Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2009 Sep 1.
Published in final edited form as: Geriatr Nurs. 2008;29(5):311–323. doi: 10.1016/j.gerinurse.2007.10.016

Simple Balance and Mobility Tests Can Assess Falls Risk When Cognition Is Impaired

Kathryn A McMichael 1, Joni Vander Bilt 2, Laurie Lavery 3, Eric Rodriguez 4, Mary Ganguli 5
PMCID: PMC2583332  NIHMSID: NIHMS75474  PMID: 18929180

Abstract

Simple balance tests can help assess falls risk, particularly in cognitively impaired elderly who have elevated falls risk and might not accurately recall previous falls.

In 2006, almost 500 million people worldwide were aged 65 years of age or older. The National Institute on Aging projects that by 2030, this number will increase to 1 billion and represent 1 of every 8 persons in the world.1 Health problems of the elderly therefore constitute rapidly growing public health concerns. Falls are a particularly prevalent health problem among the elderly and are associated with significant morbidity and mortality. In the United States, among those aged 65-74, falls cause 48% of unintentional nonfatal injuries, and 23.4% of unintentional fatal injuries.2 These proportions are even higher among those 75 years and older, in whom falls make up 71.2% of unintentional nonfatal injuries and 39.3% of unintentional fatal injuries.2 More than a third of the population 65 years of age or older falls each year, and half of these falls are recurrent.3 As the U.S. population ages, the total direct cost for falls is expected to be $43.8 billion by the year 2020.4

Although falls are a preventable source of morbidity, health care utilization, and functional decline in older adults, only 34% of elderly patients receive any type of fall evaluation.5,6 In 2001, an expert panel on falls prevention7 recommended that all older patients in all health care settings be asked whether they have fallen in the past year, with affirmative responses triggering mobility testing using a measure such as the Get Up and Go (GUG) test.8,9 New guidelines, updated in 2006 but still unpublished, recommend screening questions and risk assessment followed by multifactorial assessment in those identified as at risk for falls.10 One key quality measure recommended in the Physician Quality Reporting Initiative (PQRI), published by the Centers for Medicare and Medicaid Services in 2007, is the screening of all individuals aged 65 and over for falls risk in all health care settings.11

Although hospitalized elderly appear to be at the highest risk of falls (2.9 to 13 falls per 1000 bed days),12,13 there is growing consensus that falls risk should also be assessed routinely in ambulatory care settings. There is also increased emphasis on recognizing subgroup differences in falls risk, for example, between community-dwelling persons and long-term care residents, and between cognitively intact and cognitively impaired elderly.10

Falls risk factors in the elderly include common age-associated deficits such as decreased mobility, visual impairments, arthritis, depressive symptoms, orthostasis, cognitive impairment, gait or balance impairment, muscle weakness, and the use of a higher number of prescription medications, with varying risks associated with individual classes of drugs.3,14-16

The ultimate purpose of assessing falls risk is, of course, to reduce falls and injury rates by incorporating safety interventions and adequate protective strategies into the nursing plan of care.17 As noted by Kelly et al.,15 nurses play a pivotal role in developing fall prevention strategies. In many care settings, nurses assess falls risk by asking patients about previous falls and risk factors for falls. In institutional settings, for example, patients are placed on “fall alert” if the assessment, which usually includes possible cognitive impairment, indicates risk of falling. However, in all health care settings, self-reported information can be inaccurate in older adults with cognitive impairment, and often patients are not accompanied by an informant who can provide corroborating information. Thus, brief objective tests of gait and balance could usefully complement the routine nursing assessment of the elderly patient.

The ideal assessment tool should be easy, quick, reliable, and accurate for identifying patients at risk.15 The Romberg test18 and the GUG test8,9 are familiar tools for identifying balance impairments and detecting falls risk. The Romberg sign for sensory ataxia was initially described by Moritz Heinrich von Romberg19 as a means of detecting neurosyphilis. It is now more generally used to determine abnormalities of proprioception (ie, joint position sense or sense of posture).19 For this test, the patient stands with feet together and arms by the side for 15 seconds to observe for sway or break in position. Because impaired proprioception can be overcome by visual or vestibular feedback,19 the Romberg test is performed first with both eyes open and then with eyes closed. The GUG test8 assesses gait and balance abnormalities by having the patient rise from a chair, walk a designated distance, and return to the chair and be seated. Further details are provided under Methods. We used the original nontimed version of the GUG test, because balance and mobility, rather than motor speed, were the focus of our inquiry. This version provides a satisfactory clinical measure of balance for the elderly population,8 although it has been subsequently modified in various ways by other researchers, including the creation of a timing component.9,14,20

