Journal of Gerontological Nursing

Feature Article 

Instruments for Measuring Fall Risk in Older Adults Living in Long-Term Care Facilities: An Integrative Review

Julius Oluwole Kehinde, MSN, RN

Abstract

Lack of consistency in the literature regarding the use of fall risk assessment in long-term care settings and the uniqueness of the environment demand a critical analysis of fall risk instruments specific to older adults living in long-term care facilities. This integrative review of the existing literature on instruments used to measure fall risk in residents living in long-term care facilities revealed a total of 16 fall risk assessment tools from 13 studies. Of the 13 studies, only 8 reported sensitivity, specificity, and interrater reliability values of the tools. Only the Morse Fall Scale and Mobility Fall Chart demonstrated high predictive values in their initial developmental studies. This review can help clinicians make informed choices about tools to predict falls in their settings and establish appropriate preventive interventions.

Abstract

Lack of consistency in the literature regarding the use of fall risk assessment in long-term care settings and the uniqueness of the environment demand a critical analysis of fall risk instruments specific to older adults living in long-term care facilities. This integrative review of the existing literature on instruments used to measure fall risk in residents living in long-term care facilities revealed a total of 16 fall risk assessment tools from 13 studies. Of the 13 studies, only 8 reported sensitivity, specificity, and interrater reliability values of the tools. Only the Morse Fall Scale and Mobility Fall Chart demonstrated high predictive values in their initial developmental studies. This review can help clinicians make informed choices about tools to predict falls in their settings and establish appropriate preventive interventions.

Mr. Kehinde is a PhD student, College of Nursing, Medical University of South Carolina, Charleston, South Carolina.

The author discloses that he has no significant financial interests in any product or class of products discussed directly or indirectly in this activity, including research support. The author thanks Jeanette Andrews, PhD, APRN-BC, FNP, and Tom G. Smith, PhD, for reviewing earlier drafts of this article.

Address correspondence to Julius Oluwole Kehinde, MSN, RN, College of Nursing, Medical University of South Carolina, 99 Jonathan Lucas Street, MSC 160, Charleston, SC 29425-9640; e-mail: jok31@musc.edu.

Received: January 12, 2009
Accepted: June 18, 2009
Posted Online: October 09, 2009

Falls are common major problems in older adults. Falls among nursing home residents can lead to serious injury and functional dependence (Rubenstein, 2006). In adults age 65 and older, unintentional injuries represent the fifth leading cause of death and 66% of these deaths are fall related (Rubenstein, 2006). Approximately 50% to 66% of older adults living in nursing homes fall each year, and nearly 10% of these falls result in hip fractures, subdural hematomas, serious soft tissue injuries, and head injuries (Fuller, 2000). The rate of mortality associated with falls increases sharply with age in all racial groups, with falls representing the sixth leading cause of death in older adults (Commodore, 1995; Rubenstein, Josephson, & Robbins, 1994).

In addition to physical injuries and mortality associated with falls, social and psychological consequences of falls have also been reported in older adults. Due to fear of falling, older adults’ physical functioning and quality of life may deteriorate (Nevitt, Cummings, & Hudes, 1991). The cost of falls and related injuries among older adults living in long-term care facilities is another concern. In the United States, the total direct cost of all fall-related injuries for older adults exceeded $19 billion across all settings in 2000 (Stevens, Corso, Finkelstein, & Miller, 2006). Approximately 6.26 million hip fractures are projected to occur worldwide annually (Parker, Gillespie, & Gillespie, 2005).

Risk Factors for Falls in Older Adults Living in Long-Term Care Facilities

Older adults living in long term-care facilities are at higher risk of falling than their community-dwelling counterparts (Cusimano, Kwok, & Spadafora, 2008). The incidence of falls in long-term care facilities is approximately 33% greater than in the community, representing nearly 1.4 falls per person per year (McClure et al., 2007). Fall-related injuries among long-term care residents account for an in-hospital death rate of 15% and a survival rate of only 66% (McClure et al., 2007).

Several studies have identified risk factors for falls in older adults living in long-term care facilities (Rubenstein & Josephson, 2002; Tinetti, 2003). Both extrinsic (i.e., poorly lit environments, wet or cluttered floor surfaces, faulty assistive devices) and intrinsic (i.e., physiological illness, lower extremity weakness, cognitive impairment, polypharmacy, impaired vision) risk factors have been identified (Rubenstein & Josephson, 2002). Rawsky’s (1998) literature review of more than 100 articles published over a period of 20 years (1976–1996) in various health care settings showed that in 21 studies, the following intrinsic risk factors were most common: cognitive impairment (16 studies), chronic illness and impaired mobility (14 studies), sensory deficits (7 studies), elimination (6 studies), and fall history (6 studies).

