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Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan
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Research paper
Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan C.-K. Liang a,b,d, M.-Y. Chou a,d,e, L.-N. Peng c,d, M.-C. Liao a,f, C.-L. Chu a,g, Y.-T. Lin a,b,d,*, L.-K. Chen c,d,** a
Geriatric Medicine Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan Division of Neurology, Department of Internal Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan d Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan e Department of Family Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan f Department of Emergency Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan g Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 20 April 2014 Accepted 6 June 2014 Available online xxx
Purpose: To evaluate the effectiveness of adding gait speed to the history of falls in predicting falls among men aged 80 years and older in Taiwan. Methods: This prospective cohort study recruited 230 ambulatory men aged 80 years and older in 2012 and followed for 12 months. In addition to demographic characteristics and history of falls, a comprehensive geriatric assessment was performed for all study subjects. Gait speed was obtained by the 6-m walk and three different cut-offs (< 0.5, 0.8 and < 1.0 m/s) were tested in improving the ability of predicting subsequent falls by using history of falls. Results: Among all subjects (mean age: 85.5 4.0 years), 26.1% (60/230) reported falls during follow-up period. Univariate analysis showed that polypharmacy, urinary incontinence, history of falls, pain, poorer baseline physical function, depressive mood, and gait speed < 0.5 m/s were associated with falls. Logistic regression showed that history of falls (OR: 4.255, 95% CI 2.089–8.667; P < 0.001), pain (OR: 2.674, 95% CI 1.332–5.369; P = 0.006), older age (OR: 1.128, 95% CI 1.031–1.234; P = 0.008), and slow gait speed (OR: 2.964, 95% CI 1.394–6.300; P = 0.005) were all independent risk factors for falls. Fast gait speed (defined as 1 m/s) was a protective factor for falls, even among subjects with history of falls, but slow gait speed (defined as < 0.5 m/s) was an independent risk factor even among subjects without history of falls. Conclusions: Combined history of falls and gait speed is a simple and effective tool in risk assessment of falls among older old population. ß 2014 Published by Elsevier Masson SAS.
Keywords: Gait speed Fall Oldest old Men History of falls
1. Introduction Taiwan has become an aging society in 1993 and is estimated to become an aged society by 2017, which makes Taiwan the fastest aging country in the world [1]. Population aging may cause various
* Corresponding author. Geriatric Medicine Center and Division of Neurology, Kaohsiung Veterans General Hospital, Taiwan; No. 386, Ta-Chung 1st RD, Zuoying District, Kaohsiung 813, Taiwan. Tel.: +886 7 342 2121x2091; fax: +886 7 348 1478. ** Corresponding author. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, No. 201, Sec 2, Shih-Pai Road, Taipei, Taiwan. Tel.: +886 2 2875 7830; fax: +886 2 2875 7711. E-mail addresses: [email protected] (C.-K. Liang), [email protected] (M.-Y. Chou), [email protected] (L.-N. Peng), [email protected] (M.-C. Liao), [email protected] (C.-L. Chu), [email protected] (Y.-T. Lin), [email protected] (L.-K. Chen).
challenges to the health care systems, and falls have been associated with strong risks to the health of older people. Generally speaking, nearly a third of elderly people may experience falls every year, and more than 50% of these falls occurred during certain form of locomotion [2]. Falls are the most common cause of injury-related deaths among people aged 75 and older, which is the same as in nonfatal injuries among females aged 85 years and older [3]. Falls are also a serious public health issue that are highly associated with morbidity and mortality of older people [4], and a multifactorial approach is considered the most effective strategy to prevent falls [5]. In fall prevention programs, screening the risk of falls is the first and the most critical step to stop the vicious cycle. Screening the history of previous falls is a quick, simple, and effective tool for the first step of risk assessment, which was supported by both the American Geriatrics Society (AGS) and the British Geriatrics
http://dx.doi.org/10.1016/j.eurger.2014.06.034 1878-7649/ß 2014 Published by Elsevier Masson SAS.
