Falls among older adults represent a significant public health concern, contributing to injuries, hospitalizations, and even fatalities. Consequently, considerable efforts have been invested in developing and implementing fall risk detection and prevention interventions. However, recent research suggests that while these interventions have shown some promise, their effectiveness remains only mildly effective.

Fall Risk Assessment Tools: A Flawed Landscape

Numerous fall risk assessment tools exist, ranging from simple questionnaires to complex biomechanical assessments. However, systematic reviews have consistently determined that these approaches are ineffective.

A systemic meta-analysis conducted by Gates et al. (2008) found that most of these tools discriminate poorly between fallers and non-fallers. The inconsistency and methodological variability across studies further complicate the identification of reliable tools. Similarly, Gade et al. (2021) noted a wide range in the discriminatory performance of prognostic models for falls, with all models exhibiting a high risk of bias, rendering them unreliable in clinical practice. Finally, Park’s meta-analysis (2018) of fall risk assessment tools concluded that existing tools lack sufficiently high predictive validity for differentiating between high and low fall risks.

These three comprehensive studies, and others like them, demonstrate fundamental flaws in our current approach to identifying individuals at risk of falls. Some believe part of the issue is the failure of fall risk detection approaches to include combined motor-cognitive challenges or assessments. For now, at least, this suggests that everyone over 65, and especially everyone over 80, should be considered a potential fall risk regardless of their cognitive capabilities, level of activity, or prior experiences with falling.

Fall Prevention Interventions: Varied Success and Limited Impact

A myriad of fall prevention interventions has been explored, including physical therapy (such as gait training), group classes (i.e. Tai Chi, Otago, etc.), balance interventions (such as perturbation training[1]), home-based exercises (including digital MSK applications), environmental modifications (i.e., grab bars, lighting, etc.), assistive devices (i.e., rollators, eyeglasses, etc.), and medication management. These interventions can be provided as a single component, multi-component[2], or multifactorial[3]. However, their effectiveness remains inconsistent, with many studies reporting only modest reductions in fall rates.

The Need for Real Solutions for Fall Prevention

The limitations of current fall risk detection and prevention interventions highlight the need for new and innovative approaches. Addressing the flaws in existing assessment tools, enhancing the implementation of interventions, and improving adherence rates are crucial steps in improving outcomes for older adults at risk of falls.

Increasingly, scientific research is showing that a specific type of gait training, called dual tasking or combined motor-cognitive training, is the most effective approach to fall prevention. This is based on the latest science that shows

• Cognitive capacity, specifically executive function, is key to successful walking^{[4],[5],[6],[7],[8],[9],}[10]^,[11],[12]
• Dual tasking, specifically cognitive training with motor exercises, can improve cognitive capacity because it initiates neural plasticity.^,[13]

Current fall prevention interventions are challenged to implement these best practices because they are constrained by exercises that do not correlate with the real world, training that is hard to quantify and of limited intensity, and activities that are so repetitive, unengaging, and unpersonalized that dropout rates can be as high as 50%.

For instance, Tinetti et al. (1994) implemented a multifactorial intervention in community-dwelling elderly individuals, including medication adjustments, environmental modifications, and exercise programs, resulting in only a 31% reduction in fall rates after 12 months. Similarly, Sherrington et al. (2020) found that various exercise programs, including balance and functional exercises, resistance training, and Tai Chi, led to reductions in fall rates ranging from 23% to 28%. However, the effects of interventions primarily involving resistance training, dance, or walking remain uncertain.

Furthermore, a Cochrane review by Hopewell et al. (2018) evaluated multifactorial and multiple component interventions and found that while multifactorial interventions reduced fall rates by 23%, they had little to no effect on falling, recurrent falls, or fall-related hospital admissions. Similarly, Bhasin et al. (2020) reported that a multifactorial strategy to prevent serious fall injuries had only a modest impact on the rate of fall injuries.

In summary, multiple peer-reviewed systemic reviews have found that all current standards of care for fall prevention are only mildly effective, with reported fall reduction rates averaging between 20 to 30% even when interventions were performed over a period of 12 months.

