Journal of Gerontological Nursing

Feature Article 

Recurrent Falls Among Community-Dwelling Older Koreans: Prevalence and Multivariate Risk Factors

In-Young Yoo, PhD, RN

Abstract

This study was conducted to determine the risk factors for nonfallers versus fallers (1+ falls) and nonfallers/one-time fallers versus recurrent fallers (2+ falls) using the Resident Assessment Instrument-Home Care (RAI-HC©). Community-dwelling Koreans 65 and older (N = 411) comprised the sample. Multivariate logistic regression was used to determine the factors predictive of fallers and recurrent fallers. Eight predictive factors were statistically significant with regard to recurrent falls: unsteady gait, low scores related to performance of activities of daily living (ADLs) and instrumental ADLs (IADLs), high pain scores, number of housing environmental hazards, use of an assistive device, fear of falling, and reduced vision. Based on the findings, it is important to assess the risk factors for recurrent falls and develop differentiation strategies that help prevent recurrent falls, including management of gait problems, pain control, use of appropriate assistive devices, a fear management program, regular eye examinations, making improvements to ADLs and IADLs, and creating a safer home environment. In addition, using a standardized tool such as the RAI-HC would help assess multivariate fall risk factors to facilitate comparisons across different community care settings.

Abstract

This study was conducted to determine the risk factors for nonfallers versus fallers (1+ falls) and nonfallers/one-time fallers versus recurrent fallers (2+ falls) using the Resident Assessment Instrument-Home Care (RAI-HC©). Community-dwelling Koreans 65 and older (N = 411) comprised the sample. Multivariate logistic regression was used to determine the factors predictive of fallers and recurrent fallers. Eight predictive factors were statistically significant with regard to recurrent falls: unsteady gait, low scores related to performance of activities of daily living (ADLs) and instrumental ADLs (IADLs), high pain scores, number of housing environmental hazards, use of an assistive device, fear of falling, and reduced vision. Based on the findings, it is important to assess the risk factors for recurrent falls and develop differentiation strategies that help prevent recurrent falls, including management of gait problems, pain control, use of appropriate assistive devices, a fear management program, regular eye examinations, making improvements to ADLs and IADLs, and creating a safer home environment. In addition, using a standardized tool such as the RAI-HC would help assess multivariate fall risk factors to facilitate comparisons across different community care settings.

Dr. Yoo is Assistant Professor, Department of Nursing, Jeonju University, Jeonju, Korea.

The author discloses that she has no significant financial interests in any product or class of products discussed directly or indirectly in this activity. The author acknowledges financial support from Jeonju University in Korea and thanks the Korean older participants.

Address correspondence to In-Young Yoo, PhD, RN, Assistant Professor, Department of Nursing, Jeonju University, 1200, Hyoja-dong 3Ga, Jeonju, 560–759, Korea; e-mail: yiny@jj.ac.kr.

Received: July 11, 2010
Accepted: January 05, 2011
Posted Online: May 18, 2011

The elderly population of Korea, as a proportion of the total population, is rapidly growing. Since 1970, the percentage of people 65 and older has more than doubled—from 3.1% to 7.2%—and is expected to increase to 20.8% by 2026 (Statistics Korea, 2010b). In 2009 (the year for which the most recent data were available), the percentage of people in Korea 65 and older was 10.7% (Statistics Korea, 2010b).

Among older adults, falls represent a major cause of mortality and morbidity. According to the World Health Organization (2011), an estimated 391,000 people died due to falls in 2002, making it the second leading cause of unintentional injury death globally after road traffic injuries. Each year, approximately one third of older adults experience a fall (Fletcher & Hirdes, 2002). Falls can cause injuries, immobility, psychosocial disorders, and hospitalization, or can result in early admission to long-term care facilities. Finkelstein, Fiebelkorn, Corso, and Binder (2004) report that, in the United States, 10% of older adults who experience falls also sustain bone fractures, severe tissue damage, or head injuries, which together result in an increased mortality rate. Finally, there are financial implications related to injuries from falls. If falls are not reduced and/or prevented, medical expenses related to care of older adults will continue to increase up to at least the year 2050. In Korea in 2009, medical expenses related to the care of people 65 and older comprised 30.5% of all national medical expenses (Statistics Korea, 2010a).

Falls usually result from a convergence of many factors, including those that are intrinsic and extrinsic. Falls can also be considered the end result of a multifaceted health condition derived from diverse health problems. Many studies touch on the incidence of falls and risk factors for falls. Intrinsic factors that increase the rate of falls are age, past fall experience, balance, walking ability, mobility, decline of cognitive function and muscle strength, visual impairment, peripheral sensory impairment, pain, taking more than four different medications, depression, orthostatic hypotension, arthritis, and Parkinson’s disease (Blyth, Cumming, Mitchell, & Wang, 2007; E.S. Choi, Lim, & Jun, 2007; J.M. Kim & Lee, 2007; Lord, 2006; Lord, Sherrington, Menz, & Close, 2007; Yoo & Choi, 2007). Extrinsic factors include those that involve environmental hazards (Lord et al., 2007; Yoo, 2005).

Generally, there is an especially strong correlation between having a fall experience and experiencing another fall in the future (Fletcher & Hirdes, 2002). In the United States, a fall history was found to be the strongest risk factor associated with subsequent falls (Kiely, Kiel, Burrows, & Lipsitz, 1998). International studies seeking to identify diverse factors of multiple falls have been conducted (Cesari et al., 2002; Chu, Chi, & Chiu, 2005; Fletcher & Hirdes, 2002; Gassmann, Rupprecht, & Freiberger, 2009; Graafmans et al., 1996), but only two studies discussed the risk factors of recurrent or multiple falls in Korea (E.S. Choi et al., 2007; Park, Chang, & Park, 2008). For this reason, a study of Korean older adults with recurrent falls was needed.

A single fall is neither a definitive sign of a major problem nor does it pose an increased risk for subsequent falls, as that fall may have been an isolated event. However, a person who experiences recurrent falls (defined as having had more than two falls in a 6-month period; i.e., a recurrent faller) should be evaluated for treatable causes (George, 2000). It is clinically important to separate one-time fallers from recurrent fallers (Fletcher & Hirdes, 2002; Gassmann et al., 2009), because while one-time fallers may have experienced uncontrollable accidents, recurrent fallers may have comorbid conditions that cause diverse problems and/or physical impairments. Thus, a differentiated preventive management program for recurrent fallers can potentially reduce the negative effects of falls.