We tested the hypotheses that scores on the Romberg and GUG tests would be associated with falls risk, after adjusting for potential confounders, in older adults with and without cognitive impairment, within a clinical epidemiology study of a primary care population. If the hypothesized associations were present, nurses can use these tests to identify at-risk patients objectively with the goal of preventing future falls, particularly when a reliable history of previous falls cannot be obtained.

Methods

Design

Sampling and recruitment

The Steel Valley Seniors Survey21 was conducted in an economically depressed small-town region of Southwestern Pennsylvania. Fifteen primary care physicians in the general vicinity provided access to their patients and medical records, contingent on informed consent from the patients. These physicians were not a random sample but rather a subgroup of interested area physicians whose practices included considerable numbers of elderly patients. All patients aged 65 or older visiting participating physicians during the 2-year recruitment period were requested by office staff to consider talking with the researchers. All who agreed were interviewed by research staff who recruited them into our study, obtaining written informed consent using procedures approved by the University of Pittsburgh Institutional Review Board. Participants who could not understand the consent form were deemed unable to consent by the research nurse.

Baseline data collection

Data were collected by 6 trained research nurses with previous clinical geriatrics experience in either home health or hospice care. Training by the study geriatrician (ER) included thorough instruction and supervised practice in simple balance and mobility testing. At the office visit, each participant provided the nurse with basic demographic and contact information and underwent cognitive screening with the Mini-Mental State Examination (MMSE).22,23 Consent was also obtained for participants to be contacted at home if selected for further assessment. Those with MMSE scores at or below the standard clinical cut-point of 24 were contacted by telephone to request an in-home visit for a more detailed assessment. For comparison, the same assessment was provided to a randomly selected subgroup of participants, with MMSE scores of 25 or higher, who had visited the same physician within the same month as a participant with MMSE <24.

The baseline in-home assessment consisted of a standardized semistructured interview acquiring information related to health history (including falls history), medication use, a brief general physical, and neurological examination (including balance and mobility assessment), among other items (see Measures).

Follow-up data collection

All surviving participants who had baseline home visits were also invited to undergo annual follow-up home visits during which the entire assessment was repeated. For these analyses, subjects were considered lost to follow-up if they completed the baseline home visit but not the next 2 follow-up annual assessments.

Measures

Outcome variable—Falls History

The outcome of interest for this report was participants’ self-reports to the nurse regarding falls during the preceding year, at the baseline home visit, and at the subsequent 2 annual assessments. Based on this history, we divided the sample into 2 groups: those reporting no falls and those reporting 1 or more falls. From a clinical perspective, falls prevention strategies do not differentiate between single and multiple falls because any one fall can have a debilitating effect on an individual’s health.

Primary Explanatory Variables: Balance and Mobility Measures

Romberg Test

As summarized in Box 1, the nurse instructed the participant to stand with feet together, arms at the side, and eyes open while the nurse observed for substantial postural sway or break in position, after being reassured that the nurse would stand close by to catch the individual should he or she start to fall. Next, the nurse asked the participant to maintain that position and close his or her eyes for 15 seconds. Individuals with normal balance may sway slightly upon closing their eyes, but it is usually minimal, and they do not break position. The Romberg sign is present, that is, the test is positive (abnormal) if the sway is considerable and the participant breaks position.19 Participants unable to stand with feet together with eyes open were considered untestable. For the current analyses, we combined the Romberg positive and untestable groups as “abnormal Romberg” test because, according to Bickley,24 the inability to stand with feet together either with eyes open or closed could be indicative of cerebellar ataxia and places these subjects at risk for falling.

GUG Test

The nurse first identified the most unobstructed area in the home where the participant could walk for 10 feet, measuring and marking the distance on the floor with tape. The participant was instructed to sit back in a firm upright chair and then rise from it, without using his or her arms for support, so that leg strength could be assessed. The participant then walked for 10 feet before returning to the chair, so that gait abnormalities such as shuffling, ataxia, or apraxia could be observed. Participants who could not perform the full task, who needed their arms to rise, or who used assistive devices to walk were coded as abnormal. Reliability in administration and scoring of the balance tests was established among the research nurses by practice and supervision during training and reestablished annually to prevent drift.