Approaches to Fall Risk Assessment

Regardless of the setting or population, interventions for fall prevention always begin with fall risk assessment, which enables practitioners to identify individuals at highest risk. The first approach is a comprehensive medical assessment used by geriatricians and nurse practitioners to evaluate and treat patients at risk for falls or who have recently fallen (Rubenstein, Josephson, & Osterweil, 1996). The comprehensive assessment can be part of an overall geriatric assessment or a post-fall assessment. A comprehensive assessment involves an in-depth medical evaluation of previous falls, cognitive status, balance, gait, strength, mobility, nutrition, and chronic diseases (King & Tinetti, 1996). This assessment can be time consuming and often requires a team of clinicians (Fleming, Evans, Weber, & Chutka, 1995; Wolf-Klein et al., 1988). Comprehensive assessment focuses on identifying intrinsic risk factors that can be treated to reduce the likelihood of a fall (Rubenstein et al., 1996). However, comprehensive assessment is limited because it does not provide a fall risk index that can be readily, easily, and consistently interpreted.

Nursing assessment of fall risk is commonly used in acute settings and nursing homes, usually involving specific measurement tools or forms. These instruments identify residents or patients who are prone to falls on the basis of intrinsic characteristics such as fall history, elimination frequency and dependence, sensory deficits, movement limitations, disease conditions, and psychological status (Perell et al., 2001). Nursing assessment instruments are used at admission to acute settings or long-term care facilities. Fall risk nursing assessment is also conducted daily, at the beginning of each shift, or weekly, depending on the patient’s condition. Poor scores often indicate a need for further assessment or initiation of appropriate nursing interventions (Perell et al., 2001).

The most commonly used nursing assessment fall risk instruments include:

Although several assessment tools are available in the literature, not all are relevant to the long-term care population, and practitioners are often unaware of existing instruments for long-term care residents (Perell et al., 2001). It is not uncommon for a long-term care facility to develop its own fall risk assessment tool using intrinsic factors from the literature or data collected from chart review. Only a few of these tools have been clinically tested to determine their predictive scores (Heinze, Dassen, Halfens, & Lohrmann, 2009).

A lack of consistency is also evident in the literature regarding the use of fall risk assessments in long-term care settings (Perell et al., 2001; Scott, Votova, Scanlan, & Close, 2007). This inconsistency regarding the relevance of fall risk assessments tools among older adults living in long-term care facilities indicates the need for an integrative review.

Previous reviews have focused on all settings, including the community and acute and long-term care facilities (Perell at al., 2001; Scott et al., 2007); however, none of these reviews focused primarily on long-term care facilities. The current integrative review of the existing literature analyzes instruments for measuring fall risk in long-term care facilities, thus enabling clinicians to make an informed choice about which tool(s) to use in their settings. An integrative approach to the literature is appropriate for this review because it helps provide a comprehensive background for understanding the state of current knowledge regarding instruments for measuring fall risk in older adults living in long-term care facilities.

The aims of this integrative review are to:

  • Identify existing fall risk assessment tools for long-term care facilities available in the literature.
  • Determine whether the existing fall risk assessment tools are reliable, sensitive, and specific to older adults in long-term care facilities.
  • Determine which of the existing fall risk assessment instruments are clinically relevant and can be completed in a reasonable amount of time.

Method

Literature Search

Information was obtained through a bibliographic search of English language publications indexed in CINAHL, PubMed, Ovid MEDLINE, and Google Scholar. An Ovid MEDLINE search from 1996 to October 2008 yielded 57 articles, with only 13 of them relevant to this review. Of the 233 articles yielded in a CINAHL (1982 to October 2008) search, 17 were relevant to this review. PubMed and Google Scholar searches yielded no additional articles. Primary and secondary sources of published literature were also hand searched. Keywords used were fall risk assessment, accidental falls, fall risk measurement tools, long-term care, nursing homes, risk assessment, and clinical assessment.

Review Methods

Literature relevant to instruments for measuring fall risk in older adults living in nursing homes was extracted from peer-reviewed journals and unpublished manuscripts. In this review, the review method of Hawker, Payne, Kerr, Hardey, and Powell (2002) was used. Although Hawker et al.’s method was intended primarily for qualitative research, it can also be used for quantitative studies where disparate data are involved (Hawker et al., 2002) and has been used successfully in a quantitative systematic review (Frasure, 2008).

The three stages described by Hawker et al. (2002) in their review method were used to appraise articles extracted from the databases. The stages involved are:

  • Stage 1: Assessment of relevancy of the instruments.
  • Stage 2: Data extraction.
  • Stage 3: Assessment of methodological rigor.