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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Society (BGS) [6]. History of fall is the strongest predictor for subsequent falls [7–9], as well as the risk for fractures among people aged 45 years and over [10]. In the AGS-BGS guideline, after screening the history of falls, evaluating the gait/balance disturbance was the second step in the risk assessment, and a number of tests were recommended, such as Get up and Go test, Timed Up and Go test, the Berg Balance Scale, and the Performance-Oriented Mobility Assessment [6]. However, currently, no sufficient evidence supported using a specific test for balance and gait disturbance to predict subsequent falls [6]. Among all these tests, gait speed has been recognized as a simple screening test for various adverse health outcomes of older people, such as mobility disability, institutionalization, death, and cognitive decline [11]. It has been reported that the gait speed 0.8 m/s was associated with a higher risk of adverse health outcomes, [11,12] and a gait speed slower than 1.0 m/s may increase the risk of mortality [13,14]. However, studies focused on the effect of gait speed in predicting falls among people aged over 80 years were scarce. Moreover, some previous studies suggested to re-define the cut-off of slower gait speed among oldest old population due to their survival effect [15,16]. Although the history of falls is an effective screening tool for falls, it does not completely reflect the current health status, physical function and risk for falls of older people. Therefore, the main aim of this study was to evaluate the role of gait speed in history of previous falls to predict subsequent falls among people aged 80 years and older in Taiwan.
as currently using > 4 prescription drugs for over 2 weeks), depressive symptoms using the 15-item Chinese Geriatric Depression Scale (GDS-15, a score of 5 and more was defined as depressive) [18], nutritional status using the Mini Nutritional Assessment-short form (MNA-SF, malnutrition was defined as the MNA-SF scores of 11 and less) [19], cognitive function determined by the Chinese version of the Mini-Mental State Examination (MMSE, the scores less than 24 was defined as cognitive impairment) [20], the instrumental activities of daily living (using the Lawton-Brody Instrumental ADL scale, IADL) [21], and quality of life (using European quality of life–five domains, EQ5D) [22]. 2.2.3. Gait speed measurement A timed 6-m walk was performed for all participants at their usual walking speed with a static start throughout a 6-m distance without deceleration [23,24], and the time consumed was taken by a fixed study nurse with a stop watch (HS-70 W, Casio computer co. LTC, Tokyo, Japan). The test allowed the subjects to start with a cane or a walker as needed. Three different cut-offs for slower gait speed (< 0.5 m/s [25], 0.8 m/s [11,12], and < 1 m/s [13,14]) were tested to evaluate the effect in improving fall prevention among the study subjects.
2. Methods
2.2.4. Definitions of falls In this study, a fall was defined as an unintentional change in position resulting in coming to rest on the ground or other lower levels [26]. For all study subjects, the occurrence of falls was carefully recorded during the 12-month follow-up period.
2.1. Participants and study design
2.3. Statistical analysis
This prospective observational cohort study invited all residents living in the Veterans Home, a retirement community, in southern Taiwan in January of 2012. For those who participated in the study, demographic data were collected and the comprehensive geriatric assessments were preformed to them twice a year after the enrollment. Subjects with the following conditions were excluded for study:
In this study, continuous variables in the text and tables were expressed as means with standard deviation, and categorical data were expressed as percentages. Comparisons between dichotomous and ordinal variables were done by using the Chi2 test or Fisher’s exact test when appropriate, and comparisons between continuous variables were done using the independent Student’s t-test or Mann-Whiney U test when appropriate. Multivariate logistic regression analysis was used to determine the independent predictive factors for subsequent falls in the following year and the candidate predictors with a P value < 0.2 in univariate analysis were selected to enter the regression model. For the interaction of history of falls and gait speed, we combined the history of falls and slower gait speeds using different definitions, i.e. < 0.5 m/s, 0.8 m/s or < 1 m/s. The predictive effect was also analyzing by multivariate stepwise logistic regression analysis after adjusting confounders.
unable to walk with a walking aid; unable to communicate with research staff; unable to obtain informed consent from participants; subjects with their expected life expectancy shorter than 12 months.
