When Speed Replaces Accuracy: The Problem with Fall Risk Screening in Healthcare

Introduction

Falls remain the leading cause of injuries in older adults. One in four adults over the age of sixty-five falls each year with many experiencing long-term consequences such as hip fractures or Traumatic Brain Injury (TBI), resulting in hospitalizations and loss of independence. In response, healthcare systems have long emphasized the importance of fall risk screening in clinical practice. At first glance, this seems appropriate: if clinicians can quickly identify those at risk, they can intervene before serious injury occurs.

Yet, fall rates in older adults have not dropped in the past decade. The prevalence still hovers around twenty-five percent, showing little progress despite widespread use of fall risk screening tools. This persistence raises concern that the tools most commonly relied upon are chosen not based on accuracy, but because of speed and ease for a busy clinic.

This emphasis on speed over quality raises an uncomfortable but important question. What is the value of a test that can be administered in seconds if its ability to truly identify those at risk is poor? Screening tests with low sensitivity fail to identify the very individuals they are designed to protect, while tests with low specificity overburden both patients and clinicians with false alarms. In practice, clinicians often try to compensate by layering multiple imperfect tests together, but this strategy frequently introduces its own problems and produces misleading results.

The Central Role of Sensitivity and Specificity

Before examining specific tools, it is important to review what sensitivity and specificity actually mean. Sensitivity measures the proportion of people who truly are at risk for falls who are correctly identified by a test. Specificity measures the proportion of people not at risk who are correctly classified as low risk. In practice, In practice, the ideal test would achieve both sensitivity and specificity ≥ 90%. A test at this level is highly accurate, meaning it rarely misses those at risk and rarely mislabels those who are not.

Low sensitivity means individuals who will eventually fall are missed. These patients leave the clinic with false reassurance, believing they are safe when in reality they remain vulnerable. Low specificity means that individuals who are not actually at significant risk are misclassified as high risk. This can lead to unnecessary referrals, additional testing, or even inappropriate interventions, wasting healthcare resources and creating anxiety for the patient.

Common Fall Risk Screening Tools

Timed Up and Go (TUG)

The Timed Up and Go test is perhaps the most widely used fall risk screening tool in the world. Its appeal lies in its simplicity. A patient sits in a chair, stands up when instructed, walks three meters, turns around, walks back, and sits down. The total time is recorded, and scores above a certain threshold are interpreted as indicators of fall risk.

Despite its popularity, the Timed Up and Go has serious limitations. A systematic review reported a pooled sensitivity of only 0.31 and a specificity of 0.74 (Barry et al., 2014). In other words, the test misses more than two thirds of individuals who are truly at risk of falling. While its specificity is moderately acceptable, the low sensitivity severely undermines its utility as a screening tool.

Studies in populations with vestibular dysfunction have shown somewhat better sensitivity, around 80 percent, but this improvement comes at the cost of poor specificity at only 56 percent (Whitney et al., 2004). This means that while more high risk patients are identified, nearly half of low risk individuals are incorrectly labeled as being at high risk.

These findings make it clear that the Timed Up and Go test, though celebrated for its speed and ease of use, is a blunt instrument. Its inability to reliably balance sensitivity and specificity calls into question its reputation as a dependable predictor of fall risk.

Dynamic Gait Index (DGI)

The Dynamic Gait Index was designed to evaluate a person’s ability to adapt gait to changes in task demands. Patients perform tasks such as walking with head turns, stepping over obstacles, and changing speed.

In one study of patients with vestibular dysfunction, using a cutoff score of 18 points or less, the DGI demonstrated sensitivity of 70 percent and specificity of 51 percent (Whitney et al., 2004). These numbers place it squarely in the moderate accuracy range.

5x Sit-to-Stand

Another widely used screening tool is the Five Times Sit-to-Stand Test (5XSTS), in which an individual is instructed to rise from a chair and sit back down five times as quickly as possible while the total time is recorded. The test is popular because it requires nothing more than a chair, can be completed in less than a minute, and appears to capture aspects of lower extremity strength and functional mobility.

However, when examined for its predictive accuracy in fall risk, the results are only moderate. In one study of adults over fifty, a cutoff of 13.93 seconds yielded a sensitivity of 73 percent and a specificity of 58 percent (Alqahtani et al., 2023). Another investigation using a cutoff of 11.64 seconds found sensitivity of 80 percent and specificity of 61.8 percent (Wang et al., 2022). A classic study by Buatois and colleagues (2008) identified a cutoff of 15 seconds with sensitivity of just 55 percent and specificity of 65 percent.