Assessment of Relevancy of the Instruments. The assessment of the relevancy of instruments for measuring fall risk used the “Gold Standard Criteria” established by Wyatt and Altman (1995) and Oliver, Daly, Martin, and McMurdo (2004). The elements of the Gold Standard Criteria for quality of fall risk assessment tools emphasize that the study should have been validated in a prospective study; used specificity and sensitivity analyses; been tested in more than one population; demonstrated good face validity, interrater reliability, and adherence from staff; and included understandable and easy-to-calculate scores.

In this integrative review, the inclusion of the number of items, time to complete tools, recommended cut-off scores, sample size, sensitivity, specificity, and interrater reliability helped judge the clinical relevance and application of the instruments to the population of older adults living in long-term care facilities. These criteria were also used by Scott et al. (2007) to conduct an effective systematic analysis of multifactorial and functional mobility assessment tools for fall risk among older adults in all settings.

Data Extraction. The second stage involves extraction of data from chosen literature. Hawker et al.’s (2002) assessment form (Table 1) was used to record the details of the articles used in the review. Studies retrieved from the databases were assessed for relevance of the study design, location of the study, sample size and description, intervention (if applicable), results, and conclusion or comments. Included in this review were 13 articles that focused on 16 fall risk assessment instruments for older adults and rated fairly well with these criteria. Most of the reviewed studies also provided the psychometric properties of the instruments. This integrative review excluded papers in languages other than English, as well as fall risk instruments that had been tested in other settings but not in long-term care facilities. Tools with low internal consistency and low construct validity in long-term care facilities, such as the Hendrich Fall Risk Scale, were also excluded (Heinze, Halfens, Roll, & Dassen, 2006).

Assessment Form Used in Review

Table 1: Assessment Form Used in Review

Assessment of Methodological Rigor. Each study’s methodological rigor was assessed by examining sources of bias and method of protecting against bias (Hawker et al., 2002; Oxman, 1994) (Table 2). All 13 articles included in this review rated either fair or good under the following criteria: abstract and title, introduction and aims, method and data, sampling, data analysis, ethics and bias, findings/results, transferability/generalizability, and implications and usefulness (Hawker et al., 2002).

Assessment Form for Methodological Rigor

Table 2: Assessment Form for Methodological Rigor

Results

As shown in Table 3, a total of 16 fall risk assessment tools were reviewed from 13 studies. The fall risk tools included in this integrative review assessed which individuals are likely to fall in long-term care facilities on the basis of intrinsic factors (i.e., unsteady gait, fall history, sensory deficits, incontinence, medical conditions) and extrinsic (i.e., environmental) factors. The sample sizes of the reviewed studies varied widely from 78 to 9,943 participants. The number of items in the tools also varied, ranging from 1 to 99. The Functional Reach, Timed Chair Stands, Timed Up and Go, and Timed Walk assessment tools had only one item. The number of items included in the other tools can be found in Table 3.

Review Findings of Fall Risk Assessment Tools in Long-Term Care Facilities

Table 3: Review Findings of Fall Risk Assessment Tools in Long-Term Care Facilities

Specificity and Sensitivity

Eight studies reported sensitivity and specificity values for 6 tools (Cwikel, Fried, Biderman, & Galinsky, 1998; Lundin-Olsson, Jensen, Nyberg, & Gustafson, 2003; Lundin-Olsson, Nyberg, & Gustafson, 2000; McCollam, 1995; Mertens, Halfens, & Dassen, 2003; Morse et al., 1989; Robey-Williams et al., 2007; Rosendahl et al., 2003). Specificity and sensitivity are important criteria used in evaluating instruments designed as screening instruments or diagnostic aids (Polit & Beck, 2004). In this review, sensitivity refers to the ability of an instrument to correctly identify a person who falls. According to Polit and Beck (2004), an instrument’s sensitivity implies “its rate of yielding true positives” (p. 428). Specificity refers to the instrument’s ability to correctly identify those individuals who do not fall. An instrument’s specificity is its rate of yielding “true negatives” (Polit & Beck, 2004, p. 428).

In this review, specificity scores ranged from 28% to 83% and sensitivity scores from 43% to 100%. Although the Spartanburg Fall Risk Assessment tool demonstrated high sensitivity and interrater reliability of 100% and 90% respectively, it showed a low specificity score of 28%. Robey-Williams et al. (2007) suggested that false positives (72%) accounted for the tool’s low specificity; that is, patients who were at true risk were prevented from falling due to effective staff interventions at the institution providing care. The Mobility Fall Chart showed 85% sensitivity and 82% specificity in the initial developmental study in 2000 but demonstrated 43% sensitivity and 69% specificity in a follow-up study in 2003 (Lundin-Olsson et al., 2000, 2003). The Downton Index (Rosendahl et al., 2003) had a high sensitivity score of 91% but fared low in specificity (39%). Both the Morse Fall Scale (Morse et al., 1989) and the Mobility Fall Chart (Lundin-Olsson et al., 2000) demonstrated high sensitivity, specificity, and interrater reliability. Interrater reliability refers to agreement, or consistency, among scores assigned by two or more observers or raters (DiIorio, 2005).