A total of 278 people aged 80 years and older were screened, and 7 of them were excluded (5 people were unable to walk, 2 person with incomplete fall history) for study. Among eligible study subjects (n = 271), 41 people did not complete the 6-m walk test, so, only a total of 230 residents were enrolled in this study. The whole study was approved by the Institutional Review Board of Kaohsiung Veterans General Hospital. 2.2. Data collection 2.2.1. Demographic characteristics Three well-trained research nurses interviewed all participants to collect the demographic data, including age, smoking habit, habitual alcohol use status, presence of pain, sleep problems, urinary incontinence, medical history, co-morbidities by using Charlson Comorbidity Index [17], and body mass index (BMI) were obtained for each study subject. 2.2.2. Comprehensive Geriatric Assessment (CGA) The research nurses performed CGA for all participants, which included visual and hearing impairment, polypharmacy (defined
3. Results 3.1. Demographic characteristics and functional status Overall, 230 subjects (mean age: 85.5 4.0 years, range: 80–101 years, all males) participated in this study and 27.4% of them (63/230) reported falls in the previous year. Among them, 26.1% (60/230) reported fall events during the follow-up period. Table 1 summarized the demographic characteristics and functional status of the study participants. Approximately 40% of the study subjects had sleeping problems, urinary incontinence, cognitive impairment, or depressive symptoms. Those who developed falls in the follow-up period had significantly slower gait speed than those who developed no fall event (0.67 0.33 m/s vs 0.78 0.32 m/s, P = 0.021), lower scores in EQ5D (61.1 22.9 vs 68.1 15.7, P = 0.039), and higher prevalence of urinary incontinence (46.7% vs 27.6%, P = 0.007), presence of pain (61.7% vs 41.2%, P = 0.001), and depressive symptoms (43.3% vs 24.7%, P = 0.007).
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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Table 1 Comparisons of baseline characteristics among subjects with or without falls during the 12-month follow-up. Total
Fall (+)
Fall (–)
Variables
% or mean SD (n)
% or mean SD (n)
% or mean SD (n)
P value
Age Current smoker (yes) Current drinker (yes) Sleep problems (yes) Urine incontinence (yes) Hx of fall in past 1 year Presence of pain (yes) BMI CCI Polypharmacy (yes) Visual impairment (yes) Hearing impairment (yes) Baseline IADL EQ5D VAS Scores Cognitive impairment (yes) Depressive symptoms (yes) Risk of malnutrition (yes) Gait speed (m/s)
85.5 4.0 (n = 230) 43/230 (18.7%) 65/230 (28.3%) 88/230 (38.3%) 75/230 (32.6%) 63/230 (27.4%) 107/230 (46.5%) 24.1 3.5 (n = 169) 1.01 1.48 (n = 230) 146/230 (63.5%) 183/230 (79.6%) 149/230 (64.8%) 6.9 1.3 (n = 230) 66.2 18.1 (n = 203) 88/230 (38.3%) 68/230 (29.6%) 51/230 (22.2%) 0.75 0.32 (n = 230)
86.1 4.2 (n = 60) 10/60 (16.7%) 15/60 (25.0%) 26/60 (43.3%) 28/60 (46.7%) 29/60 (48.3%) 37/60 (61.7%) 24.2 3.9 (n = 37) 1.08 1.52 (n = 60) 45/60 (75.0%) 48/60 (80.0%) 44/60 (73.3%) 6.6 1.6 (n = 60) 61.1 22.9 (n = 54) 24/60 (40.0%) 26/60 (43.3%) 12/60 (20.0%) 0.67 0.33 (n = 60)
85.3 3.9 (n = 170) 33/170 (19.4%) 50/170 (29.4%) 62/170 (36.5%) 47/170 (27.6%) 34/170 (20.0%) 70/170 (41.2%) 24.1 3.4 (n = 132) 0.99 1.46 (n = 170) 101/170 (59.4%) 135/170 (79.4%) 105/170 (61.8%) 7.1 1.1 (n = 170) 68.1 15.7 (n = 149) 64/170 (37.6%) 42/170 (24.7%) 39/170 (22.9%) 0.78 0.32 (n = 170)
0.110 0.639 0.514 0.347 0.007 < 0.001 0.001 0.850 0.669 0.031 0.923 0.107 0.038 0.039 0.747 0.007 0.637 0.021
BMI: Body mass index; CCI: Charson Comorbisity Index.