Taken together, these findings show that while the 5XSTS is simple and efficient, its ability to accurately distinguish those who will fall from those who will not is limited. Like the Timed Up and Go, it reflects the broader problem in traditional care where speed and practicality take precedence over diagnostic precision.

Mini-Balance Evaluation Systems Test (Mini-Best)

The Mini-Balance Evaluation Systems Test (Mini-BESTest) is a 14-item assessment developed to evaluate multiple domains of dynamic balance, including anticipatory postural adjustments, reactive postural control, sensory orientation, and gait stability. Compared to simpler screens like the TUG or the Berg Balance Scale, the Mini-BESTest demonstrates higher accuracy in distinguishing fallers from non-fallers.

In a cohort of community-dwelling older adults, a cutoff score of 16 out of 28 produced sensitivity of 85 percent and specificity of 75 percent, yielding an AUC of 0.84 and outperforming the TUG and Berg in predictive accuracy (Franchignoni et al., 2010; Godi et al., 2013). Studies in Brazilian older adults have identified age-specific thresholds, such as a cutoff of 25 points for those aged 60–69 (sensitivity 58 percent, specificity 74 percent, AUC 0.68) and 23 points for those aged 70–79 (sensitivity 66 percent, specificity 73 percent, AUC 0.74) (Magnani et al., 2019).

Another investigation of Brazilian women reported extremely high classification accuracy, with AUCs approaching 1.0 and both sensitivity and specificity above 92 percent, although these results may reflect smaller sample sizes and population-specific factors (Cunha et al., 2020). In neurological populations such as individuals with Parkinson’s disease, the Mini-BESTest has also shown predictive validity, ranking just behind instrumented measures of turning in its ability to forecast future falls, with an AUC of 0.69 (McKee et al., 2023).

While it requires more administration time than very rapid screens, the Mini-BESTest provides a more comprehensive and reliable view of balance impairments and fall risk across diverse populations.

Activities-Specific Balance Confidence (ABC) Scale

The Activities-Specific Balance Confidence (ABC) Scale is a self-reported questionnaire that measures an individual’s confidence in performing daily activities without losing balance. Unlike performance-based screens, the ABC provides insight into how a person perceives their own stability, which is itself a meaningful predictor of falls.

Research suggests that lower ABC scores are strongly associated with higher fall risk, but the tool’s accuracy varies depending on population and cutoff score. In older adults, a score below 67 percent has been shown to correctly classify fallers with an accuracy of about 84 percent (Lajoie & Gallagher, 2004; APTA NeuroPT, 2018). Among people with multiple sclerosis, an ABC-16 cutoff of 70 percent produced sensitivity of 71.7 percent and specificity of 86.5 percent, while the shorter ABC-6 with a 65 percent cutoff yielded sensitivity of 76.7 percent and specificity of 79.2 percent (Caronni et al., 2024).

In community-dwelling older adults, an ABC-6 cutoff of 60 percent achieved sensitivity of 70.8 percent and specificity of 84.3 percent (Chen et al., 2022). Other studies, however, have reported sensitivity as low as 41 percent even when specificity was high, particularly in subgroups such as older adults with hearing impairment (Alghwiri et al., 2018).

This wide variability highlights the limitations of relying solely on self-report measures: while the ABC Scale captures fear of falling and perceived balance, which are important risk factors, its predictive value is inconsistent across different groups and settings (Park et al., 2019).

STEADI Initiative

Another approach commonly used in primary care and community settings involves brief questionnaires, such as those included in the CDC’s STEADI initiative. A patient may be asked three “key questions,” such as whether they have fallen in the past year, whether they feel unsteady when standing or walking, and whether they worry about falling.

On the surface, such questionnaires appear attractive because they require no physical performance testing at all. However, the evidence for their accuracy is mixed. The three-question screen has demonstrated sensitivity of 68.7 percent and specificity of 57.9 percent. A longer twelve-question tool shows sensitivity of 55.7 percent and specificity of 75.9 percent (Lusardi et al., 2017).