One of the barriers to determining risk factors for falls among older adults is the lack of consistency in the variables and tools used in research. Fletcher and Hirdes (2002) suggested that the use of internationally standard tools, such as the Resident Assessment Instrument-Home Care (RAI-HC©), would assist researchers in making comparisons across settings. The RAI-HC is widely used in Canada, Finland, Germany, Hong Kong, Italy, Singapore, the United States, and other countries to determine risk factors for falls and their effects (Cesari et al., 2002; Chen, Hwang, Chen, Chen, & Lan, 2008; Fletcher, Berg, Dalby, & Hirdes, 2009; Fletcher & Hirdes, 2002; French et al., 2007; Gassmann et al., 2009), but is rarely used in Korea. The RAI-HC is a comprehensive tool used to determine the functional health status of older adults; therefore, a diversity of evaluation indicators are used. The RAI-HC is also effective in analyzing factors related to risk for falls, from multidimensional aspects. Thus, the purpose of the current study was to determine, through the use of the RAI-HC, the risk factors for nonfallers versus fallers (1+ falls) and nonfallers/one-time fallers versus recurrent fallers (2+ falls).

Method

Sample and Setting

The current study used a cross-sectional descriptive design. Participants were recruited on a volunteer basis from older adults receiving home care services from any of six public health centers or six welfare centers in the provinces of Chungcheongbuk-do or Chunrabuk-do in Korea. Korean people 65 and older who could communicate, understand the purpose and procedure of the research, and agree to participate in the study were included in the study sample.

Data Collection

The study was conducted between June 2008 and June 2009. Data were collected by 12 trained research assistants using a structured questionnaire (i.e., the RAI-HC). To maintain data consistency and improve reliability, a training session was conducted for the research assistants, consisting of 16 hours of training over a 2-week period and practical training using the RAI-HC just prior to data collection. This training made use of the instruction manual that accompanies the RAI-HC and a detailed guidebook on environmental risk factors pertaining to falls.

The research assistants visited the participants and explained the purpose and significance of the research. Written consent was obtained from each participant to ensure his or her right to self-determination; participants were also informed during the interview that they could withdraw from the study at any time. The interview was conducted at the participants’ homes and lasted 50 to 60 minutes. If the participants were not at home, the research assistants made another appointment either with their families or with the participants via telephone. Among 430 potential participants, a final sample of 411 older adults was obtained (response rate = 95.6%).

Instruments

The main instrument used in the study was the RAI-HC version 2.0, which had been developed by investigators for use in Canada, France, Italy, Japan, the Netherlands, Switzerland, the United Kingdom, and the United States (Fletcher & Hirdes, 2002). It contains a Minimum Data Set (MDS) that can provide a comprehensive evaluation of older adults’ current health status, including their functional health status, medical conditions, environment, and other factors in home care settings (Table 1). The evaluation guidance is very detailed, which helps minimize variation among evaluators. Its validity has been approved both within Korea and worldwide (S.M. Kim et al., 2000; Morris et al., 1997).

Domain Areas Assessed in the Minimum Data Set-Home Care

Table 1: Domain Areas Assessed in the Minimum Data Set-Home Care

The independent variables used to evaluate the risk factors for falls were separated into intrinsic and extrinsic factors, according to the RAI-HC manual (Morris et al., 1997). The former included socioeconomic variables, medical conditions, historical factors, and physical functional status; the latter included environmental hazards. These factors were delineated as follows:

  • Sociodemographic variables: gender, age, educational level, living arrangements, health insurance.
  • Medical conditions: various chronic diseases, number of diseases, perceived health status.
  • Historical factors: medications, pain (MDS-Pain), incontinence, fear of falling.
  • Physical functional status: unsteady gait, use of assistive device, activities of daily living (MDS-ADL), instrumental activities of daily living (MDS-IADL), depression (MDS-Depression Rating Scale [MDS-DRS]), cognition (MDS-Cognitive Performance Scale [MDS-CPS]), vision, hearing.
  • Extrinsic factors: housing environmental hazards.

The scales based on the RAI-HC items were designed to describe pain (MDS-Pain) and performance in terms of personal ADLs, IADLs, depression (MDS-DRS), and cognitive impairment (MDS-CPS). The MDS-Pain scale contains two items: pain frequency and pain intensity (Fries, Simon, Morris, Flodstrom, & Bookstein, 2001). The MDS-Pain score was calculated using a 4-point Likert scale (0 = no pain to 3 = very severe pain). In this study, the Cronbach’s alpha coefficient for this dimension was 0.89.

Similar scoring was used for each of the other items. The MDS-ADL scale was based on a self-evaluation of performance in the following tasks: bed mobility, mobility from bed to chair and chair to bed, locomotion, dressing, eating, toilet use, and personal hygiene. It was scored with a 7-point Likert scale (0 = total dependence to 6 = no impairment) (Morris, Fries, & Morris, 1999). For this study, the Cronbach’s alpha coefficient for this dimension was 0.95.

The MDS-IADL scale was based on four levels of self-evaluation: performance in meal preparation, housework, use of the telephone, use of transportation, shopping, managing finances, and taking medication. Each IADL category was scored from 0 (totally dependent) to 3 (independent). The MDS-IADL score was the sum of the aforementioned items and thus ranged from 0 to 21; for this study, the Cronbach’s alpha coefficient for this dimension was 0.90.

The MDS-DRS is composed of 7 items, each rated on a scale of 0 (does not exhibit) to 2 (exhibits almost daily), for a total score ranging from 0 to 14. The higher the score, the more severe the feelings of depression. Validation studies were based on a comparison to the American Psychiatric Association’s (1994) criteria for major or minor depression diagnoses. The MDS-DRS was 91% sensitive and 69% specific at a cut-off score of 3 of 7 (Burrows, Morris, Simon, Hirdes, & Phillips, 2000). In this study, the Cronbach’s alpha coefficient for the MDS-DRS was 0.82.

Finally, the MDS-CPS was based on selected MDS items, including two cognitive items (short-term memory and decision-making skills), a measure of communication ability (understood by others), self-evaluation of performance in eating, and level of consciousness. MDS-CPS scores were computed according to the method described by Morris et al. (1994), with scores ranging from 0 (intact) to 6 (very severe impairment). In this study, the Cronbach’s alpha coefficient for this dimension was 0.71.

Regarding extrinsic factors, 8 items addressed housing environmental hazards, which were determined to be beneficial or harmful through yes or no responses. The dependent variable was fall status in the past 90 days. A fall event was considered an unintentional change of position that resulted in the body coming into contact with the floor. The outcome variables were compared and analyzed in two ways: (a) nonfallers versus fallers (1+ falls) (Model 1) and (b) nonfallers/one-time fallers versus recurrent fallers (2+ falls) (Model 2).