Covariates

Additional variables analyzed in relation to reported falls included demographic characteristics (age, sex, education) and cognitive functioning (MMSE score categorized as <25 vs. ≥25). We also examined 2 measures of overall morbidity: self-rated health (categorized as “excellent,” “very good,” or “good” versus “fair” or “poor”) and total number of regularly taken prescription medications. Both are robust measures that correlate highly with morbidity25 and mortality.26 Number of medications is also associated with falls risk.3,15,16 These covariates represent potential confounders that could distort the observed relationship between balance and mobility tests and falls if not controlled for in our multivariable models.

Statistical Methods

Data were analyzed using SAS.27 The 2 falls groups (no falls, ≥1 fall), 2 Romberg groups (abnormal, normal), and 2 GUG groups (abnormal, normal) were first compared on their demographic and health characteristics using chi-square tests for categorical variables and t tests for continuous variables. An a priori P value of <.05 was considered statistically significant.

Logistic regression models were established to examine both cross-sectional and longitudinal associations. The outcome variable was baseline falls (during preceding year, as reported at baseline) in the cross-sectional models, and cumulative future falls (falls reported at first and second follow-up, combined) in the longitudinal models. The explanatory variables were baseline Romberg and GUG, each of which were first examined separately and then in a model adjusted for age, sex, education, and self-rated health. The longitudinal analyses, which modeled future falls, also included baseline falls as a covariate.

To explore further the association among cognitively impaired participants, we repeated these analyses using dummy variables to create 4 possible combinations of the 2 variables: Romberg (normal and abnormal) versus higher MMSE (≥25) and lower MMSE (<25). A similar analysis was conducted for GUG (normal and abnormal) versus higher MMSE (≥25) and lower MMSE (<25). Odds ratios and 95% confidence intervals were calculated. Given concerns that cognitively impaired individuals might not report falls accurately, a post hoc analysis was conducted to examine the relationship of MMSE (higher vs. lower) with abnormal Romberg and GUG tests among those who did not report falls.

We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for Romberg and GUG for the 2 groups of future fallers (no falls, ≥1 fall) to determine the ability of these 2 tests to predict falls. These were calculated as follows: Sensitivity [True Positives / (True Positives + False Negatives)], or the proportion of those who had at least 1 fall in the following 2 years who had an abnormal test result at baseline; Specificity [True Negatives / (True Negatives + False Positives)], or the proportion of those who did not fall in the following 2 years who had a normal test at baseline. In the more clinically relevant situation of assessing fall risk, predictive values provide the probability that falls will or will not occur. Thus, we calculated the Positive Predictive Value [True Positives / (True Positives + False Positives)] or the probability of future falls when the baseline test is abnormal, and Negative Predictive Value [True Negatives / (False Negatives + True Negatives)], or the probability of no future falls when the baseline test is normal.

Results

Overall Sample Characteristics

Of 1107 primary care patients aged 65 years or older screened in the primary care offices, 358 received the detailed in-home clinical assessment, as reported previously in detail.21 Women constituted 68.7% of the sample, and 66.8% had at least a high school education or the equivalent (GED). Their mean (SD) age was 77.5 (SD 6.7; range 65-95 years) (see Table 1).

Characteristics of Fallers

At baseline, of the 358 participants, 112 (31.3%) reported falling at least once during the preceding year. The range of reported falls was 1-7, except for 1 outlier who reported falling 30 times because of a balance problem following a stroke. Compared with those without reported falls, the fallers had significantly greater proportions with abnormal Romberg tests and abnormal GUGs, were less educated, and had a greater proportion taking 4 or more prescription medications at the time of the assessment (Table 1).

Characteristics of Participants with an Abnormal Romberg Test

Compared with the normal Romberg test group, participants with an abnormal Romberg test results were significantly older, included a greater proportion of women, and were less likely to have at least a high school education. They were also significantly more likely to have abnormal GUG tests, to score <25 on the MMSE, to rate their health as fair or poor, to be taking 4 or more prescription medications, and to report falls (Table 2).