Of the 13 studies reviewed, 7 reported the interrater reliability of 10 tools: Area Ellipse of Postural Sway (0.72) (Thapa, Gideon, Brockman, Fought, & Ray, 1996), Functional Reach (0.92, Rockwood, Awalt, Carver, MacKnight, 2000; 0.57, Thapa et al., 1996), Mobility Fall Chart (0.80) (Lundin-Olsson et al., 2000), Morse Fall Scale (0.96, Morse et al., 1989; 0.68, McCollam, 1995), Resident Assessment Instrument (0.79) (Morris et al., 1997), Spartanburg Fall Risk Assessment (0.90) (Robey-Williams et al., 2007), Timed Chair Stands (0.63) (Thapa et al., 1996), Timed Up and Go (0.56) (Rockwood et al., 2000), Timed Walk (0.88) (Thapa et al., 1996), and Tinetti Balance Subscale (0.98) (Thapa et al., 1996).

The time to administer these assessment tools ranged from less than 1 minute for the Morse Fall Scale to 80 minutes for the Resident Assessment Instrument (Table 3). Time taken to administer the Care Dependency Scale, Downton Index, Functional Reach, High Risk for Fall Assessment, Reassessment Is Safe “Kare,” Spartanburg Fall Risk Assessment, and Tinetti Balance Subscale were not stated in the articles reviewed for this study.

Discussion

The findings of this integrative review suggest that fall risk instruments exist with evidence to support their relevance in predicting the risk of falls in older adults living in long-term care facilities. According to Scott et al. (2007), the choice of a tool in the clinical setting depends on the why the tool is needed. The authors suggested that if the purpose is to screen for high-risk populations, an instrument that is quick and easy to implement and yet has high sensitivity and specificity scores is needed. If the purpose is to reduce risk, the tools must be able to reliably identify the modifiable risk factors toward which fall prevention intervention can be targeted.

There is inconsistency in the literature regarding the methods of reporting and interpreting the properties of fall risk measurement tools (Scott et al., 2007). For example, while Perell et al. (2001) set criteria for establishing high predictive values for fall risk assessment tools at 80% sensitivity and greater than 75% specificity, Oliver et al. (2004) recommended a cut-off score of only 70% for both sensitivity and specificity. Oliver et al.’s (2004) criteria were used to appraise the predictive values of the tools in this review.

Of the 13 studies reviewed, 5 failed to report the predictive values of the instruments studied (i.e., Area Ellipse of Postural Sway, Functional Reach, High Risk for Fall Assessment, Mean Velocity of Postural Sway, Reassessment Is Safe “Kare,” Resident Assessment Instrument, Timed Chair Stands, Timed Up and Go, Timed Walk, Tinetti Balance Subscale). Although five of the assessment tools in this review demonstrated interrater reliability ranging from adequate (0.72) to highly desirable (0.98) (Polit & Beck, 2004), the noninclusion of their sensitivity and specificity scores limits their clinical relevance in long-term care facilities. There is a need in future studies to evaluate and report the sensitivity and specificity of these fall risk instruments. It will also be beneficial to use receiver operating characteristic (ROC) curves in future validation studies to determine the best cut-off scores (Polit & Beck, 2004). An instrument’s ROC curve is constructed by plotting its sensitivity against the false positive rate (i.e., the rate of incorrectly diagnosing an individual as a case). The best cut-off point is the balance between sensitivity and specificity; this point is at or near the shoulder of the ROC curve (Polit & Beck, 2004).

The Elderly Fall Screening Test (Cwikel et al., 1998) demonstrated high specificity (78%) and sensitivity (93%) scores but failed to report interrater reliability. The tool also takes an average of 17 minutes to complete. The relatively longer time it takes to complete the Elderly Fall Screening test and its missing inter-rater reliability score limit its usefulness in long-term care facilities.