Data Analysis

SPSS version 14.0 for Windows® was used for data analyses. Descriptive analyses were performed for sociodemographic factors and prevalence of falls. Univariate analyses for risk factors of falls and recurrent falls were performed, along with chi-square and t tests. Multivariate logistic regressions were used to identify predictive factors that had an influence on falling.

Results

General Characteristics

The general characteristics of the study population are shown in Table 2. Of the 411 participants, 298 (72.5%) were women, and 113 (27.5%) were men. Participants’ mean age was 77.8. Regarding educational levels, 197 (47.9%) did not have any education, and 214 (52.1%) had at least elementary school education. As to living arrangements, 227 (55.2%) lived alone, whereas 184 (44.8%) lived with family members or others. Regarding health insurance, 175 (42.6%) participants were eligible for health insurance, and 236 (57.4%) were eligible for Medicare.

General Characteristics and Fall Status of the Study Participants (N = 411)

Table 2: General Characteristics and Fall Status of the Study Participants (N = 411)

One quarter of participants (25.3%) had experienced at least one fall during the past 90 days, and 11.2% had experienced 2+ falls (Table 2). Among those who fell, 44.2% experienced recurrent fall.

Intrinsic Factors Related to Falls and Recurrent Falls

With regard to general characteristics, two fall-related factors were statistically significant: age and living arrangement. Participants in the 65–80 age group who lived alone showed a greater prevalence of falls. Similar results were found among recurrent fallers (Table 3).

Falls Experience of the Study Participants According to General Characteristics

Table 3: Falls Experience of the Study Participants According to General Characteristics

In terms of statistically significant medical conditions, more fallers than nonfallers (Model 1) had cardiovascular disease, arthritis, glaucoma, and poor perceived health status, and more recurrent fallers than nonfallers/one-time fallers (Model 2) had cardiovascular disease, hypertension, and glaucoma (Table 4). In regard to historical factors and physical functional status, significant differences were found between nonfallers and fallers in their scores on the following factors: MDS-Pain, fear of falling, unsteady gait, use of an assistive device, MDS-ADL, MDS-IADL, MDS-DRS, MDS-CPS, and reduced hearing. Fallers had higher mean MDS-Pain and MDS-DRS scores and lower mean MDS-ADL, MDS-IADL, MDS-CPS, and hearing scores than nonfallers (Table 4). When recurrent fallers were compared with nonfallers/one-time fallers, significant differences were found in their scores on the following factors: MDS-Pain, fear of falling, unsteady gait, use of an assistive device, MDS-ADL, MDS-IADL, MDS-CPS, and reduced vision. Recurrent fallers had higher mean MDS-Pain and lower mean MDS-ADL, MDS-IADL, MDS-CPS, and vision scores than nonfallers/one-time fallers (Table 4).

Falls Experience of the Study Participants According to Intrinsic FactorsFalls Experience of the Study Participants According to Intrinsic Factors

Table 4: Falls Experience of the Study Participants According to Intrinsic Factors

Extrinsic Factors Related to Falls and Recurrent Falls

Extrinsic fall-related risk factors were analyzed according to housing environmental hazards (Table 5). In terms of statistically significant hazards, results showed that fallers had more cases than nonfallers of “inadequate or no lighting in living room, sleeping room, kitchen, toilet,” “holes in floor, leaking pipes,” “inadequate environment of toilet and bathroom,” “difficulty entering and leaving the house,” and “difficulty entering and leaving rooms in the house.” Recurrent fallers had more cases than nonfallers/one-time fallers of most these same hazards, with the exception of “holes in floor, leaking pipes.” This study identified a significantly higher number of housing environmental hazards among fallers and recurrent fallers compared with the other group, respectively.

Falls Experience of the Study Participants According to Extrinsic Factors

Table 5: Falls Experience of the Study Participants According to Extrinsic Factors

Predictive Factors of Falls and Recurrent Falls

Multivariate logistic regression was used to determine the factors predictive of fallers and recurrent fallers. In Model 1, examining non-fallers versus fallers, eight significant risk factors for predicting falls included: age, living alone, depression, unsteady gait, low MDS-IADL scores, high MDS-Pain scores, number of housing environmental hazards, and use of an assistive device (Table 6). In the analysis of Model 2, examining nonfallers/one-time fallers versus recurrent fallers, there were also eight factors associated with an increased risk of falling: unsteady gait, low MDS-IADL scores, high MDS-Pain scores, number of housing environmental hazards, use of an assistive device, fear of falling, low MDS-ADL scores, and reduced vision.

Logistic Regression Analysis of Predictors of Falls and Recurrent Falls

Table 6: Logistic Regression Analysis of Predictors of Falls and Recurrent Falls

Discussion and Implications

This study investigated two different outcome measures for fall events among community-dwelling older adults: nonfallers versus fallers (1+ falls) (Model 1) and nonfallers/one-time fallers versus recurrent fallers (2+ falls) (Model 2). Both the categorization and analyses of falls are consistent with those of previous studies (Chu et al., 2005; Fletcher & Hirdes, 2002; Graafmans et al., 1996).

In the 90 days leading up to the survey, the recurrent fall rate among participants who had experienced falls was 44.2%. This rate was higher than that reported by Fletcher and Hirdes (2002) and Fletcher et al. (2009)—38.1% and 42.4%, respectively—for community-dwelling older adults. The high recurrent fall rate among Korean older adults, as found in the current study, is consistent with the rate reported in other Korean studies: 43% (E.S. Choi et al., 2007) and 53.8% (Park et al., 2008). This finding suggests that, in Korea, many falls may be considered isolated events and that no fall prevention program was implemented to address this health issue at the community level.

Univariate analyses indicated that the following were risk factors for experiencing one or more falls: age, living arrangement, cardiovascular disease, arthritis, glaucoma, poor perceived health status, high MDS-Pain scores, fear of falling, unsteady gait, use of an assistive device, low MDS-ADL or MDS-IADL scores, high MDS-DRS scores, low MDS-CPS scores, reduced hearing, and a high number of housing environmental hazards. Similar results were found for recurrent fallers, but arthritis, poor perceived health status, high MDS-DRS scores, and reduced hearing were not risk factors. In addition, hypertension and reduced vision were important risk factors for recurrent falls.