Characteristics of Participants with an Abnormal GUG Test

The group with abnormal GUG results was significantly older and more likely to be female than the normal GUG group. Unlike the Romberg groups, the 2 GUG groups did not differ in education. The abnormal GUG group had significantly higher proportions with abnormal Romberg results and with scores <25 on the MMSE. They were also more likely to rate their health as “fair” or “poor,” to be taking 4 or more prescription medications, and to report falls more often than the normal GUG group (Table 2).

Cross-Sectional Analyses

Logistic regression models used baseline falls (ie, self-reported falls during the preceding year) as the outcome variable and either the Romberg or the GUG test as the independent variable. Both abnormal Romberg and GUG tests were significantly associated with concurrent falls, even when adjusted for age, sex, education, and self-rated health (Table 3).

Longitudinal Analyses

Baseline Romberg and GUG tests as predictors of future falls

In longitudinal analyses, baseline self-report of falling was a significant predictor of future falls (ie, falls reported at the next 2 annual assessments). In univariate analyses, abnormal baseline Romberg and GUG tests were both significant predictors of future falls over the subsequent 2 years. In the multiple logistic regression models, after adjusting for previous falls (at baseline), sex, age, education, and self-rated health, an abnormal baseline GUG remained a significant predictor for future falls, but an abnormal baseline Romberg test did not (Table 3).

Prediction of Future Falls in Cognitively Impaired and Unimpaired Subgroups

Logistic regression models were fitted with 4 possible combinations of 2 variables: Romberg (normal and abnormal) and MMSE (low and high). A similar model was fitted with the GUG test in place of the Romberg. Having either an abnormal Romberg or an abnormal GUG was a significant predictor of future falls in the low MMSE group. However, after controlling for age, sex, education, self-rated health, and previous falls, only the GUG test remained a significant predictor of future falls in the low MMSE group (Table 4).

Relationship of cognition to GUG and Romberg tests

The preceding results raised the question of whether cognitively impaired individuals were failing to report falls accurately. A post hoc analysis was conducted, limited to participants who did not report falls, examining the relationship of MMSE (high vs. low) with abnormal Romberg and GUG tests. Among these 246 reported nonfallers, both balance and mobility tests were abnormal in a significantly higher proportion (23.7%) of those with low MMSE scores (<25) than the 10.1% of those with normal MMSE scores (chi-square 5.11, P = .02).

Sensitivity, specificity, positive and negative predictive values: These were calculated for no falls versus any falls over the subsequent 2 years, in relation to baseline Romberg and GUG tests. For future falls over the next 2 years, although the 2 balance tests had similar sensitivity, the GUG had higher specificity, positive predictive value, and negative predictive value than the Romberg (Table 5).

Discussion

In a sample of 358 ambulatory primary care patients aged 65-95 years, 2 brief, simple assessments of balance and mobility, the Romberg and GUG tests, were each significantly associated with falls reported both concurrently and over the subsequent 2 years. Although sensitivity of both tests was low, specificity and predictive value of each of them was high enough to warrant consideration as a nursing assessment tool to help identify those at risk for future falls.

A widely quoted article by Tinetti3 covers the scope of the clinical problem, the evidence on risk factors, assessment and interventions, guidelines, and areas of uncertainty. A comprehensive review of the literature on falls risk factors and risk assessment is not warranted here, given excellent reviews already published.3,12,15,28 The 2001 guidelines encourage a distinction between modifiable and nonmodifiable risk factors,7 some of which may be considered epiphenomena of the underlying conditions causing falls but could still be useful in identifying those at risk. A systematic literature review12 revealed few prospectively validated studies of falls risk. Frequently reported risk factors include gait instability3,12 balance problems,3 previous falls,12 cognitive impairment,3,29 agitated confusion,12 urinary frequency and incontinence,12 daytime somnolence,30 and overall number of prescription drugs.12,16 Specific drugs and drug classes implicated include digoxin, diuretics, class 1A antiarrhythmics,3,16 sedative-hypnotics,12 antidepressants, neuroleptics, benzodiazepines, and anticonvulsants,3 although the literature on drug groups is inconsistent.12