The Spartanburg Fall Risk Assessment (Robey-Williams et al., 2007), the Morse Fall Scale (Morse et al., 1989), and the Mobility Fall Chart (Lundin-Olsson et al., 2000) show great potential for clinical relevance in long-term care facilities. The Morse Fall Scale demonstrated high predictive values of 0.96 interrater reliability, 78% sensitivity, and 83% specificity. A follow-up validation study on the Morse Fall Scale conducted by McCollam (1995) showed similar predictive values. Another appealing quality of the Morse Fall Scale is that it takes less than 1 minute to complete. In addition, the Morse Fall Scale has an established cutoff score between 45 and 55, with scores above indicating the need for intervention. Using Perell et al.’s (2001) criteria of high sensitivity, specificity, and interrater reliability; reasonable time to complete; established cut-off score; and similarity of the population of interest to those with which the tool was developed, the Morse Fall Scale demonstrates clinical relevance for long-term care facilities.

Similarly, the Mobility Fall Chart demonstrated 85% sensitivity, 82% specificity, and 0.80 interrater reliability for residents of long-term care facilities when it was first developed (Lundin-Olsson et al., 2000). The predictive scores of the instrument were notably lower in a follow-up study (Lundin-Olsson et al., 2003). The authors did not give any explanation for the low predictive accuracy in this validating study. The Mobility Fall Chart has great potential for clinical relevance in long-term care facilities; however, the inconsistency between the developmental and the follow-up samples suggests a need for further testing of the instrument among older adults.

Several studies in this review failed to state specificity, sensitivity, or interrater reliability values for the instruments. This makes it more difficult to compare studies on fall risk measurement in long-term care facilities with the aim of identifying a clinically relevant tool. Similarly, excepting the Morse Fall Scale, none of the instruments in this review showed high predictive values consistently in both developmental and follow-up samples (Lundin-Olsson et al., 2000, 2003; McCollam, 1995; Morse et al., 1989) (Table 3). Further validation studies that include sensitivity, specificity, and reliability values of fall risk measurement tools tested in long-term care facilities would be beneficial. The use of ROC curves to help determine cut-off scores of the fall risk instruments would also be beneficial.

Limitations

This integrative review is limited by the lack of reported sensitivity, specificity, and interrater reliability scores. Additionally, many authors did not report the time required to administer the tools. The inclusion of these variables in all of the studies would further strengthen the findings of this review.

Strengths

The use of Hawker et al.’s (2002) comprehensive review process helped provide structure and a sound methodological approach to this review. Inclusion of psychometric properties in some of the studies helped identify the Morse Fall Scale as an instrument that is clinically relevant to long-term care residents.

Conclusion

Several fall risk assessment tools are available for residents of long-term care facilities. Only a few of the published studies on these instruments have reported predictive values and time required to administer the instruments, making it challenging to determine the clinical relevance of these tools in long-term care facilities. Thus, further validation research is needed to determine the sensitivity, specificity, and reliability of these tools in long-term care settings. Of the 13 studies reviewed, only the Morse Fall Scale demonstrated high predictive values in both developmental and follow-up samples and evidence that it can be completed in less than a minute, thus demonstrating its clinical relevance in long-term care facilities. Similarly, the Mobility Fall Chart demonstrated high predictive scores in the developmental sample; however, in a follow-up validating study, the predictive accuracy of the instrument was notably low, suggesting need for further testing of this tool in long-term care facilities.