The pattern is clear. These tools are designed for speed and minimal clinician burden, but they deliver only moderate accuracy.

Systematic Review Evidence

A systematic review of 29 fall risk tools concluded that most discriminated poorly between fallers and non-fallers, and that there was insufficient evidence to recommend any single tool as reliable for prediction (Gates et al., 2008).

Why Speed Is Prioritized Over Accuracy

If these tools are known to have limited accuracy, why do they remain so entrenched in clinical care? The answer lies in the culture of healthcare education and the operational pressures of clinical practice.

Physical therapy programs frequently highlight the speed of tests like the TUG. Students learn that it can be completed in less than a minute with no special equipment, which makes it ideal for busy outpatient clinics. Professors may demonstrate how to conduct the test quickly during class, emphasizing its ease of use while giving little attention to its low sensitivity. This creates an implicit value system where a test’s practicality outweighs its scientific validity.

In practice, clinicians face enormous time pressures. In outpatient rehabilitation or primary care, they may have only fifteen to twenty minutes with each patient. Under such constraints, a test that can be done quickly will always be favored over a more comprehensive assessment that requires time, equipment, and interpretation.

Clinical guidelines also contribute to the problem. Because tools like the TUG are easy to standardize and widely known, they are often included in fall prevention protocols. Even when evidence shows poor predictive power, tradition keep them in place. Thus, a cycle is perpetuated where convenience trumps accuracy.

The Pitfalls of Combining Multiple Tests

Recognizing the limitations of individual tools, many clinicians attempt to improve accuracy by combining several screenings. A patient may complete the Timed Up and Go test, answer a brief questionnaire, and perform the Dynamic Gait Index during the same visit. The hope is that using multiple measures will offset the weaknesses of each.

In practice, this strategy is less effective than it appears. When tools with low specificity are combined, the likelihood of false positives rises. Patients who are not truly at risk may be incorrectly flagged, leading to unnecessary referrals or interventions. On the other hand, when tools with low sensitivity are combined, some high-risk individuals may still slip through undetected, since the blind spots of each test overlap.

The problem is further compounded by inconsistent thresholds. For instance, TUG cutoff scores vary from 11.1 seconds to 13.5 seconds across studies (Barry et al., 2014). This variability means that classification often depends as much on the clinic’s chosen standard as on the patient’s actual performance. A patient deemed at risk in one setting might be considered safe in another.

From a statistical standpoint, sensitivities and specificities do not simply add together. Instead, they interact in complex ways depending on how results are interpreted. Unless a specific combination of tools has been validated in rigorous research, clinicians may inadvertently create an assessment battery that magnifies flaws rather than corrects them. The result is a landscape where patients are subjected to multiple quick but imprecise tests, producing both false reassurance and unnecessary concern.

Consequences for Patients and Clinicians

The real-world consequences of prioritizing speed over accuracy are significant. Patients who are incorrectly identified as low risk may not receive interventions such as balance training, strength conditioning, or home safety modifications that could prevent future falls. When they later experience a fall, the opportunity for prevention has been lost.

On the other hand, patients falsely classified as high risk may be referred for unnecessary therapy, prescribed mobility aids they do not need, or discouraged from activities they could safely enjoy. This can foster dependence, reduce quality of life, and waste limited healthcare resources.

Clinicians bear the burden as well. Managing false positives consumes time and energy that could be directed toward those truly at risk. It also undermines confidence in the tools themselves. When a therapist repeatedly sees patients misclassified, trust in the screening process erodes, yet the cycle of quick testing continues because there are few practical alternatives widely implemented.

Moviq Health

At Moviq Health, we are positioning ourselves as the accuracy-first alternative to traditional fall risk screening. Stopwatch-based tests and subjective observation have long been the norm in clinical care, but they leave too much room for error. A patient’s risk classification should never depend on how quickly a clinician can press stop or how consistently different therapists judge performance. These limitations explain why many of the most widely used screens report only moderate sensitivity and specificity.

Moviq Health takes a fundamentally different approach. By using fully instrumented equipment, we minimize human error and capture high-resolution biomechanical data that reflects the true quality of movement. Our system measures joint kinematics, force transfer, postural sway, and spatial-temporal parameters, the signals that research has already shown to be more predictive of falls than speed alone. This design is rooted in biomechanics, a field that has consistently outperformed traditional screeners in research studies, but has until now been largely confined to laboratories.