3.2. Independent risk factors for falls All candidate predictors with the P value < 0.2 in the univariate analysis were entered into the logistic regression analysis, including age, polypharmacy, hearing impairment, urinary incontinence, history of falls, EQ5D scores, presence of pain, IADL, depressive symptoms, and gait speed. Results showed that history of falls in the previous year (odds ratio [OR]: 4.255, 95% confidence interval [CI]: 2.089–8.667; P < 0.001), presence of pain (OR: 2.674, 95% CI: 1.332–5.369; P = 0.006) and older age (OR: 1.128, 95% CI 1.031–1.234; P = 0.008) were all independent risk factors for falls (Table 2). Although the gait speed per se was not an independent predictive factor for falls, but the slower gait speed (defined by < 0.5 m/s) was independently associated with falls in the subsequent year (OR: 2.964, 95% CI: 1.394–6.300; P = 0.005). 3.3. Synergic effect of history of falls and gait speed Adjusted for age, polypharmacy, hearing impairment, urinary incontinence, EQ5D scores, presence of pain, IADL, and depressive symptoms, we evaluated the potentially synergic effect of history of falls and slower gait speed in fall prediction (Table 3). A slower gait speed did not significantly add predictive value on history of falls if it was defined as 0.8 m/s (OR: 4.044, 95% CI: 1.504– 10.869, P = 0.006 for subjects with history of falls and slower gait speed; OR: 4.290, 95% CI: 1.379–13.346, P = 0.012 for subjects with history of falls and faster gait speed). Moreover, if slower gait speed was defined as < 0.5 m/s, a strong synergic effect was identified
(OR: 3.308, 95% CI: 1.280–8.549, P = 0.014 for subjects with slow gait speed without history of falls; OR: 4.423, 95% CI: 1.857– 10.535; P = 0.001 for subjects with history of falls and no slow gait speed; OR: 10.920, 95% CI: 3.423–34.839, P < 0.001 for subjects with history of falls and slow gait speed). In addition, among subjects with history of previous falls, a faster gait speed ( 1 m/s) was a significant protective factor for subsequent falls. 4. Discussion 4.1. Risk for falls among older people This prospective cohort study evaluated the effectiveness of adding gait speed to history of previous falls in predicting falls in the subsequent year among male people aged 80 years and older in Taiwan. Results of this study provided risk stratification for octogenarians with the same history of falls in subsequent falls, which highlighted the benefits of this combined approach to improve identifying people with higher risk of falls. In this study, 26.1% of all study subjects fell at least once in the follow-up period. Older age, history of falls in the previous year, and presence of pain were all independent risk factors for falls in this study, which were similar to previous studies. However, subjects with previous history of fall plus slower gait speed (defined as < 0.5 m/s) were at a very high risk of falls. On the contrary, subjects with their gait speed 1 m/s were not at a higher risk of falls even if they had a previous history of falls. These findings strengthened the AGS-BGS guidelines in the fall prevention programs, and were a simple and efficient approach to stratify risk of falls for people aged 80 years and older.
