Falls are the leading cause of injury and injury death in older adults, with 3 million older adults treated in emergency settings for fall-related injuries according to the CDC. The outcomes of falls take an emotional toll on individuals and their caregivers and place an economic burden on the healthcare ecosystem at large. Implementing holistic fall prevention solutions that take into consideration the multifactorial causes of falls is critical.

While fall risk assessments most commonly incorporate patient-reported outcome measures, balance tests, and lower extremity strength testing, gait analysis is not always considered – or considered objectively due to limited access to gait labs.

The correlation between gait and fall risk

Gait has been dubbed the sixth vital sign because there is a growing body of research that supports the use of gait as a valuable indicator of health status. Among this research are studies that indicate several gait parameters are correlated with an increased risk for falls.

For this reason, performing a thorough fall risk assessment should incorporate objective gait analysis over multiple points in time to establish a baseline and identify changes in gait parameters that can signal the need for intervention and hopefully prevent a fall from occurring. By capturing trends and change in patient status early on, providers can better tailor their interventions in a targeted and timely fashion.

 Identifying gait analysis trends that may indicate an increased risk for falls

The following changes in specific gait parameters have demonstrated a correlation with increased fall risk:

  • Decreased gait speed (at or below 0.8 m/s)1-6
  • Decreased step length2
  • Increased stride length variability4,5
  • Increased step length variability2,4
  • Increased step time variability2
  • Increased double support stance variability4
  • Increased double support phase5
  • The cadence that falls outside the optimal range of 80-110 steps/min4

 Remote gait analysis offers better patient insight

Traditionally, objective gait analysis has only been available in expensive lab settings, leaving providers to rely on observation analysis that is not well-quantified or standardized. Unfortunately, that means many patients never receive a proper gait analysis, leaving out important mobility insight that could be used to identify risk for falls proactively and enhance the delivery of remote care including remote therapeutic monitoring (RTM).

Luckily, OneStep fills this gap and is helping clinicians provide patient care with clinically-validated remote motion analysis technology. OneStep’s innovative science turns any smartphone into a 24/7 remote motion analysis lab that can accurately capture over 40 gait and motion parameters, including the ones that are correlated with increased risk for falls. OneStep’s remote gait analysis enables providers to deliver science-driven patient care by conveniently monitoring a patient’s gait in the clinic and during real-life circumstances using unique background analysis – without any wearables. OneStep provides accurate gait analysis data across multiple points over time for unprecedented insight into the patient’s everyday mobility that enhances the delivery of remote patient care and remote therapeutic monitoring.

OneStep notifies providers if gait analysis trends fall outside normal ranges so they can intervene proactively, revolutionizing the way providers can take part in upstream patient care and deliver fall prevention strategies with great impact.


1Keep on Your Feet—Preventing Older Adult Falls. CDC. Updated December 16, 2020. Accessed August 23, 2022.

2Middleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. J Aging Phys Act. 2015;23(2):314-322.

3Kwon 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.

4Quach 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.

5Callisaya 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.

6Verghese 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.

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A freelance Digital Health research analyst currently based out of Weybridge, Surrey, UK. I provide a range of services catering to the Health IT and healthcare industry. With over 18 years of work experience in the Health IT industry, I offer consultancy in Health IT system requirements and design, testing, user scenarios, user manual documentation, blog writing and more, as will be evident from my website www.digitalhealthwrite.com. I’m passionate about collaborating with physicians, startups, NGOs and educational institutes in the field of healthcare and Health IT to explore the options of resolving practical problems in healthcare with the help of technology. You can follow me at @DigiHealthWrite and @radhikabio28

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