The literature includes a variety of assessment tools for falls risk. The original nontimed GUG was found clinically satisfactory and had good reliability and validity.8 Other versions include the timed GUG,9,14 which had good reliability, and the expanded GUG,20 which was sensitive but time-consuming and required a multimemory stopwatch. Other tests include the Functional Reach9,13,14 and Morse Fall Scale, although nurses’ clinical judgment has been found as helpful at predicting falls as either of these.13 Current guidelines3,10 do not recommend any particular test over the others but emphasize the need for multilevel, multifactorial assessment (ie, a history of falls followed by simple balance testing followed by a more detailed assessment). Oliver12 suggested that we need either to develop better screening tools for routine use with all older adults or pay closer attention to the common risk factors to identify those patients with the greatest risk. Cognitive function is mentioned repeatedly as a risk factor for falls and as a factor to consider in falls risk assessment.3,12,29

Our study extends the current literature on balance and mobility tests in falls risk assessment by examining the role of cognitive impairment in this relationship. Those with cognitive impairment may have poor recollection of their own falls history.31 In an ambulatory or other health care setting where cognitive status is not always known, and when a reliable informant is not always available, the routine use of these quick tests in nursing assessments could improve falls risk assessment and thus potentially reduce the number of falls. In our study, the GUG test remained a significant predictor for future falls in the cognitively impaired (low MMSE) group after adjusting for potential confounders. However, the GUG test requires both more time and more instruction to perform than the Romberg. Also, those with cognitive impairment may have more motor and praxis deficits as well, making the GUG the more challenging test for this group.

Despite the association of reported falls with abnormal results on these tests, we emphasize that a normal balance and mobility test does not imply absence of falls risk. Test results should be interpreted in the context of the full nursing assessment, with all relevant individual characteristics taken into consideration. The purpose of identifying patients with elevated falls risk is to target them for interventions to prevent falls, not to dismiss the possibility of falls risk in other patients.

Our finding that those taking many (4 or more) medications were at a greater risk of falling than those taking fewer medications is consistent with that of an earlier meta-analysis.16 This finding could reflect balance problems caused by specific drugs, by overmedication, or by the conditions for which the multiple medications are prescribed.

Our study participants were ambulatory primary care patients assessed in their homes, and our results may therefore not generalize completely to older adults in other settings, such as acute and long-term care. Similarly, results from our small-town Pennsylvania community sample of predominantly white older adults may not be typical of other populations. Our nontimed version of the GUG test is not directly comparable to the more standard version14,20,32 because our focus was not on speed of task completion.

Falls were assessed by self-report, and participants with poor recall may have underreported their previous falls. We explored this possibility in post hoc analyses limited to those with low MMSE scores who had reported no falls. Our finding that a substantial proportion of these cognitively impaired patients had abnormal mobility tests suggests that they may in fact have underreported falls. However, this only reinforces our interpretation that the use of objective mobility tests may be particularly valuable in cognitively impaired patients. thus, a nurse should not rely on self-reporting of falls history in elderly patients especially if cognitive impairment is suspected. Although cognitive impairment alone should alert the nurse to place this patient on fall risk alert, cognition itself is quite often not assessed at the time falls risk assessment is typically performed.

The purpose of falls risk assessment is to prevent future falls. A review of prevention strategies is beyond the scope of this article, but similar to falls risk assessment, effective intervention strategies are multifactorial and based on the risks identified in the given individual.33,34 Components of multifactorial intervention strategies can include health and environmental screening, exercise training, environmental modification, and withdrawal of culprit drugs.14,28,33 Exercise training programs have received particular attention in the literature with a previous meta-analysis demonstrating a 12% reduction in fall risk with exercise.35 Several specific exercise programs that have reduced falls risk are listed in Table 6. However, the effectiveness of physical training for falls prevention in cognitively impaired older adults is less clear.36

Baker5 reported that barriers to falls risk management included lack of awareness of fall morbidity and preventability, inadequate referral patterns, perceived lack of expertise and Medicare coverage, and lack of a federal mandate. The recently published PQRI parameters11 should help to address the latter.

Nurses, whether working in the community or in the hospital or nursing home, play an important role in prevention of falls in the elderly.15 Prevention of falls among elderly individuals with cognitive impairment who are a particular high risk for falls needs to remain a research priority.45 In the current climate of increasing emphasis on quality assurance and patient safety among an ever-expanding geriatric population, 2 simple mobility tests offer a potential approach to improving patient outcomes. Future research should determine prospectively whether the Romberg or GUG tests (or both) are useful additions to the nursing assessment in the acute and long-term care settings to identify fall risk and whether improved detection actually reduces the number of falls.