References

  • Brians, L.K., Alexander, K., Grota, P., Chen, R.W. & Dumas, V. (1991). The development of the RISK tool for fall prevention. Rehabilitation Nursing, 16, 67–69.
  • Commodore, D.I. (1995). Falls in the elderly population: A look at incidence, risks, healthcare costs and preventive strategies. Rehabilitation Nursing, 20(2), 84–89.
  • Cusimano, M.D., Kwok, J. & Spadafora, K. (2008). Effectiveness of multifaceted fall-prevention programs for the elderly in residential care. Injury Prevention, 14, 113–122. doi:10.1136/ip.2007.017533 [CrossRef]
  • Cwikel, J.G., Fried, A.V., Biderman, A. & Galinsky, D. (1998). Validation of a fall-risk screening test, the Elderly Fall Screening Test (EFST), for community-dwelling elderly. Disability and Rehabilitation, 20, 161–167.
  • DiIorio, C.K. (2005). Measurement in health behavior: Methods for research and evaluation. San Francisco: Jossey-Bass.
  • Fife, D.D., Solomon, P. & Stanton, M. (1984). A risk/falls program: Code orange for success. Nursing Management, 15(11), 50–53.
  • Fleming, K.C., Evans, J.M., Weber, D.C. & Chutka, D.S. (1995). Practical functional assessment of elderly persons: A primary-care approach. Mayo Clinic Proceedings, 70, 890–910. doi:10.4065/70.9.890 [CrossRef]
  • Frasure, J. (2008). Analysis of instruments measuring nurses’ attitudes towards research utilization: A systematic review. Journal of Advanced Nursing, 61, 5–18.
  • Fuller, G.F. (2000). Falls in the elderly. American Family Physician. Retrieved July 27, 2009, from http://www.aafp.org/afp/20000401/2159.html
  • Hawker, S., Payne, S., Kerr, C., Hardey, M. & Powell, J. (2002). Appraising the evidence: Reviewing disparate data systematically. Qualitative Health Research, 12, 1284–1299. doi:10.1177/1049732302238251 [CrossRef]
  • Heinze, C., Dassen, T., Halfens, R. & Lohrmann, C. (2009). Screening the risk of falls: A general or a specific instrument?Journal of Clinical Nursing, 18, 350–356. doi:10.1111/j.1365-2702.2008.02453.x [CrossRef]
  • Heinze, C., Halfens, R.J., Roll, S. & Dassen, T. (2006). Psychometric evaluation of the Hendrich Fall Risk Model. Journal of Advanced Nursing, 53, 327–332. doi:10.1111/j.1365-2648.2006.03728.x [CrossRef]
  • Hendrich, A., Nyhuis, A., Kippenbrock, T. & Soja, M.E. (1995). Hospital falls: Development of predictive model for clinical practice. Applied Nursing Research, 8, 129–139. doi:10.1016/S0897-1897(95)80592-3 [CrossRef]
  • King, M.B. & Tinetti, M.E. (1996). A multifactorial approach to reducing injurious falls. Clinics in Geriatric Medicine, 12, 745–759.
  • Lundin-Olsson, L., Jensen, J., Nyberg, L. & Gustafson, Y. (2003). Predicting falls in residential care by a risk assessment tool, staff judgement and history of falls. Aging Clinical and Experimental Research, 15, 51–90.
  • Lundin-Olsson, L., Nyberg, L. & Gustafson, Y. (2000). The Mobility Interaction Fall chart. Physiotherapy Research International, 5, 190–201. doi:10.1002/pri.198 [CrossRef]
  • MacAvoy, S., Skinner, T. & Hines, M. (1996). Clinical methods: Fall risk assessment tool. Applied Nursing Research, 9, 213–218. doi:10.1016/S0897-1897(96)80127-3 [CrossRef]
  • McClure, R.J., Turner, C., Peel, N., Spinks, A., Eakin, E. & Hughes, K. (2007). Population-based interventions for the prevention of fall-related injuries in older people (Article No. CD00441). Cochrane Database of Systematic Reviews, Issue 2.
  • McCollam, M.E. (1995). Evaluation and implementation of a research-based falls assessment innovation. Nursing Clinics of North America, 30, 507–514.
  • Mercer, L. (1997). Falling out of favour. Australian Nursing Journal, 4(7), 27–29.
  • Mertens, E.I., Halfens, R.J.G. & Dassen, T. (2003). Using the Care Dependency Scale for fall risk screening. Journal of Advanced Nursing, 58, 594–601. doi:10.1111/j.1365-2648.2007.04265.x [CrossRef]
  • Morris, J.N., Nonemaker, S., Murphy, K., Hawes, C., Fries, B.E. & Mor, V. et al. (1997). A commitment to change: Revision of HCFA’s RAI. Journal of the American Geriatrics Society, 45, 1011–1016.
  • Morse, J.M., Morse, R.M. & Tylko, S. (1989). Development of a scale to identify the fall-prone patient. Canadian Journal on Aging, 8, 366–377.