While Moviq Health is still in the process of building its validation dataset, our mission is clear: to deliver fall risk screening that is objective, reproducible, and clinically meaningful. We are aligning our platform with the gold standard benchmark of high sensitivity and high specificity, and we are actively working toward peer-reviewed validation to demonstrate that performance. By bridging the gap between biomechanics research and clinical practice, Moviq Health is setting a new standard. Our screening prioritizes accuracy over speed, giving providers confidence in their decisions and clients the trust that their results truly reflect their risk.

Shifting the Culture: Education and Clinical Priorities

For change to occur, both educational institutions and healthcare systems must shift their priorities. Physical therapy and medical programs should teach students not only how to perform tests like the TUG but also their limitations. Students should understand that a quick test is not necessarily a good test.

Clinicians should be encouraged to think critically about the tools they use and to seek out more accurate alternatives when possible. Healthcare systems should support this by providing time, training, and technology that allows for quality assessments rather than defaulting to what can be done in under a minute.

Ultimately, fall prevention is too important to be reduced to quick checklists. Accuracy must take precedence over speed.

Conclusion

Fall risk screening is meant to serve as an early defense against one of the most serious health threats facing older adults. Yet the tools most often used are selected more for their speed than their ability to accurately identify those at risk. Tests like the Timed Up and Go, the Dynamic Gait Index, 5x STS, ABC, and min-BEST, and STEADI offer convenience at the cost of reliability. Their low to moderate sensitivity and specificity mean that many patients are misclassified, with significant consequences for health, independence, and resource use.

Clinicians attempt to compensate by combining multiple imperfect tests, but this strategy frequently leads to false results. Patients who most need intervention may be missed, while others who are not at risk are subjected to unnecessary treatments.

The way forward lies in recognizing that speed should never replace accuracy in matters of patient safety. Education must shift to emphasize the importance of validity over convenience, and healthcare systems must support the adoption of more sophisticated, evidence-based assessment methods. Only then can fall risk screening fulfill its true purpose: protecting older adults from preventable harm and helping them maintain independence for as long as possible.

References

Alghwiri, A. A., Alghadir, A. H., & Whitney, S. L. (2018). The Arabic Activities-specific Balance Confidence (ABC) Scale for elderly with hearing impairment: Reliability and validity. BMC Geriatrics, 18(1), 258. https://doi.org/10.1186/s12877-018-0943-5

Alqahtani, B. A., Alenazi, A. M., Alshehri, M. M., Reddy, R. S., Samuel, P. S., & Kakaraparthi, V. N. (2023). The association between five times sit-to-stand test and falls in community-dwelling Saudi older adults. Saudi Medical Journal, 44(2), 149–155. https://pubmed.ncbi.nlm.nih.gov/36701043/

Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552. https://doi.org/10.1136/bmj.308.6943.1552

American Physical Therapy Association, Academy of Neurologic Physical Therapy (APTA NeuroPT). (2018). Core outcome measures: Activities-specific Balance Confidence Scale. Retrieved from https://www.neuropt.org

Barry, E., Galvin, R., Keogh, C., Horgan, F., & Fahey, T. (2014). Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: A systematic review and meta-analysis. BMC Geriatrics, 14, 14. https://doi.org/10.1186/1471-2318-14-14

Buatois, S., Miljkovic, D., Manckoundia, P., Gueguen, R., Miget, P., Vançon, G., Perrin, P., & Benetos, A. (2008). Five times sit to stand test is a predictor of recurrent falls in healthy community-living subjects aged 65 and older. Journal of the American Geriatrics Society, 56(8), 1575–1577. https://doi.org/10.1111/j.1532-5415.2008.01777.x

Caronni, A., Brichetto, G., Tacchino, A., Prosperini, L., & Solaro, C. (2024). Predictive validity of ABC-16 and ABC-6 for fall risk in people with multiple sclerosis. Neurological Sciences, 45(2), 529–537. https://doi.org/10.1007/s13760-024-02515-y

Centers for Disease Control and Prevention (CDC). (2021). Important facts about falls. https://www.cdc.gov/falls/facts.html

Chen, H., Wu, Y., Zhang, J., & Wang, J. (2022). Validation of the Activities-specific Balance Confidence Scale short form (ABC-6) in older adults. Clinical Interventions in Aging, 17, 1221–1232. https://doi.org/10.2147/CIA.S380921