ACKNOWLEDGMENTS

Supported in part by Grant Nos. R01 AG16705 and K24 AG022035 from the National Institute on Aging, National Institutes of Health, United States Department of Health and Human Services. The authors thank Yangchun Du, MS, for assistance with statistical analysis. They also gratefully acknowledge the contributions of the SVSS project staff, cooperating physicians and their office staff, and our SVSS participants, whose dedication to our project was essential to its success.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Kathryn A. McMichael, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA.

Joni Vander Bilt, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.

Laurie Lavery, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.

Eric Rodriguez, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.

Mary Ganguli, Department of Psychiatry, University of Pittsburgh School of Medicine, and the Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA.

References

  • 1.National Institute on Aging . Why Population Aging Matters. A global perspective. U.S. Department of Health and Human Services; Washington, DC: Mar, 2007. NIH Publication No. 07-6134. [Google Scholar]
  • 2.Vyrostek SB, Annest JL, Ryan GW. Surveillance for fatal and nonfatal injuries—United States, 2001. MMWR. Surveillance Summaries: Morbidity & Mortality Weekly Report. Surveillance Summaries/CDC. 2004;53:1–71. [PubMed] [Google Scholar]
  • 3.Tinetti M. Preventing falls in elderly persons. New Engl J Med. 2003;348:42–9. doi: 10.1056/NEJMcp020719. [DOI] [PubMed] [Google Scholar]
  • 4.Centers for Disease Control and Prevention . Preventing Falls among Older Adults. CDC; Atlanta: 2006. Available at www.cdc.gov/ncipc/duip/preventadultfalls.htm. Cited July 30, 2007. [Google Scholar]
  • 5.Baker D, King M, Fortinsky R, et al. Dissemination of an evidence-based multicomponent fall risk-assessment and management strategy throughout a geographic area. J Am Geriatr Soc. 2005;53:675–80. doi: 10.1111/j.1532-5415.2005.53218.x. [DOI] [PubMed] [Google Scholar]
  • 6.Wenger NS, Solomon DH, Roth CP, et al. The quality of medical care provided to vulnerable community-swelling older patients. Ann Int Med. 2003;139:740–7. doi: 10.7326/0003-4819-139-9-200311040-00008. [DOI] [PubMed] [Google Scholar]
  • 7.American Geriatrics Society, British Geriatrics Society, and American Academy of Orthopaedic Surgeons Panel on Falls Prevention Guideline for the prevention of falls in older persons. J Am Geriatr Soc. 2001;49:664–72. [PubMed] [Google Scholar]
  • 8.Mathias S, Nayak U, Isaacs B. Balance in elderly patients: The “Get-up and Go” test. Arch Phys Med Rehab. 1986;67:387–9. [PubMed] [Google Scholar]
  • 9.Huang H-C. A checklist for assessing the risk of falls among the elderly. J Nurs Res. 2004;12:131–41. doi: 10.1097/01.jnr.0000387496.39675.7f. [DOI] [PubMed] [Google Scholar]
  • 10.Rubenstein L. Evidence-based practice guideline for the prevention of falls in older persons; Panel presentation at the American Geriatrics Society 2006 Annual Meeting; Chicago IL. May 2006; session 0423.Cited in Williams ME. Updated Practice Guidelines for the Prevention of Falls in Older Persons from the AGS and BGS. Medscape. 2006 Available at www.medscape.com/viewarticle/532942. Cited July 30, 2007.
  • 11.Centers for Medicare and Medicaid Services Physician Quality Reporting Initiative. 2007 Available at www.cms.hhs.gov/apps/ama/license.asp?file=/PQRI/Downloads/PQRIMeasuresList.pdf. Cited July 27, 2007.
  • 12.Oliver D, Daly F, Martin F, et al. Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review. Age Ageing. 2004;33:122–30. doi: 10.1093/ageing/afh017. [DOI] [PubMed] [Google Scholar]