Table 2 Independent risk factors for falls among men aged 80 years and older during 12-month follow-up. Independent Variablesa
Unadjusted OR
95% CI
P value
Adjusted OR
95% CI
P value
History of falls in past 1 year Age (years) Presence of pain (yes) Gait speed (m/s) Gait speed with cut-off point < 1 m/s Gait speed with cut-off point 0.8 m/s Gait speed with cut-off point < 0.5 m/s
3.742 1.059 2.298 0.319 – – 2.987
1.992–7.030 0.987 1.257–4.202 0.119–0.854 – – 1.577–5.656
< 0.001 1.136 0.007 0.023 – – 0.001
4.255 1.128 2.674 – – – 2.964
2.089–8.667 1.031–1.234 1.332–5.369 – – – 1.394–6.300
< 0.001 0.008 0.006 – – – 0.005
a Covariates adjusting for age, polypharmacy, hearing impairment, urine incontinence, history of falling in past 1 year, EQ5D VAS scores, with symptoms of pain, IADL, and depressive symptoms based on GDS-15, and gait speed (m/s).
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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Table 3 Synergistic effect of history of falls and slow gait speed in predicting subsequent falls. Cut-off point of slower gait speed < 1 m/s Dependent variablesa History History History History
of of of of
falls falls falls falls
(–) (–) (+) (+)
and and and and
slow slow slow slow
gait gait gait gait
speed speed speed speed
(–) (+) (–) (+)
Cut-off point of slower gait speed 0.8 m/s
Cut-off point of slower gait speed < 0.5 m/s
Adjusted OR
95% CI
P value
Adjusted OR
95% CI
P value
Adjusted OR
95% CI
P value
Reference – – 3.052
– – – 1.054–8.840
– – – 0.040
Reference – 4.290 4.044
– – 1.379–13.346 1.504–10.869
– – 0.012 0.006
Reference 3.308 4.423 10.920
– 1.280–8.549 1.857–10.535 3.423–34.839
– 0.014 0.001 < 0.001
a Covariates adjusting for age, polypharmacy, hearing impairment, urine incontinence, EQ5D VAS scores, with symptoms of pain, IADL, and depressive symptoms based on GDS-15.
4.2. Gait speed and falls Slower gait speed has been recognized to be associated with risk of falls for the elderly, which was a simple and effective test in clinical practice [8,23,27,28]. The E´pidemiologie de l’Osteoporose study, EPIDOS, surveyed 7575 community-dwelling French women aged 75 years and older showing that the lowest quartile of gait speed had a 1.4 higher risk (95% CI 1.1–1.6) of femoral neck fracture than the highest quartile during a mean 1.9 years followup [27]. Besides, Chu et al. enrolled 1517 elderly Hong Kong people and disclosed that the faster gait speed was a significant protective factor against falls [8]. Despite all the supporting evidences, the cut-off of gait speed in predicting falls remained unclear, especially for those who aged 80 years and older. Montero-Odasso et al. reported that the gait speed < 0.7 m/s was significantly predictive for falls in the coming two years (RR = 5.4, 95% CI: 2.0– 4.3) by a study sample with similar age to ours (102 participants aged 75 years and older) [28]. Compared to the previous studies, CGA was performed for all study subjects to identify potential confounders in predicting subsequent falls, including visual and hearing impairment, malnutrition, presence of pain, and comorbidities. In 2012, Taekema et al. found that the gait speed of < 0.46 m/s in men was associated with a higher risk of mortality during 12-year follow-up [14]. In this study, a gait speed of < 0.5 m/s was a stronger predictive factor for falls than the slower gait speed using the cut-off of 0.8 or 1 m/s.