Multivariate logistic regression analyses revealed five variables that were significant in both models (Table 6): unsteady gait, low MDS-IADL scores, high MDS-Pain scores, number of housing environmental hazards, and use of an assistive device. Age, living alone, and high MDS-DRS scores were significant predictors of falls, while three other factors were significant predictors of recurrent falls: fear of falling, low MDS-ADL scores, and reduced vision (Table 6).

The findings of the current study differ from those of most studies concerning age. Although incidences of falls are known to increase with age in general (Chu et al., 2005), the current study found that adults ages 65 to 80 had a higher rate of falls than those 81 to 94. This result is similar to that of another Korean study (J.M. Kim & Lee, 2007). It is possible that adults 81 and older had a reduced rate of falls due to an overall reduction in activities. Vigorous older adults are more likely to participate in dynamic activities and to fall and be injured (Speechley & Tinetti, 1991). Runge (1993) suggested that the rate of falls among older adults increases with age, but among those older than 85, the rate of falls has actually decreased, perhaps due to a reduced survival rate and limited activities among this population.

In terms of living arrangements, older adults living alone showed a higher rate of falls than those who live with family members or others, consistent with Yoo’s (2005) findings in Korea. Because the number of Korean older adults living alone is rapidly increasing, to help reduce falls in this group health care practitioners need to: (a) complete a comprehensive assessment for fall risk; (b) provide additional and/or ongoing education regarding the risk factors for falls based on the assessment; (c) recommend strategies for fall prevention that can be implemented in the home; (d) inquire if others can become involved in the person’s care, such as a relative, friend, or neighbor; and (e) make referrals to other practitioners for care as needed (e.g., ophthalmologist for vision care).

With respect to the fall risk factors that are based on medical conditions, recurrent fallers had worse conditions in terms of cardiovascular disease, high blood pressure, and glaucoma than nonfallers/one-time fallers. Black and Wood (2005) reported that eye disease, such as glaucoma, increases one’s chances of experiencing a fall, and Lipsitz, Jonsson, Kelly, and Koestner (1991) indicated that abnormal blood pressure levels can increase fall risk. Therefore, health care practitioners need to do more than just assess older adults for these conditions; they need to refer these patients for appropriate treatment, if they cannot provide the treatment themselves (e.g., referral to a cardiologist and/or internist for treatment and management of hypertension). The home care nurse can then follow up with continuing education for the older adult if his or her specific disease relates to an increased risk for falling.

With respect to the extrinsic factors that lead to recurrent falls, housing environmental hazards were found to be important predictors of falls and recurrent falls. Compared with nonfallers/one-time fallers, recurrent fallers had a higher rate of inadequate or no lighting in living rooms, sleeping rooms, kitchens, and toilet and difficulties entering/leaving the house and room. In addition, the average number of housing environmental hazards for recurrent fallers was 3.04; for nonfallers/one-time fallers, it was 1.99. Given these results, it is not surprising that housing environmental hazards can increase the prevalence of falls by more than 50% (Cesari et al., 2002). Evaluation of the older adult’s home seems to be of primary importance, especially for frail older adults who rely on home care. In a Falls-HIT (Home Intervention Team) program by Nikolaus and Bach (2003), intervention was provided on home visits to identify environmental hazards that could increase the risk of falling. The intervention consisted of giving advice about possible changes, offering assistance with home modifications, and providing training in using safety devices and mobility aids. These interventions reduced the fall rate by 31% (Nikolaus & Bach, 2003). The intervention was most effective for those with recurrent falls in the previous year.

Community nurses should develop a strategy to manage housing environmental hazards and unsafe behavior and recommend home modifications and behavior changes. This would involve control of an individual’s intrinsic risk factors in home care settings. Community nurses need to: (a) assess the toilet and bathroom and offer suggestions to improve safety in this area (e.g., grab bars, elevated toilet seats, access to needed supplies); (b) assess the older adult’s skill and/or ability to enter and leave the home and various rooms within; (c) recommend ways to improve safety (e.g., adding more lighting, constructing ramps for access into and out of the home, rearranging furniture); and (d) connect the older adult to the community resource center and/or church.

The findings of this study clearly show the important predictors of falls and recurrent falls (Table 6). Age, living alone, and depression were predictors of falls but not of recurrent falls. Participants with an unsteady gait were 10 times more likely to be at risk of recurrent falls, whereas the increase for falls in general was 6.9-fold. Gassmann et al. (2009) reported that older adults with unsteady gait had a 9.56-fold increased risk of recurrent falls, which is consistent with the findings of the current study. Again, health care practitioners should strive to determine the fundamental, underlying causes of unsteady gait, not only to prevent recurrent falls but to improve the person’s ability to walk. Health care practitioners can also recommend new footwear if the person wears poor-fitting shoes and/or an exercise regimen if unsteady gait is the result of muscular weakness.

Regarding recurrent falls, the current study found that pain increases the risk of future falls 11-fold; for falls in general, however, the increase was only 1.68-fold. Blyth et al. (2007) stated that pain contributes to falls among older adults, and thus emphasized the importance of pain management. Health care practitioners should discuss older adult patients’ pain issues and assess for psychological or physical factors related to chronic pain (e.g., musculoskeletal disease, stress, lack of sleep), as pain management is dependent on the cause of the pain. Nurses are also required to develop and apply a multidisciplinary pain control program that is tailored to each patient’s individual needs. Such a program could involve tai chi, aqua therapy, massage therapy, or psychological counseling and support. Nurses can also refer older adults with pain to other medical specialists for appropriate treatment.

The use of an assistive device can be a benefit, in that it can prevent falls; however, such devices may actually cause more falls if used improperly. In this study, it was found that use of an assistive device increased the chance of recurrent falls 22.56-fold and the chance of falling in general 7.07-fold. Consistent with the current study results, French et al. (2007) found in the United States that the use of assistive devices (e.g., canes, walkers, crutches) or the use of wheelchairs increases the risk of recurrent falls. Chu et al. (2005) indicated that the proportion of recurrent fallers who use assistive devices was high (37.5%) but that only 9.2% of nonfallers/one-time fallers used assistive devices. Falls may be caused by a failure to use a wheelchair’s locking device or using a cane or assistive device that does not suit the person’s size or needs. Therefore, health care providers should assess and educate older adults on the proper use of assistive devices, ensure an expert recommended the device (not a neighbor or friend), and determine whether the person was trained by an expert on use of the device. Ongoing assessment and additional training by nurses is essential.

Fear of falling, low MDS-ADL scores, and reduced vision were three of the significant risk factors that distinguished fallers (1+ falls) from recurrent fallers (2+ falls). Participants with a fear of falling were 22 times more likely to be at risk for recurrent falls, a result consistent with those of other studies (Chu et al., 2005; Yoo & Choi, 2007). Even if no physical injury occurs, the experience of falling may create a fear of future falls and become a factor that increases the chances of recurrent falls. Older adults with a fear of falling should be treated in a fear management program, such as the Home Support Exercise Program (HSEP) and a tai chi self-help program (J.H. Choi & Yoo, 2007; Yoo, 2009).

The rate of recurrent falls was higher among older adults with low ability to perform ADLs and IADLs. Ability to perform ADLs was an important predictor of recurrent falls (Model 2) but not of falls in general (Model 1). Consistent with the current study’s findings, other research (Chu et al., 2005; Jung, Shin, Chung, & Lee, 2007) has reported that low daily function capability increases risk of falls. These problems would be closely related to unsteady gait and use of assistive devices, and they exacerbate the risk of falls. Further research in this area would be beneficial.

Reduced vision was the most profound predictor for recurrent falls, as indicated by odds ratio values in this study of 30.23. In the United States, Lord and Dayhew (2001) found that older adults with impaired vision were three times more likely to be at risk of a fall or recurrent falls. Lord (2006) also reported that vision contributes significantly to balance, and thus impaired vision is a significant independent risk factor for falls and fractures in older people. In Korea, older adults consider reduced vision a normal part of aging rather than a “fixable” impairment; for this reason, they tend not to seek proper care for poor eyesight, including use of eyeglasses (Jung et al., 2007). Therefore, initiatives among health care practitioners—to raise awareness of the importance of regular eye examination and use of appropriate vision prescriptions—are required to prevent recurrent falls in older adults.

Limitations

One limitation in this study is the use of retrospective design, by which measurement error in self-reported number of falls might have occurred. The number of falls may have been underreported, because participants have limited accuracy in remembering falls over a certain period in the past. Cummings, Nevitt, and Kidd (1988), for example, reported that 13% to 32% of older adults who have fallen previously have forgotten about their falls. To attenuate this issue, the current study examined the falling rate from only the previous 90 days. Furthermore, future research should use a prospective design to determine more accurately the incidence of falling. Another limitation is that only volunteers were selected for participation in the study. It is possible that the findings may not be generalized to other older adults. To increase generalizability, recruiting via random selection is needed.

Conclusion

Among the 104 Korean older adults in this study who had experienced at least one fall, nearly half (n = 46) had experienced a recurrent fall. The aging of Korean society is occurring at a rapid pace; therefore, a high rate of recurrent falls is a serious problem among Korean older adults. The findings of this study indicated that unsteady gait, diminished ability to perform ADLs and IADLs, presence of pain, housing environmental hazards, use of an assistive device, fear of falling, and reduced vision were important predictors of recurrent falls among participants. Falls can be indicators of poor health and declining functional status; these predictors should be detected as early as possible, during the provision of home care service. Although it is not simple to prevent falls, these factors reveal the potential to manage falls. Nurses, especially in community settings, must take great care to evaluate the risk factors—especially cardiovascular disease, high blood pressure, unsteady gait, and vision—to prevent recurrent falls. In addition, nurses need to develop strategies by which they can manage housing environmental hazards in home care settings. Nurses can also provide older adults with education on proper use of assistive devices, training in performance of ADLs and IADLs, and counseling to manage fear of falls. Finally, nurses should use comprehensive assessment tools, such as the RAI-HC, to identify risk factors for recurrent falls, as well as develop and apply individualized preventive programs for older adults.