Adherence Challenges: A Barrier to Success

In addition to limited effectiveness, fall prevention programs face challenges related to adherence. Osho et al. (2017) conducted a meta-analysis of fall prevention exercise programs and found that average adherence in intervention groups was only 66%, with an overall attrition rate of 20%. Similarly, Nyman (2021) analyzed adherence data from Tai Chi trials and found median class attendance rates ranging from 63% to 81%. When adherence rates drop below 80%, some studies have even found the fall prevention ability can drop to single digits.

[1] Hausdorff JM, et. al.  Gait variability and fall risk in community-living older adults: a 1-year prospective study.  Arch Phys Med Rehabil 2001; 82: 1050-56.

[2] Van Iersel MB, Kessels RP, Bloem BR, Verbeek AL, Olde Riddert MG.  Executive functions are associated with gait and balance in community-living elderly people.  J Gerontol A Biol Sci Med Sci 2008; 63:1344-49.

[3] Rosso AL, et al.  Aging, the central nervous system, and mobility.  J Gerontol A Biol Sci Med Sci 2013; 68:1379-86.

[4] Amboni M, Barone P, Hausdorff JM. Cognitive contributions to gait and falls: evidence and implications. Mov Disord. 2013 Sep 15;28(11):1520-33. doi: 10.1002/mds.25674. PMID: 24132840; PMCID: PMC4119872.

[5] Mirelman A, Herman T, Brozgol M, Dorfman M, Sprecher E, Schweiger A, Giladi N, Hausdorff JM. Executive function and falls in older adults: new findings from a five-year prospective study link fall risk to cognition. PLoS One. 2012;7(6):e40297.

[6] Zhang W, Low LF, Schwenk M, Mills N, Gwynn JD, Clemson L. Review of Gait, Cognition, and Fall Risks with Implications for Fall Prevention in Older Adults with Dementia. Dement Geriatr Cogn Disord. 2019;48(1-2):17-29.

[7] Montero-Odasso M, Speechley M. Falls in Cognitively Impaired Older Adults: Implications for Risk Assessment And Prevention. J Am Geriatr Soc. 2018 Feb;66(2):367-375. doi: 10.1111/jgs.15219. Epub 2018 Jan 10. PMID: 29318592.

[8] Buracchio, T.J., et al. Executive function predicts risk of falls in older adults without balance impairment. BMC Geriatr 11, 74 (2011).

[9] Welmer AK, Rizzuto D, Laukka EJ, Johnell K, Fratiglioni L. Cognitive and Physical Function in Relation to the Risk of Injurious Falls in Older Adults: A Population-Based Study. J Gerontol A Biol Sci Med Sci. 2017 May 1;72(5):669-675.

[10] Li KZH, Bherer L, Mirelman A, Maidan I, Hausdorff JM. Cognitive Involvement in Balance, Gait and Dual-Tasking in Aging: A Focused Review From a Neuroscience of Aging Perspective. Front Neurol. 2018 Oct 29;9:913.

[11] Wongcharoen S, Sungkarat S, Munkhetvit P, Lugade V, Silsupadol P. Home-based interventions improve trained, but not novel, dual-task balance performance in older adults: A randomized controlled trial. Gait Posture. 2017 Feb;52:147-152.

[12] Tarasova I, Trubnikova O, Kukhareva I, Syrova I, Sosnina A, Kupriyanova D, Barbarash O. A Comparison of Two Multi-Tasking Approaches to Cognitive Training in Cardiac Surgery Patients. Biomedicines. 2023 Oct 18;11(10):2823.

[13] Droby A, Maidan I, Jacob Y, Giladi N, Hausdorff JM, Mirelman A. Distinct Effects of Motor Training on Resting-State Functional Networks of the Brain in Parkinson’s Disease. Neurorehabil Neural Repair. 2020 Sep;34(9):795-803. doi: 10.1177/1545968320940985. Epub 2020 Jul 18. PMID: 32684069.