physiological, functional and psychological conditions of old women with a recent history of falls [32]. Results of this study showed that older people with history of falls eventually were at no higher risk for falls when their gait speed exceeded 1.0 m/s, so they may not be the most prioritized population for fall prevention programs even though they reported a previous history of falls. In particular, those who had a previous history of falls and the gait speed of < 0.5 m/s, a stronger intervention program should be introduced to prevent subsequent falls because they were at very high risk of falls. 4.4. Limitations Despite all the research efforts went into this study, there were still several limitations. First, the study sample was homogenous in their demographic characteristics that they were all males, and all veterans were living in the same retirement community. However, we believe results of this study were still of great implications to effectively conduct fall prevention programs in the communities. Second, among all eligible study subjects, 27 refused the evaluation of EQ5D, and 61 of them refused being measured for their BMI. These conditions may lessened the statistic power, however, results of this study still clearly showed the benefits of adding gait speed measurements to history of falls in predicting falls. Third, a self-report bias of fall episodes may exist in this study because older veterans may be reluctant to report falls due to their self-esteem.
4.3. Combined approach of history of falls and gait speed 4.5. Conclusions The ‘‘history of fall’’ was a strong risk factor for subsequent falls traditionally, but physical conditions of older people with the same history of falls may differ extensively. Rekeneire et al. reported that fallers might eventually have certain subclinical deficits that were overlooked by clinicians [29]. Moreover, analysis of gait characteristics among older people with a previous history of falls showed that their gait speed varied extensively from 0.18 to 1.07 m/s [30], which clearly demonstrated the great heterogeneity of these people that were classified as at the same risk for falls due to the history of previous falls. Therefore, adding the dynamic gait speed measurement to the static history of falls improved the effectiveness in risk stratification for fall prevention programs. In particular, history of falls is not a modifiable risk factor, but a slower gait speed may be improved through certain intervention programs. In this study, the history of fall remained to be a strong independently risk factor for falls no matter slow gait speed was defined as < 0.5 or 0.8 m/s. It has been reported that adequate resistance training would improve the gait speed, even among people aged 90 years and older [31], and the 6month multidimensional training program may sustain the beneficial effects upon the increasing gait speed, as well as the
Among men aged 80 years and older living in the retirement communities, the history of previous falls alone was not a single best risk factor for subsequent falls. A gait speed 1 m/s was protective against subsequent falls in spite of the presence of history of previous falls. On the other hand, for subjects with the history of previous falls and a slow gait speed (< 0.5 m/s), a stronger intervention program should be introduced due to the disproportionately high risk of fall in the following year. Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgement The study was supported by the Veteran Affairs Council, R.O.C (Grant number: VAC101-C1 and VAC102-C1) and all authors declare no conflicts of interest. The study group thanks all staff in the Gangshan Veterans Home for their valuable assistance in obtaining the information.
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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Ethical approval: The whole study has been approved by the Institutional Review Board of Kaohsiung Veterans General Hospital, Taiwan.
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[15] Cesari M, Onder G, Zamboni V, Manini T, Shorr RI, Russo A, et al. Physical function and self-rated health status as predictors of mortality: results from longitudinal analysis in the ilSIRENTE study. BMC Geriatry 2008;8:34. [16] Taekema DG, Gussekloo J, Westendorp RG, de Craen AJ, Maier AB. Predicting survival in oldest old people. Am J Med 2012;125:1188e1–94e1. [17] Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373–83. [18] Liao YC, Yeh TL, Ko HC, et al. Geriatric depression scale (GDS) – a preliminary report of reliability and validity for Chinese version. Changhua Med J 1995;1:11–7 [in Chinese]. [19] Kaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Cederholm T, et al. Validation of the Mini Nutritional