  • Nevitt, M.C., Cummings, S.R. & Hudes, E.S. (1991). Risk factors for injurious falls: A prospective study. Journal of Gerontology, 46, M164–M170.
  • Oliver, D., Britton, M., Seed, P., Martin, F.C. & Harper, A.H. (1997). Development and evaluation of evidence-based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: Case-control and cohort studies. BMJ, 315, 1049–1053.
  • Oliver, D., Daly, F., Martin, F.C. & McMurdo, M.E. (2004). Risk factors and risk assessment tools for falls in hospital in-patients: A systematic review. Age and Ageing, 33, 122–130. doi:10.1093/ageing/afh017 [CrossRef]
  • Oxman, A.D. (1994). Systematic reviews: Checklists for review articles. BMJ, 309, 648–651.
  • Parker, M.J., Gillespie, L.D. & Gillespie, J.W. (2005). Hip protectors for preventing hip fractures in the elderly (Article No. CD001255). Cochrane Database of Systematic Reviews, Issue 3.
  • Perell, K.L., Nelson, A., Goldman, R.L., Luther, S.L., Preito-Lewis, N. & Rubenstein, L.Z. (2001). Fall risk assessment measures: An analytic review. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56, M761–M766.
  • Polit, D.F. & Beck, C.T. (2004). Nursing research: Principles and methods (7th ed.). Philadelphia: Lippincott Williams & Wilkins.
  • Rawsky, E. (1998). Review of the literature on falls among the elderly. Image, 30(1), 47–52.
  • Robey-Williams, C., Rush, K.L., Bendyk, H., Patton, L.M., Chamberlain, D. & Sparks, T. (2007). Spartanburg Fall Risk Assessment Tool: A simple three-step process. Applied Nursing Research, 20, 86–93. doi:10.1016/j.apnr.2006.02.002 [CrossRef]
  • Rockwood, K., Awalt, E., Carver, D. & Macknight, C. (2000). Feasibility and measurement properties of the functional reach and timed up and go tests in the Canadian study of health and aging. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 55, M70–M73.
  • Rosendahl, E., Lundin-Olsson, L., Kallin, K., Jensen, J., Gustafson, Y. & Nyberg, L. (2003). Prediction of falls among older people in residential care facilities by the Downton Index. Aging Clinical and Experimental Research, 15, 142–147.
  • Rubenstein, L.Z. (2006). Falls in older people: Epidemiology, risk factors and strategies for prevention. Age and Ageing, 35(Suppl. 2), ii37–ii41. doi:10.1093/ageing/afl084 [CrossRef]
  • Rubenstein, L.Z. & Josephson, K.R. (2002). The epidemiology of falls and syncope. In Kenny, R.A. & O’Shea, D. (Eds.), Clinics in geriatric medicine: Falls and syncope in elderly patients (pp. 141–158). Philadelphia: Saunders. doi:10.1016/S0749-0690(02)00002-2 [CrossRef]
  • Rubenstein, L.Z., Josephson, K.R. & Osterweil, D. (1996). Falls and fall prevention in the nursing home. Clinics in Geriatric Medicine, 12, 881–903.
  • Rubenstein, L.Z., Josephson, K.R. & Robbins, A.S. (1994). Falls in the nursing home. Annals of Internal Medicine, 121, 442–451.
  • Scott, V., Votova, K., Scanlan, A. & Close, J. (2007). Multifactorial and functional mobility assessment tools for fall risk among older adults in community, home support, long-term and acute care settings. Age and Ageing, 36, 130–139. doi:10.1093/ageing/afl165 [CrossRef]
  • Stevens, J.A., Corso, P.S., Finkelstein, E.A. & Miller, T.R. (2006). The cost of fatal and non-fatal falls among older adults. Injury Prevention, 12, 290–295. doi:10.1136/ip.2005.011015 [CrossRef]
  • Thapa, P.B., Gideon, P., Brockman, K.G., Fought, R.L & Ray, W.A. (1996). Clinical and biomechanical measures of balance as fall predictors in ambulatory nursing home residents. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 51, M239–M246.
  • Tinetti, M.E. (2003). Preventing falls in elderly persons. New England Journal of Medicine, 348, 42–49. doi:10.1056/NEJMcp020719 [CrossRef]
  • Wolf-Klein, G.P., Silverstone, F.A., Basavaraju, N., Foley, C.J., Pacaru, A. & Ma, P.H. (1988). Prevention of falls in the elderly population. Archives of Physical Medicine and Rehabilitation, 69, 689–691.
  • Wyatt, J.C. & Altman, D.G. (1995). Prognostic models: Clinically useful or quickly forgotten?BMJ, 311, 1539–1541.
  • Young, S.W., Abedzadeh, C.B. & White, M.W. (1989). A fall prevention program for nursing homes. Nursing Management, 20, 80Y–80AA, 80DD, 80FF.