  • 13.Eagle DJ, Salama S, Whitman D, et al. Comparison of three instruments in predicting accidental falls in selected inpatients in a general teaching hospital. J Gerontol Nurs. 1999;25:40–5. doi: 10.3928/0098-9134-19990701-14. [DOI] [PubMed] [Google Scholar]
  • 14.Robinson BS, Gordon JM, Wallentine SW, et al. Effectiveness of physical therapy intervention in decreasing the risk for falls in a community-dwelling aging population. Orthop Nurs. 2002;21(55):57–69. [Google Scholar]
  • 15.Kelly A, Dowling M. Reducing the likelihood of falls in older people. Nurs Standard. 2004;18:33–40. doi: 10.7748/ns2004.08.18.49.33.c3672. [DOI] [PubMed] [Google Scholar]
  • 16.Leipzig RM, Cumming RG, Tinetti ME. Drugs and falls in older people: a systematic review and meta-analysis: II. cardiac and analgesic drugs. J Am Geriatr Soc. 1999;47:40–50. doi: 10.1111/j.1532-5415.1999.tb01899.x. [DOI] [PubMed] [Google Scholar]
  • 17.Morse JM. Enhancing the safety of hospitalization by reducing patient falls. Am J Infect Control. 2002;30:376–80. doi: 10.1067/mic.2002.125808. [DOI] [PubMed] [Google Scholar]
  • 18.Lanska DJ, Goetz CG. Romberg’s sign: development, adoption, and adaptation in the 19th century. Neurology. 2000;55:1201–6. doi: 10.1212/wnl.55.8.1201. [DOI] [PubMed] [Google Scholar]
  • 19.Gokula KA. Romberg’s test. J Postgrad Med. 2003;49:169–72. [PubMed] [Google Scholar]
  • 20.Wall JC, Bell C, Campbell S, et al. The timed get-up-and-go test revisited: measurement of the component tasks. J Rehab Res Dev. 2000;37:109–14. [PubMed] [Google Scholar]
  • 21.Ganguli M, Rodriguez E, Mulsant B, et al. Detection and management of cognitive impairment in primary care: The Steel Valley Seniors Survey. J Am Geriatr Soc. 2004;52:1668–75. doi: 10.1111/j.1532-5415.2004.52459.x. [DOI] [PubMed] [Google Scholar]
  • 22.Folstein M, Folstein S, McHugh P. “Mini Mental State.” A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  • 23.Ganguli M, Du Y, Rodriguez EG, Mulsant BH, et al. Discrepancies in information provided to primary care physicians by patients with and without dementia: The Steel Valley Seniors Survey. Am J Geriatr Psychiatry. 2006;14:446–55. doi: 10.1097/01.JGP.0000199340.17808.77. [DOI] [PubMed] [Google Scholar]
  • 24.Bickley LS, Hoekelman RA. Bates guide to physical examination and history taking. 7th ed. Lipincott; Philadelphia: 1999. p. 585. [Google Scholar]
  • 25.Mulsant BH, Ganguli M, Seaberg EC. The relationship between self-rated health and depressive symptoms in an epidemiological sample of community-dwelling older adults. J Am Geriatr Soc. 1997;45:954–58. doi: 10.1111/j.1532-5415.1997.tb02966.x. [DOI] [PubMed] [Google Scholar]
  • 26.Ganguli M, Dodge HH, Mulsant BH. Rates and predictors of mortality in an aging, rural, community-based cohort: The role of depression. Arch Gen Psychiatry. 2002;59:1046–52. doi: 10.1001/archpsyc.59.11.1046. [DOI] [PubMed] [Google Scholar]
  • 27.SAS Institute . SAS/STAT User’s Guide, Version 8. SAS Institute; Cary, NC: 1999. [Google Scholar]
  • 28.Gillespie LD, Gillespie WJ, Robertson MC, et al. Interventions for preventing falls in elderly people. Cochrane Database Syst Rev. 2007;(4):CD000340. doi: 10.1002/14651858.CD000340. [DOI] [PubMed] [Google Scholar]
  • 29.Anstey KJ, von Sanden C, Luszcz MA. An 8-year prospective study of the relationship between cognitive performance and falling in very old adults. J Am Geriatr Soc. 2006;54:1169–76. doi: 10.1111/j.1532-5415.2006.00813.x. [DOI] [PubMed] [Google Scholar]
  • 30.Stone KL, Ewing SK, Lui L-Y, et al. Self-reported sleep and nap habits and risk of falls and fractures in older women: the study of osteoporotic fractures. J Am Geriatr Soc. 2006;54:1177–83. doi: 10.1111/j.1532-5415.2006.00818.x. [DOI] [PubMed] [Google Scholar]
  • 31.Cummings SR, Nevitt MC, Kidd S. Forgetting falls: the limited accuracy of recall of falls in the elderly. J Am Geriatr Soc. 1988;36:613–6. doi: 10.1111/j.1532-5415.1988.tb06155.x. [DOI] [PubMed] [Google Scholar]