Cunha, B. P., de Faria, C. R., Ferreira, L. B., Lima, N. M., & de Freitas, P. B. (2020). Accuracy of Mini-BESTest in identifying older women with a history of falls. Brazilian Journal of Motor Behavior, 14(3), 37–45. https://socibracom.com/bjmb/index.php/bjmb/article/view/354

Franchignoni, F., Horak, F., Godi, M., Nardone, A., & Giordano, A. (2010). Using psychometric techniques to improve the Balance Evaluation Systems Test: The Mini-BESTest. Journal of Rehabilitation Medicine, 42(4), 323–331. https://doi.org/10.2340/16501977-0537

Gates, S., Smith, L. A., Fisher, J. D., & Lamb, S. E. (2008). Systematic review of accuracy of screening instruments for predicting fall risk among independently living older adults. Journal of Rehabilitation Research and Development, 45(8), 1105–1116. https://doi.org/10.1682/JRRD.2008.04.0057

Godi, M., Franchignoni, F., Caligari, M., Giordano, A., Turcato, A. M., & Nardone, A. (2013). Comparison of reliability, validity, and responsiveness of the Mini-BESTest and Berg Balance Scale in patients with balance disorders. Physical Therapy, 93(2), 158–167. https://doi.org/10.2522/ptj.20120171

Lajoie, Y., & Gallagher, S. P. (2004). Predicting falls within the elderly community: Comparison of postural sway, reaction time, the Berg Balance Scale, and the Activities-specific Balance Confidence (ABC) scale for comparing fallers and non-fallers. Archives of Gerontology and Geriatrics, 38(1), 11–26. https://doi.org/10.1016/S0167-4943(03)00082-7

Li, W., Ke, S., & Zhang, J. (2022). Machine learning-based prediction of fall risk in older adults using gait and cognitive measures. Frontiers in Digital Health, 4, 869812. https://doi.org/10.3389/fdgth.2022.869812

Lusardi, M. M., Fritz, S., Middleton, A., Allison, L., Wingood, M., Phillips, E., Criss, M., Verma, S., Osborne, J., & Chui, K. K. (2017). Determining risk of falls in community dwelling older adults: A systematic review and meta-analysis using posttest probability. Journal of Geriatric Physical Therapy, 40(1), 1–36. https://doi.org/10.1519/JPT.0000000000000099

Magnani, P. E., Genovez, M. B., Porto, J. M., Zanellato, N. F., Alvarenga, I. C., Freire, R. C., & Rebelatto, J. R. (2019). Age- and sex-related differences in the performance of the Mini-BESTest and fall risk in Brazilian older adults. Brazilian Journal of Physical Therapy, 23(5), 440–446. https://doi.org/10.1016/j.bjpt.2018.09.007

McKee, K. E., Vallabhajosula, S., & Earhart, G. M. (2023). Clinical and instrumented measures of turning predict future falls in people with Parkinson’s disease. Frontiers in Neurology, 14, 1228302. https://doi.org/10.3389/fneur.2023.1228302

Park, S.-H., Lee, J. Y., & Yang, Y. (2019). Self-reported measures of balance confidence and fall risk: A systematic review. Archives of Rehabilitation Research and Clinical Translation, 1(1–2), 100003. https://doi.org/10.1016/j.arrct.2019.100003

Podsiadlo, D., & Richardson, S. (1991). The Timed Up & Go: A test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society, 39(2), 142–148. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x

Shumway-Cook, A., Brauer, S., & Woollacott, M. (2000). Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Physical Therapy, 80(9), 896–903. https://doi.org/10.1093/ptj/80.9.896

Wang, T., Wu, H., Chen, H., & Lin, H. (2022). Diagnostic value of the five times sit-to-stand test for sarcopenia in community-dwelling older adults. Maturitas, 163, 37–43. https://doi.org/10.1016/j.maturitas.2022.05.010

Whitney, S. L., Marchetti, G. F., Schade, A. I., & Wrisley, D. M. (2004). The sensitivity and specificity of the Timed “Up & Go” and the Dynamic Gait Index for self-reported falls in persons with vestibular disorders. Journal of Vestibular Research, 14(5), 397–409. https://pubmed.ncbi.nlm.nih.gov/15598995/