Assessment Short-Form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging 2009;13:782–8. [20] Tombaugh TN, McIntyre NJ. The mini-mental state examination: a comprehensive review. J Am Geriatr Soc 1992;40:922–35. [21] Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 1969;9:179–86. [22] Rabin R, De Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med 2001;33:337–43. [23] Liu LK, Lee WJ, Liu CL, Chen LY, Lin MH, Peng LN, et al. Age-related skeletal muscle mass loss and physical performance in Taiwan: implications to diagnostic strategy of sarcopenia in Asia. Geriatr Gerontol Int 2013;13:964–71. [24] Lee WJ, Liu LK, Peng LN, Lin MH, Chen LK, Research ILAS Group. Comparisons of sarcopenia defined by IWGS and EWGSOP criteria among older people: results from the I-Lan longitudinal aging study. J Am Med Dir Assoc 2013;14:528e1–7. [25] Biderman A, Cwikel J, Fried AV, Galisnky D. Depression and falls among community dwelling elderly people: a search for common risk factors. J Epidemiol Community Health 2002;56:631–6. [26] A report of the Kellogg International Work Group on the prevention of falls by the elderly. The prevention of falls in later life. Dan Med Bull 1987;34:1–24. [27] Dargent-Molina P, Favier F, Grandjean H, et al. Fall-related factors and risk of hip fracture: the EPIDOS prospective study. Lancet 1996;348:145–9. [28] Montero-Odasso M, Schapira M, Soriano ER, Varela M, Kaplan R, Camera LA, Mayorga LM. Gait velocity as a single predictor of adverse events in healthy senior aged 75 years and older. J Gerontol Med Sci 2005;60:1304–9. [29] De Rekeneire N, Visser M, Peila R, Nevitt MC, Cauley JA, Tylavsky FA, et al. Is a fall just a fall: correlates of falling in healthy older persons. The Health, Aging and Body Composition Study. J Am Geriatr Soc 2003;51:841–6. [30] Barak Y, Wagenaar RC, Holt KG. Gait characteristics of elderly people with a history of falls: a dynamic approach. Phys Ther 2006;86:1501–10. [31] Idland G, Sylliaas H, Mengshoel AM, Pettersen R, Bergland A. Progressive resistance training for community-dwelling women aged 90 or older: a single-subject experimental design. Disabil Rehabil 2014;36(15):1240–8. [32] Beyer N, Simonsen L, Bu¨low J, Lorenzen T, Jensen DV, Larsen L, et al. Old women with a recent fall history show improved muscle strength and function sustained for six months after finishing training. Aging Clin Exp Res 2007;19:300–9.
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Research paper
Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan C.-K. Liang a,b,d, M.-Y. Chou a,d,e, L.-N. Peng c,d, M.-C. Liao a,f, C.-L. Chu a,g, Y.-T. Lin a,b,d,*, L.-K. Chen c,d,** a
Geriatric Medicine Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan Division of Neurology, Department of Internal Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan d Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan e Department of Family Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan f Department of Emergency Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan g Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 20 April 2014 Accepted 6 June 2014 Available online xxx
Purpose: To evaluate the effectiveness of adding gait speed to the history of falls in predicting falls among men aged 80 years and older in Taiwan. Methods: This prospective cohort study recruited 230 ambulatory men aged 80 years and older in 2012 and followed for 12 months. In addition to demographic characteristics and history of falls, a comprehensive geriatric assessment was performed for all study subjects. Gait speed was obtained by the 6-m walk and three different cut-offs (< 0.5, 0.8 and < 1.0 m/s) were tested in improving the ability of predicting subsequent falls by using history of falls. Results: Among all subjects (mean age: 85.5 4.0 years), 26.1% (60/230) reported falls during follow-up period. Univariate analysis showed that polypharmacy, urinary incontinence, history of falls, pain, poorer baseline physical function, depressive mood, and gait speed < 0.5 m/s were associated with falls. Logistic regression showed that history of falls (OR: 4.255, 95% CI 2.089–8.667; P < 0.001), pain (OR: 2.674, 95% CI 1.332–5.369; P = 0.006), older age (OR: 1.128, 95% CI 1.031–1.234; P = 0.008), and slow gait speed (OR: 2.964, 95% CI 1.394–6.300; P = 0.005) were all independent risk factors for falls. Fast gait speed (defined as 1 m/s) was a protective factor for falls, even among subjects with history of falls, but slow gait speed (defined as < 0.5 m/s) was an independent risk factor even among subjects without history of falls. Conclusions: Combined history of falls and gait speed is a simple and effective tool in risk assessment of falls among older old population. ß 2014 Published by Elsevier Masson SAS.