  • 32.Piva SR, Fitzgerald GK, Irrgang JJ, et al. Get Up and Go test in patients with knee osteoarthritis. Arch Phys Med Rehab. 2004;85:284–9. doi: 10.1016/j.apmr.2003.05.001. [DOI] [PubMed] [Google Scholar]
  • 33.Rubenstein LZ, Josephson KR. Falls and their prevention in elderly people: What does the evidence show? Med Clin N Am. 2006;90:807–24. doi: 10.1016/j.mcna.2006.05.013. [DOI] [PubMed] [Google Scholar]
  • 34.Chang JT, Morton SC, Rubenstein LZ, et al. Interventions for the prevention of falls in older adults: systematic review and meta-analysis of randomized clinical trials. BMJ. 2004;328:680–6. doi: 10.1136/bmj.328.7441.680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Province MA, Hadley EC, Hornbrook MC, et al. The effects of exercise on falls in elderly patients. A preplanned meta-analysis of the FICSIT Trials. Frailty Injuries: Cooperative Studies of Intervention Techniques. JAMA. 1995;273:1341–7. [PubMed] [Google Scholar]
  • 36.Hauer K, Becker C, Lindemann U, et al. Effectiveness of physical training on motor performance and fall prevention in cognitively impaired older persons: a systemic review. Am J Phys Med Rehab. 2006;85:847–57. doi: 10.1097/01.phm.0000228539.99682.32. [DOI] [PubMed] [Google Scholar]
  • 37.Wolf SL, Barnhart HX, Kutner NG, et al. Reducing frailty and falls in older persons: an investigation of tai chi and computerized balance training. J Am Geriatr Soc. 1996;44:489–97. doi: 10.1111/j.1532-5415.1996.tb01432.x. [DOI] [PubMed] [Google Scholar]
  • 38.Hauer K, Rost B, Rutschle K, et al. Exercise training and secondary prevention of falls in geriatric patients with a history of injurious falls. J Am Geriatr Soc. 2001;49:10–20. doi: 10.1046/j.1532-5415.2001.49004.x. [DOI] [PubMed] [Google Scholar]
  • 39.Robertson MC, Gardner MM, Devlin N, et al. Effectiveness and economic evaluation of a nurse delivered home exercise programme to prevent falls. 2: Controlled trial in multiple centres. BMJ. 2001;322:1–5. doi: 10.1136/bmj.322.7288.701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Day L, Fildes B, Gordon I, et al. Randomized factorial trial of falls prevention among older people living in their own homes. BMJ. 2002;325:128–33. doi: 10.1136/bmj.325.7356.128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Barnett A, Smith B, Lord SR, et al. Community-based group exercise improves balance and reduces falls in at-risk older people: a randomized controlled trial. Age Ageing. 2003;32:407–14. doi: 10.1093/ageing/32.4.407. [DOI] [PubMed] [Google Scholar]
  • 42.Lord SR, Castell S, Corcoran J, et al. The effect of group exercise on physical functioning and falls in frail older people living in retirement villages: a randomized, controlled trial. J Am Geriatr Soc. 2003;51:1685–92. doi: 10.1046/j.1532-5415.2003.51551.x. [DOI] [PubMed] [Google Scholar]
  • 43.Liu-Ambrose T, Khan KM, Eng JJ, et al. Resistance and agility reduce fall risk in women aged 75 to 85 with low bone mass: a 6-month randomized, controlled trial. J Am Geriatr Soc. 2004;52:657–665. doi: 10.1111/j.1532-5415.2004.52200.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Inokuchi S, Matsusaka N, Hayashi T, et al. Feasibility and effectiveness of a nurse-led community exercise programme for prevention of falls among frail elderly people: a multi-centre controlled trial. J Rehab Med. 2007;39:479–85. doi: 10.2340/16501977-0080. [DOI] [PubMed] [Google Scholar]
  • 45.Shaw FE, Bond J, Richardson DA, et al. Multifactorial intervention after a fall in older people with cognitive impairment and dementia presenting to the accident and emergency department: randomised controlled trial. BMJ. 2003;326:73. doi: 10.1136/bmj.326.7380.73. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES