Falls can cause injuries and even deaths among adults age 65 and older, making falls a serious threat to the health of older adults and their ability to remain independent1. While many falls do not cause injuries, one out of five falls leads to a serious injury such as a broken bone or head injury2. Furthermore, falling once significantly increases the likelihood of falling again3, and is also associated with limitations in daily living activities4, highlighting the importance of fall prevention in this population.
While falls are common in older adults, they can be preventable. Healthcare providers can play an important role in prevention by screening older adults for fall risk5. Gait measures such as stride time, step length, cadence, and gait-cycle variability have shown to be predictive of first-time falls3.
However, the technology needed to perform these assessments requires sufficient training, space, and can be quite costly. A simple, objective, and easy to administer test can help healthcare providers increase the effectiveness of identifying those at risk for falls and consequently implement prevention techniques.
ActiGraph launched the Digital Endpoint Accelerator Research “DEAR” Grant to help address these evidence gaps by partnering with clinical researchers on projects that examine the validity of digital measures as compared to established ground truth measures. One of the researchers receiving this grant is John Buckley, PhD, a researcher of movement biomechanics in the Faculty of Engineering and Digital Technologies at the University of Bradford, West Yorkshire, England.
Dr. Buckley teaches courses in clinical movement analysis, biomechanics, and rehabilitation engineering. His main area of research focus has been clinical biomechanics and human movement, particularly with individuals experiencing musculoskeletal issues and/or sensory decline.
While falls are multi-factorial and can occur during any event, because undertaking everyday activities requires having to negotiate frequent and multiple changes in terrain (e.g. steps, stairs and slopes) walking activity is commonplace for fall incidences. Measures of walking, or gait, ability are thus predictive of fall risk, but given the limitations in broadly implementing these measures in the free-living environment, Dr. Buckley and his team are investigating the role of digital health technologies in automating clinical evaluations of ankle function for assessing fall risk.
Ankle function provides a key contribution to everyday activities, particularly ascending and descending stairs, where many serious falls occur. Further, functional decline at the ankle that is associated with ageing can lead to an increased risk of falls.
With the DEAR Grant, Dr. Buckley and his research team will determine if actigraphy could be used to reliably measure the performance and variability of ankle function in older adults to help inform fall risk. A simple rising up-on-the-toes (UTT) test can be used to assess ankle strength, function, and endurance. Using actigraphy, the results of UTT tests can be quickly and objectively collected and can provide enhanced novel digital measures such as movement repeatability and consistency.
“Evidence generated from these studies should accelerate the adoption of wearable data as patient-centered outcomes, and may ultimately lead to improvements in healthcare and healthy ageing,” shared Dr. Buckley.
This research project is one of five that have been selected to receive the DEAR Grant, all of which will progress over the course of 2023-2024.
To learn more about digital measures of gait and balance collected from wearable digital health technologies, check out our Digital Health Monthly webinar, "Monitoring Activity and Gait in Children using DHTs.”
References
1 Center for Disease Control and Prevention. Older Adult Fall Prevention. Facts About Falls. https://www.cdc.gov/falls/index.html
2 Sterling DA, O'Connor JA, Bonadies J. Geriatric falls: injury severity is high and disproportionate to mechanism. J Trauma. 2001 Jan;50(1):116-9. doi: 10.1097/00005373-200101000-00021. PMID: 11231681.
3 König N, Taylor WR, Armbrecht G, Dietzel R, Singh NB. Identification of functional parameters for the classification of older female fallers and prediction of 'first-time' fallers. J R Soc Interface. 2014 Aug 6;11(97):20140353. doi: 10.1098/rsif.2014.0353. PMID: 24898021; PMCID: PMC4208368.
4 Stevens JA, Ballesteros MF, Mack KA, et al. Gender differences in seeking care for falls in the aged Medicare population. American Journal of Preventive Medicine. 2012 Jul;43(1):59-62. DOI: 10.1016/j.amepre.2012.03.008. PMID: 22704747.
5 Bergen G, Stevens MR, Burns ER. Falls and Fall Injuries Among Adults Aged ≥65 Years — United States, 2014. MMWR Morb Mortal Wkly Rep 2016;65:993–998. DOI: http://dx.doi.org/10.15585/mmwr.mm6537a2external icon.