References

  • American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.
  • Black, A. & Wood, J. (2005). Vision and falls. Clinical and Experimental Optometry, 88, 212–222. doi:10.1111/j.1444-0938.2005.tb06699.x [CrossRef]
  • Blyth, F.M., Cumming, R., Mitchell, P. & Wang, J.J. (2007). Pain and falls in older people. European Journal of Pain, 11, 564–571. doi:10.1016/j.ejpain.2006.08.001 [CrossRef]
  • Burrows, A.B., Morris, J.N., Simon, S., Hirdes, J.P. & Phillips, C. (2000). Development of a minimum data set-based depression rating scale for use in nursing homes. Age and Ageing, 29, 165–172. doi:10.1093/ageing/29.2.165 [CrossRef]
  • Cesari, M., Landi, F., Torre, S., Onder, G., Lattanzio, F. & Bernabei, R. (2002). Prevalence and risk factors for falls in an older community-dwelling population. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 57, M722–M726. doi:10.1093/gerona/57.11.M722 [CrossRef]
  • Chen, Y.M., Hwang, S.J., Chen, L.K., Chen, D.Y. & Lan, C.F. (2008). Risk factors for falls among elderly men in a veterans home. Journal of the Chinese Medical Association, 71, 180–185. doi:10.1016/S1726-4901(08)70101-1 [CrossRef]
  • Choi, E.S., Lim, K.I. & Jun, T.W. (2007). The study of isokinetic muscle power, flexibility, static balance and dynamic reaction time according to the frequency of fall down in elderly women. Journal of Korean Physical Education Association for Girls and Women, 21(3), 55–64.
  • Choi, J.H. & Yoo, I.Y. (2007). Effects of tai chi self-help program on functional status of knee joint, fatigue, fear of falling for elderly woman patients with knee osteoarthritis. Journal of the Korean Gerontological Society, 27, 913–927.
  • Chu, L.W., Chi, I. & Chiu, A.Y. (2005). Incidence and predictors of falls in the Chinese elderly. Annals of the Academy of Medicine, Singapore, 34, 60–72.
  • Cummings, S.R., Nevitt, M.C. & Kidd, S. (1988). Forgetting falls. The limited accuracy of recall of falls in the elderly. Journal of the American Geriatrics Society, 36, 613–616.
  • Finkelstein, E.A., Fiebelkorn, I.C., Corso, P.S. & Binder, S.C. (2004, January16). Medical expenditures attributable to injuries—United States, 2000. Morbidity and Mortality Weekly Report, 53. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5301a1.htm
  • Fletcher, P.C., Berg, K., Dalby, D.M. & Hirdes, J.P. (2009). Risk factors for falling among community-based seniors. Journal of Patient Safety, 5, 61–66. doi:10.1097/PTS.0b013e3181a551ed [CrossRef]
  • Fletcher, P.C. & Hirdes, J.P. (2002). Risk factors for falling among community-based seniors using home care services. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 57, M504–M510. doi:10.1093/gerona/57.8.M504 [CrossRef]
  • French, D.D., Werner, D.C., Campbell, R.R., Powell-Cope, G.M., Nelson, A.L., Rubenstein, L.Z. & Spehar, A.M.,… (2007). A multivariate fall risk assessment model for VHA nursing homes using the minimum data set. Journal of the American Medical Directors Association, 8, 115–122. doi:10.1016/j.jamda.2006.08.005 [CrossRef]
  • Fries, B.E., Simon, S.E., Morris, J.N., Flodstrom, C. & Bookstein, F.L. (2001). Pain in U.S. nursing homes: Validating a pain scale for the minimum data set. The Gerontologist, 41, 173–179. doi:10.1093/geront/41.2.173 [CrossRef]
  • Gassmann, K.G., Rupprecht, R. & Freiberger, E. (2009). Predictors for occasional and recurrent falls in community-dwelling older people. Zeitschrift fur Gerontologie und Geriatrie, 42, 3–10. doi:10.1007/s00391-008-0506-2 [CrossRef]
  • George, G.F. (2000). Falls in the elderly. American Family Physician, 61. Retrieved from http://www.aafp.org/afp/20000401/2159.html
  • Graafmans, W.C., Ooms, M.E., Hofstee, H.M.A., Bezemer, P.D., Bouter, L.M. & Lips, P. (1996). Falls in the elderly: A prospective study of risk factors and risk profiles. American Journal of Epidemiology, 143, 1129–1136.
  • Jung, Y.M., Shin, D.S., Chung, K.S. & Lee, S.E. (2007). Health status and fall-related factors among older Korean women. Journal of Gerontological Nursing, 36(10), 12–20.
  • Kiely, D.K., Kiel, D.P., Burrows, A.B. & Lipsitz, L.A. (1998). Identifying nursing home residents at risk for falling. Journal of the American Geriatrics Society, 46, 551–555.
  • Kim, J.M. & Lee, M.S. (2007). Risk factors for falls in the elderly population in Korea: An analysis of the third Korea National Health and Nutrition Examination Survey data. Journal of Korean Society for Health Education and Promotion, 24(4), 23–39.
  • Kim, S.M., Bae, S.S., Kim, D.H., June, K.J., Kim, C.Y. & Yoon, J.L. (2000). Validity of resident assessment instrument-minimum data set home care version in Korea. Journal of the Korean Geriatrics Society, 4(1), 68–75.
  • Lipsitz, L.A., Jonsson, P.V., Kelly, M.M. & Koestner, J.S. (1991). Causes and correlates of recurrent falls in ambulatory frail elderly. Journal of Gerontology, 46, M114–M122.
  • Lord, S., Sherrington, C., Menz, H. & Close, J. (2007). Falls in older people: Risk factors and strategies for prevention. Cambridge, UK: Cambridge University Press. doi:10.1017/CBO9780511722233 [CrossRef]
  • Lord, S.R. (2006). Visual risk factors for falls in older people. Age and Ageing, 35(Suppl. 2), ii42–ii45. doi:10.1093/ageing/afl085 [CrossRef]
  • Lord, S.R. & Dayhew, J. (2001). Visual risk factors for falls in older people. Journal of the American Geriatrics Society, 49, 508–515. doi:10.1046/j.1532-5415.2001.49107.x [CrossRef]
  • Morris, J.N., Bernabei, R., Ikegami, N., Gilgen, R., Fries, B.E., Steel, K. & Carpenter, I. (1997). RAI-home care© assessment manual. Retrieved from the North Carolina Department of Health and Human Services website: http://info.dhhs.state.nc.us/olm/manuals/doa/saih/man/RAI-HCAssessmentManual.pdf
  • Morris, J.N., Fries, B.E., Mehr, D.R., Hawes, C., Philips, C., Mor, V. & Lipsitz, L.A. (1994). MDS cognitive performance scale. Journal of Gerontology, 49, M174–M182.
  • Morris, J.N., Fries, B.E. & Morris, S.A. (1999). Scaling ADLs within the MDS. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 54, M546–M553. doi:10.1093/gerona/54.11.M546 [CrossRef]
  • Nikolaus, T. & Bach, M. (2003). Preventing falls in community-dwelling frail older people using a home intervention team (HIT): Results from the randomized Falls-HIT trial. Journal of the American Geriatrics Society, 51, 300–305. doi:10.1046/j.1532-5415.2003.51102.x [CrossRef]
  • Park, H.S., Chang, R. & Park, K.Y. (2008). Comparative study on fall related characteristics between single and recurrent falls in community-dwelling older women. Journal of Korean Academy of Adult Nursing, 20, 905–916.
  • Runge, J.W. (1993). The cost of injury. Emergency Medicine Clinics of North America, 11, 241–253.
  • Speechley, M. & Tinetti, M. (1991). Falls and injuries in frail and vigorous community elderly persons. Journal of the American Geriatrics Society, 39, 46–52.
  • Statistics Korea. (2010a). 2010 social indicators of Korea. Retrieved from http://kostat.go.kr/portal/korea/kor_nw/2/6/1/index.board?bmode=read&aSeq=245833
  • Statistics Korea. (2010b). Statistics of Korean elderly. Retrieved from http://kostat.go.kr/portal/korea/kor_nw/2/6/1/index.board?bmode=read&bSeq=&aSeq=67505&pageNo=1&rowNum=10&navCount=10&currPg=&sTarget=title&sTxt=%EA%B3%A0%EB%A0%B9%EC%9E%90%ED%86%B5%EA%B3%84
  • World Health Organization. (2011). Violence and injury prevention and disability: Falls. Retrieved from http://www.who.int/violence_injury_prevention/other_injury/falls/en
  • Yoo, I.Y. (2005). Fall and housing environmental problem of the couple and single elderly. Journal of Korean Society of Living Environmental System, 12, 199–205.
  • Yoo, I.Y. (2009). Effects of fall prevention program applying HSEP on physical balance and gait, leg strength, fear of falling and falls efficacy of community-dwelling elderly. Journal of the Korean Gerontological Society, 29, 259–273.
  • Yoo, I.Y. & Choi, J.H. (2007). Experience of falls and predictors of falls in the elderly who use senior citizens’ centers. Journal of Korean Academy of Community Nursing, 18(1), 14–22.

