Falls remain one of the most dangerous and costly events in healthcare, particularly among older adults and patients with neurological conditions. CDC researchers report that falls are the leading cause of fatal and nonfatal injuries among adults aged 65 and older, with 28.7% of older adults reporting a fall in 2014 (Bergen et al., 2016), and estimated the medical costs attributable to older-adult falls in the United States at roughly $50 billion in 2015 (Florence et al., 2018). Despite decades of awareness, many fall prevention programs still lean on periodic screenings and subjective risk questionnaires that can miss the subtle changes happening between visits.
The problem is timing. By the time a patient presents with a fall, the window for intervention has already closed. What clinicians need is the ability to detect deterioration before it becomes a crisis.
Objective movement analysis makes this possible. The underlying signals are well studied: in community-living older adults, greater stride-to-stride variability predicted who would fall over the following year (Hausdorff et al., 2001), and slower gait speed and greater gait variability were independent predictors of incident falls in a cohort of nearly 600 adults aged 70 and older (Verghese et al., 2009). By tracking metrics like postural sway, stride variability, and sit-to-stand transition times over multiple sessions, clinicians can identify downward trends that are hard to see with the naked eye. A slight increase in gait asymmetry or a gradual decline in balance scores becomes an actionable signal — not just a data point.
No single measure settles the question, though. A large systematic review of fall-risk assessment in community-dwelling older adults concluded that falls are multifactorial and that no single test is an accurate diagnostic tool on its own (Lusardi et al., 2017). Trends across several measures, read in clinical context, are more informative than any one number. (Published cut-offs for common tests are collected in our functional test reference values.)
At Kinetically, our platform is designed to surface these patterns automatically, giving providers the early warnings they need to adjust treatment plans, modify home environments, or escalate care before a fall occurs.
Prediction isn't about replacing clinical intuition. It's about giving clinicians the quantified evidence to act on what they already suspect — sooner.
Two quick, standardized screens from the CDC's STEADI toolkit are covered in more depth in the 4-Stage Balance Test and the 30-Second Chair Stand.
References
- Bergen G, Stevens MR, Burns ER. Falls and fall injuries among adults aged ≥65 years — United States, 2014. MMWR Morbidity and Mortality Weekly Report. 2016;65(37):993-998. doi:10.15585/mmwr.mm6537a2
- Florence CS, Bergen G, Atherly A, Burns E, Stevens J, Drake C. Medical costs of fatal and nonfatal falls in older adults. Journal of the American Geriatrics Society. 2018;66(4):693-698. doi:10.1111/jgs.15304
- Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Archives of Physical Medicine and Rehabilitation. 2001;82(8):1050-1056. doi:10.1053/apmr.2001.24893
- Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. Journals of Gerontology Series A. 2009;64(8):896-901. doi:10.1093/gerona/glp033
- Lusardi MM, Fritz S, Middleton A, et al. Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. Journal of Geriatric Physical Therapy. 2017;40(1):1-36. doi:10.1519/JPT.0000000000000099