/minute. Research suggests that when values for cadence fall outside the optimal range, there is an increased risk of falls.4 Additionally, it is important to monitor the variability of cadence as well.
Gait speed and gait variability help identify fall risk
While there is not a definitive consensus on which gait parameters hold the absolute most importance when assessing fall risk, both gait speed and gait variability appear consistently throughout the literature and demonstrate a strong correlation with increased fall risk.
Gait speed
Gait speed is now considered the 6th vital sign and studies have shown it offers insight into functional mobility, frailty status, fall risk, and health outcomes in the older adult population.2,7 As mentioned above, a decline in gait speed has been correlated with an increased risk of falls. Specifically, a study in the Journals of Gerontology observed that “Each 10 cm/s decrease in gait speed was associated with a 7% increased risk for falls. Participants with slow gait speed (≤70 cm/s) had a 1.5-fold increased risk for falls compared with those with normal speed.”5 It is evident trends in gait speed should be monitored consistently and over time in order to identify changes indicative of fall risk and other health concerns.
Gait variability
Gait variability refers to how much a specific parameter deviates from its average, typically represented by a standard deviation from the mean. Variability can be measured in multiple parameters, such as the ones listed above. When observing variability, it is important to assess the gait parameter over time. If there is consistent variance greater than a standard deviation, or high variance, this is cause for concern. Increased gait variability may lead to instability during ambulation, decreased dynamic balance, and subsequently incidence of falls.4-6
Incorporating gait analysis into fall risk assessments
While observational gait analysis can provide some insight into the way your patient walks, it’s not enough for precise feedback on individual gait parameters. Today, a few different ways to perform a quantitative gait analysis exist.
Treadmills, mats, and walkways with sensors
One method to analyze gait is to walk over surfaces such as treadmills, mats, and walkways that utilize various types of sensors to collect gait parameter data. These devices are often large, cumbersome, and expensive. They are typically found in a lab setting, making them less accessible for regular gait analysis. They also are only able to collect data in a controlled, unnatural environment when the patient is aware they are being analyzed.
Wearables
Another method is the use of wearable sensors. These sensors can be as simple as a watch, wristband, or shoe. Wearables provide more access to gait analysis, but they can still be costly and are limited in the number of gait parameters they are able to detect at this time. The patient also has to remember to put the wearable on and ensure it is being used properly.
OneStep
OneStep’s innovative technology turns any smartphone into a 24/7 motion analysis lab that is able to accurately capture and analyze over 40 motion parameters, including gait variability. OneStep’s gait analysis has been validated against 4 gold-standard labs in leading medical centers8,9 and empowers providers with a convenient way to monitor a patient’s gait both in the clinic and during real-life circumstances with background analysis. This offers accurate gait analysis data over multiple points in time for a window into the patient’s everyday mobility.
When it comes to OneStep’s fall risk assessment capabilities, OneStep’s technology is able to detect changes in gait trends over time that might be indicative of increased risk for falls and notify providers of these changes so that they can intervene proactively. OneStep is revolutionizing the way in which providers are able to implement gait analysis into their fall risk assessments and prevention strategies.
Interested in leveraging OneStep’s innovative motion analysis technology to improve your fall risk assessment capabilities in your own practice?
Contact Gregg at gregg@onestep.co or Scott at scott@onestep.co with any questions and for a personalized demo.
References:
1. Middleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. J Aging Phys Act. 2015;23(2):314-322.
2. Kwon MS, Kwon YR, Park YS, Kim JW. Comparison of gait patterns in elderly fallers and non-fallers. Technol Health Care. 2018;26(S1):427-436.
3. Quach L, Galica AM, Jones RN, et al. The nonlinear relationship between gait speed and falls: the Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly of Boston Study. J Am Geriatr Soc. 2011 Jun;59(6):1069-73.
4. Callisaya ML, Blizzard L, et al. Gait, gait variability and the risk of multiple incident falls in older people: a population-based study. Age Ageing. 2011 Jul;40(4):481-7.
5. Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. J Gerontol A Biol Sci Med Sci. 2009 Aug;64(8):896-901.
6. Hausdorff, J. M., Edelberg, H. K., Mitchell, S. L., Goldberger, A. L. & Wei, J. Y. Increased gait unsteadiness in community-dwelling elderly fallers. 1997. Arch Phys Med Rehabil. 78, 278–283.
7. Jung HW, Jang IY, Lee CK, et al. Usual gait speed is associated with frailty status, institutionalization, and mortality in community-dwelling rural older adults: a longitudinal analysis of the Aging Study of Pyeongchang Rural Area. Clin Interv Aging. 2018 Jun 6;13:1079-1089.
8. Shahar RT, Agmon M. Gait Analysis Using Accelerometry Data from a Single Smartphone: Agreement and Consistency between a Smartphone Application and Gold-Standard Gait Analysis System. Sensors (Basel). 2021;21(22):7497.
9. Shema-Shiratzky S, Beer Y, Mor A, Elbaz A. Smartphone-based inertial sensors technology - Validation of a new application to measure spatiotemporal gait metrics. Gait Posture. 2022;93:102-106.