Domain Areas Assessed in the Minimum Data Set-Home Care

AA Demographic Section I Continence
AB Referral Section J Disease diagnosis
Section A Health assessment information Section K Health condition/preventive health measures
Section B Cognition Section L Nutrition/hydration
Section C Communication/hearing Section M Oral hygiene
Section D Vision Section N Skin condition
Section E Mood and behavior Section O Environmental assessment
Section F Social functioning Section P Service utilization
Section G Support (caregiver) Section Q Medications
Section H ADL/IADL

General Characteristics and Fall Status of the Study Participants (N = 411)

Variable n(%)
Gender
  Women 298 (72.5)
  Men 113 (27.5)
Age (mean age = 77.8)
  65 to 80 263 (64)
  81 to 94 148 (36)
Educational level
  Uneducated 197 (47.9)
  Elementary school or higher 214 (52.1)
Living arrangement
  Alone 227 (55.2)
  With family or others 184 (44.8)
Health insurance
  Eligible for health insurance 175 (42.6)
  Eligible for Medicare 236 (57.4)
Falls (0 versus 1+)
  No fall 307 (74.7)
  One or more falls 104 (25.3)
Falls (0 or 1 versus 2+)
  0 or 1 fall 365 (88.8)
  2+ falls 46 (11.2)

Falls Experience of the Study Participants According to General Characteristics

Model 1 Model 2
Nonfaller (n= 307) Faller (1+ Falls) (n= 104) Nonfaller/One-Time Faller (n= 365) Recurrent Faller (2+ Falls) (n= 46)
Variable n(%) n(%) χ2 n(%) n(%) χ2
Gender 0.023 2.327
  Women 222 (72.3) 76 (73.1) 269 (73.7) 29 (63)
  Men 85 (27.7) 28 (26.9) 96 (26.3) 17 (37)
Age 4.989* 9.718**
  65 to 80 187 (60.9) 76 (73.1) 224 (61.4) 39 (84.8)
  81 to 94 120 (39.1) 28 (26.9) 141 (38.6) 7 (15.2)
Educational level 1.951 0.912
  Uneducated 141 (45.9) 56 (53.8) 178 (48.8) 19 (41.3)
  Elementary school or higher 166 (54.1) 48 (46.2) 187 (51.2) 27 (58.7)
Living arrangement 12.604*** 41.958***
  Alone 154 (50.2) 73 (70.2) 181 (49.6) 46 (100)
  With family or others 153 (49.8) 31 (29.8) 184 (50.4) 0 (0)
Health insurance 3.612 2.106
  Eligible for health insurance 139 (45.3) 36 (34.6) 160 (43.8) 15 (32.6)
  Eligible for Medicare 168 (54.7) 68 (65.4) 205 (56.2) 31 (67.4)

Falls Experience of the Study Participants According to Intrinsic Factors

Model 1 Model 2
Nonfaller (n= 307) Faller (1+ Falls) (n= 104) Nonfaller/One-Time Faller (n= 365) Recurrent Faller (2+ Falls) (n= 46)
Variable n(%) n(%) χ2 n(%) n(%) χ2
MEDICAL CONDITION
Stroke 0.019 0.647a
  No 261 (85) 89 (85.6) 309 (84.7) 41 (89.1)
  Yes 46 (15) 15 (14.4) 56 (15.3) 5 (10.9)
Cardiovascular disease 10.515** 43.953***
  No 297 (96.7) 92 (88.5) 355 (97.3) 34 (73.9)
  Yes 10 (3.3) 12 (11.5) 10 (2.7) 12 (26.1)
Hypertension 1.172 5.503*
  No 181 (59) 55 (52.9) 217 (59.5) 19 (41.3)
  Yes 126 (41) 49 (47.1) 148 (40.5) 27 (58.7)
Diabetes mellitus 0.083 2.133
  No 250 (81.4) 86 (82.7) 302 (82.7) 34 (73.9)
  Yes 57 (18.6) 18 (17.3) 63 (17.3) 12 (26.1)
Parkinson’s disease 7.122** 2.649a
  No 287 (93.5) 104 (100) 345 (94.5) 46 (100)
  Yes 20 (6.5) 0 (0) 20 (5.5) 0 (0)
Arthritis 3.814* 0.196
  No 146 (47.6) 38 (36.5) 162 (44.4) 22 (47.8)
  Yes 161 (52.4) 66 (63.5) 203 (55.6) 24 (52.2)
Osteoporosis 2.818 1.680
  No 243 (79.2) 74 (71.2) 285 (78.1) 32 (69.6)
  Yes 64 (20.8) 30 (28.8) 80 (21.9) 14 (30.4)
Cataracts 15.995*** 11.187a,**
  No 239 (77.9) 99 (95.2) 292 (80) 46 (100)
  Yes 68 (22.1) 5 (4.8) 73 (20) 0 (0)
Glaucoma 12.186** 44.092***
  No 301 (98) 94 (90.4) 359 (98.4) 36 (78.3)
  Yes 6 (2) 10 (9.6) 6 (1.6) 10 (21.7)
Pulmonary disease 0.172 6.052a,**
  No 276 (89.9) 92 (88.5) 322 (88.2) 46 (100)
  Yes 31 (10.1) 12 (11.5) 43 (11.8) 0 (0)
Perceived health status 4.020* 1.365
  Good 55 (17.9) 10 (9.6) 55 (15.1) 10 (21.7)
  Poor 252 (82.1) 94 (90.4) 310 (84.9) 36 (78.3)
Mean (SD) Mean (SD) tValue Mean (SD) Mean (SD) tValue
Number of diseases 2.452 (1.62) 2.519 (1.80) −0.352 2.463 (1.69) 2.522 (1.42) −0.225
n(%) n(%) χ2 n(%) n(%) χ2
HISTORICAL FACTORS
Incontinence 0.283 2.790a
  No 255 (83.1) 84 (80.8) 297 (81.4) 42 (91.3)
  Yes 52 (16.9) 20 (19.2) 68 (18.6) 4 (8.7)
Fear of falling 4.710* 8.650**
  No 185 (60.3) 50 (48.1) 218 (59.7) 17 (37)
  Yes 122 (39.7) 54 (51.9) 147 (40.3) 29 (63)
Mean (SD) Mean (SD) tValue Mean (SD) Mean (SD) tValue
Number of medications 1.289 (1.30) 1.115 (1.35) 1.169 1.227 (1.26) 1.391 (1.65) −0.796
MDS-Pain 1.016 (0.97) 1.337 (0.83) −3.008** 1.038 (0.95) 1.565 (0.74) −3.606***
n(%) n(%) χ2 n(%) n(%) χ2
PHYSICAL FUNCTIONAL STATUS
Unsteady gait 42.415a,*** 18.820a,***
  No 127 (41.4) 7 (6.7) 132 (36.2) 2 (4.3)
  Yes 180 (58.6) 97 (93.3) 233 (63.8) 44 (95.7)
Use of an assistive device 16.091*** 7.650**
  No 119 (38.8) 18 (17.3) 130 (35.6) 7 (15.2)
  Yes 188 (61.2) 86 (82.7) 235 (64.4) 39 (84.8)
Mean (SD) Mean (SD) tValue Mean (SD) Mean (SD) tValue
MDS-ADL 2.095 (1.67) 1.664 (0.93) 2.498* 2.068 (1.58) 1.326 (0.66) 3.134**
MDS-IADL 9.498 (6.83) 6.903 (5.69) 3.486** 9.331 (6.72) 4.956 (4.47) 4.294***
MDS-DRS 4.267 (3.61) 5.404 (3.63) −2.771** 4.484 (3.62) 5.108 (3.76) −1.094
MDS-CPS 2.756 (1.23) 2.327 (0.79) 3.309** 2.723 (1.20) 2.043 (0.20) 3.821***
Reduced vision 1.006 (0.87) 0.960 (0.92) 0.446 0.961 (0.84) 1.261 (1.14) −2.161*
Reduced hearing 0.827 (0.83) 0.625 (0.91) 2.082* 0.800 (0.86) 0.587 (0.83) 1.586