Assessment Form Used in Review

Author(s):
Title:
Year of publication:
Date assessed:
Assessment criteria:

Study design

Location of study

Sample size and description

Intervention (if applicable)

Results

Conclusion or comments

Assessment Form for Methodological Rigor

1. Abstract and title Good (abstract includes all information) Fair (abstract includes most information) Poor (inadequate abstract) Very Poor (no abstract)
2. Introduction and aims Good (concise background with clear aims) Fair (some background with research question outline) Poor (some background with no aims) Very Poor (no aims or background)
3. Method and data Good (appropriate method and described clearly) Fair (appropriate method, description could be better) Poor (method described inadequately) Very Poor (no mention of method and/or method inappropriate)
4. Sampling Good (detailed and appropriate sample size) Fair (sample size justified) Poor (sampling mentioned but few descriptive details) Very Poor (no detail of sample)
5. Data analysis Good (clear description of analysis) Fair (description of analysis) Poor (minimal details about analysis) Very Poor (no description of analysis)
6. Ethics and bias Good (issues of confidentiality, sensitivity, researcher’s bias, and consent well addressed) Fair (lip service paid to issues of ethics and bias) Poor (brief mention of ethics and bias issues) Very Poor (no mention of issues)
7. Findings/results Good (findings explicit and easy to understand; results relate to aims, and data support findings/results) Fair (findings/results mentioned without explanation) Poor (findings/results haphazardly presented) Very Poor (findings/results not mentioned or not related to aims)
8. Transferability/generalizability Good (context and setting of the study is adequately described) Fair (some context and setting described but may need to be compared with other studies) Poor (minimal description of setting and context) Very Poor (no description of context or setting)
9. Implications and usefulness Good (contributes something new in terms of insight or perspective, suggests implications for policy and practice, and emphasizes the need for further research) Fair (states only two of those mentioned in “Good”) Poor (states only one of those mentioned in “Good”) Very Poor (states none of those mentioned in “Good”)

Review Findings of Fall Risk Assessment Tools in Long-Term Care Facilities

Tool Study Purpose No. of Items Time to Complete Cut-Off Score n Mean Age Sensitivity Specificity Interrater Reliability
Area Ellipse of Postural Swaya Thapa et al. (1996)b, c Screening 10 seconds 118 81 0.72
Care Dependency Scale Mertens et al. (2003) Screening 15 61 to 68 9,943 78 60% to 74% 60% to 74%
Downton Index Rosendahl et al. (2003)c, d Screening 11 ≥3 78 81 91% 39%
Elderly Fall Screening Test Cwikel et al. (1998)e Screening 6 17 minutes 3 361 93 % 78%
Functional Reach Rockwood et al. (2000)c, f Screening 1 323 78 0.92
Functional Reach Thapa et al. (1996)b, c Screening 1 118 81 0.57
High Risk for Fall Assessment Young et al. (1989)e Screening 7
Mean Velocity of Postural Sway Thapa et al. (1996)b, c Screening 10 seconds 118 81
Mobility Fall Chart Lundin-Olsson et al. (2000)c Screening 5 to 15 minutes 78 82 85%h 82%h 0.80
Mobility Fall Chart Lundin-Olsson et al. (2003)c Screening 5 to 15 minutes 203 82 43% 69%
Morse Fall Scale Morse et al. (1989)e Screening 6 <1 minute 45 100 78% 83% 0.96
Morse Fall Scale McCollam (1995)e Screening 6 <1 minute 55 458 83% 83% 0.68
Reassessment Is Safe “Kare” Brians et al. (1991)e Screening 4 1 208
Resident Assessment Instrument Morris et al. (1997)e Assessment 99 80 minutes 187 0.79
Spartanburg Fall Risk Assessment Robey-Williams et al. (2007) Screening 4 172 72 100% 28%g 0.90
Timed Chair Stands Thapa et al. (1996)b, c Screening 1 30 seconds 118 81 0.63
Timed Up and Go Rockwood et al. (2000)c Screening 1 <1 minute 323 78 0.56
Timed Walk Thapa et al. (1996)b, c Screening 1 variesi 118 81 0.88
Tinetti Balance Subscale Thapa et al. (1996)b, c Screening 6 118 81 0.98

Measuring Fall Risk

Kehinde, J.O. (2009). Instruments for Measuring Fall Risk in Older Adults Living in Long-Term Care Facilities: An Integrative Review. Journal of Gerontological Nursing, 35(10), 46–55.

  1. Lack of consistency in the literature regarding the use of fall risk assessment in long-term care settings and the uniqueness of the environment demand a critical analysis of fall risk instruments that is specific to older adults living long-term care facilities.

  2. Of the 13 studies reviewed, only 8 reported sensitivity and specificity, and 7 reported interrater reliability values of the fall risk assessment tools. Therefore, further validation research is needed to determine predictive values of these tools and their relevance in long-term care settings.

  3. The Morse Fall Scale demonstrated high predictive values in both developmental and follow-up samples and evidence that it can be completed in less than a minute. Thus, it is clinically relevant in long-term care facilities.

Authors

Mr. Kehinde is a PhD student, College of Nursing, Medical University of South Carolina, Charleston, South Carolina.

The author discloses that he has no significant financial interests in any product or class of products discussed directly or indirectly in this activity, including research support. The author thanks Jeanette Andrews, PhD, APRN-BC, FNP, and Tom G. Smith, PhD, for reviewing earlier drafts of this article.

Address correspondence to Julius Oluwole Kehinde, MSN, RN, College of Nursing, Medical University of South Carolina, 99 Jonathan Lucas Street, MSC 160, Charleston, SC 29425-9640; e-mail: .jok31@musc.edu

10.3928/00989134-20090902-01

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