Falls Experience of the Study Participants According to Extrinsic Factors

Model 1 Model 2
Nonfaller (n= 307) Faller (1+ Falls) (n= 104) Nonfaller/One-Time Faller (n= 365) Recurrent Faller (2+ Falls) (n= 46)
Housing Environmental Hazards n(%) n(%) χ2 n(%) n(%) χ2
Inadequate or no lighting in living room, sleeping room, kitchen, toilet 25.743*** 15.469***
  No 237 (77.2) 53 (51) 269 (73.7) 21 (45.7)
  Yes 70 (22.8) 51 (49) 96 (26.3) 25 (54.3)
Holes in floor, leaking pipes 23.622*** 2.447
  No 250 (81.4) 60 (57.7) 271 (74.2) 39 (84.8)
  Yes 57 (18.6) 44 (42.3) 94 (25.8) 7 (15.2)
Inadequate environment of toilet and bathroom 37.909*** 9.266**
  No 172 (56) 22 (21.2) 182 (49.9) 12 (26.1)
  Yes 135 (44) 82 (78.8) 183 (50.1) 34 (73.9)
Inadequate environment of kitchen 3.042 3.721
  No 236 (76.9) 71 (68.3) 278 (76.2) 29 (63)
  Yes 71 (23.1) 33 (31.7) 87 (23.8) 17 (37)
Inadequate heating and cooling 0.829 0.722
  No 257 (83.7) 83 (79.8) 304 (83.3) 36 (78.3)
  Yes 50 (16.3) 21 (20.2) 61 (16.7) 10 (21.7)
Personal safety 2.457a 4.822a,*
  No 277 (90.2) 99 (95.2) 330 (90.4) 46 (100)
  Yes 30 (9.8) 5 (4.8) 35 (9.6) 0 (0)
Difficulty entering/leaving the house 9.297** 13.901***
  No 241 (78.5) 66 (63.5) 283 (77.5) 24 (52.2)
  Yes 66 (21.5) 38 (36.5) 82 (22.5) 22 (47.8)
Difficulty entering/leaving rooms in the house 36.490*** 17.870***
  No 245 (79.8) 51 (49) 275 (75.3) 21 (45.7)
  Yes 62 (20.2) 53 (51) 90 (24.7) 25 (54.3)
Mean (SD) Mean (SD) tValue Mean (SD) Mean (SD) tValue
Number of housing environmental hazards 1.762 (1.91) 3.144 (1.54) −6.689*** 1.995 (1.90) 3.043 (1.76) −3.549***

Logistic Regression Analysis of Predictors of Falls and Recurrent Falls

Model 1 Model 2
Fallers Recurrent Fallers
Variable Odds Ratio (95% CI) pValue Odds Ratio (95% CI) pValue
Age (reference = 65–80) 0.269 (0.138, 0.527) 0.000
Living alone (reference = with family or others) 2.077 (1.057, 4.083) 0.034
MDS-DRS 1.108 (1.016, 1.209) 0.021
Unsteady gait (reference = no) 6.925 (2.653, 18.075) 0.000 10.052 (1.435, 70.395) 0.020
MDS-IADL 0.827 (0.777, 0.882) 0.000 0.561 (0.456, 0.690) 0.000
MDS-Pain 1.680 (1.212, 2.329) 0.002 11.006 (4.167, 29.071) 0.000
Number of housing environmental hazards 1.659 (1.376, 2.001) 0.000 1.557 (1.166, 2.080) 0.003
Use of an assistive device (reference = no) 7.074 (3.245, 15.422) 0.000 22.564 (4.687, 108.631) 0.000
Fear of falling (reference = no) 5.106 (1.469, 17.744) 0.010
MDS-ADL 0.319 (0.142, 0.718) 0.006
Reduced vision (reference = no) 30.231 (6.629, 137.854) 0.000
Constant 0.007 0.000 0.007 0.021

Yoo, I.-Y. (2011). Recurrent Falls Among Community-Dwelling Older Koreans: Prevalence and Multivariate Risk Factors. Journal of Gerontological Nursing, 37(9), 28–40.

  1. It is clinically important to separate older adults who are onetime fallers from recurrent fallers.

  2. The findings of this study revealed significant predictors of falls and recurrent falls.

  3. Fear of falling, low scores related to performance of activities of daily living, and reduced vision were three risk factors that distinguished fallers from recurrent fallers.

  4. Nurses should use comprehensive assessment tools such as the Resident Assessment Instrument-Home Care© to identify recurrent fall risk factors and develop and apply individualized preventive programs for older adults.

Keypoints

Authors

Dr. Yoo is Assistant Professor, Department of Nursing, Jeonju University, Jeonju, Korea.

The author discloses that she has no significant financial interests in any product or class of products discussed directly or indirectly in this activity. The author acknowledges financial support from Jeonju University in Korea and thanks the Korean older participants.

Address correspondence to In-Young Yoo, PhD, RN, Assistant Professor, Department of Nursing, Jeonju University, 1200, Hyoja-dong 3Ga, Jeonju, 560–759, Korea; e-mail: .yiny@jj.ac.kr

10.3928/00989134-20110503-01

Sign up to receive

Journal E-contents