Keywords: Gait speed Fall Oldest old Men History of falls
1. Introduction Taiwan has become an aging society in 1993 and is estimated to become an aged society by 2017, which makes Taiwan the fastest aging country in the world [1]. Population aging may cause various
* Corresponding author. Geriatric Medicine Center and Division of Neurology, Kaohsiung Veterans General Hospital, Taiwan; No. 386, Ta-Chung 1st RD, Zuoying District, Kaohsiung 813, Taiwan. Tel.: +886 7 342 2121x2091; fax: +886 7 348 1478. ** Corresponding author. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, No. 201, Sec 2, Shih-Pai Road, Taipei, Taiwan. Tel.: +886 2 2875 7830; fax: +886 2 2875 7711. E-mail addresses: [email protected] (C.-K. Liang), [email protected] (M.-Y. Chou), [email protected] (L.-N. Peng), [email protected] (M.-C. Liao), [email protected] (C.-L. Chu), [email protected] (Y.-T. Lin), [email protected] (L.-K. Chen).
challenges to the health care systems, and falls have been associated with strong risks to the health of older people. Generally speaking, nearly a third of elderly people may experience falls every year, and more than 50% of these falls occurred during certain form of locomotion [2]. Falls are the most common cause of injury-related deaths among people aged 75 and older, which is the same as in nonfatal injuries among females aged 85 years and older [3]. Falls are also a serious public health issue that are highly associated with morbidity and mortality of older people [4], and a multifactorial approach is considered the most effective strategy to prevent falls [5]. In fall prevention programs, screening the risk of falls is the first and the most critical step to stop the vicious cycle. Screening the history of previous falls is a quick, simple, and effective tool for the first step of risk assessment, which was supported by both the American Geriatrics Society (AGS) and the British Geriatrics
http://dx.doi.org/10.1016/j.eurger.2014.06.034 1878-7649/ß 2014 Published by Elsevier Masson SAS.
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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Society (BGS) [6]. History of fall is the strongest predictor for subsequent falls [7–9], as well as the risk for fractures among people aged 45 years and over [10]. In the AGS-BGS guideline, after screening the history of falls, evaluating the gait/balance disturbance was the second step in the risk assessment, and a number of tests were recommended, such as Get up and Go test, Timed Up and Go test, the Berg Balance Scale, and the Performance-Oriented Mobility Assessment [6]. However, currently, no sufficient evidence supported using a specific test for balance and gait disturbance to predict subsequent falls [6]. Among all these tests, gait speed has been recognized as a simple screening test for various adverse health outcomes of older people, such as mobility disability, institutionalization, death, and cognitive decline [11]. It has been reported that the gait speed 0.8 m/s was associated with a higher risk of adverse health outcomes, [11,12] and a gait speed slower than 1.0 m/s may increase the risk of mortality [13,14]. However, studies focused on the effect of gait speed in predicting falls among people aged over 80 years were scarce. Moreover, some previous studies suggested to re-define the cut-off of slower gait speed among oldest old population due to their survival effect [15,16]. Although the history of falls is an effective screening tool for falls, it does not completely reflect the current health status, physical function and risk for falls of older people. Therefore, the main aim of this study was to evaluate the role of gait speed in history of previous falls to predict subsequent falls among people aged 80 years and older in Taiwan.
as currently using > 4 prescription drugs for over 2 weeks), depressive symptoms using the 15-item Chinese Geriatric Depression Scale (GDS-15, a score of 5 and more was defined as depressive) [18], nutritional status using the Mini Nutritional Assessment-short form (MNA-SF, malnutrition was defined as the MNA-SF scores of 11 and less) [19], cognitive function determined by the Chinese version of the Mini-Mental State Examination (MMSE, the scores less than 24 was defined as cognitive impairment) [20], the instrumental activities of daily living (using the Lawton-Brody Instrumental ADL scale, IADL) [21], and quality of life (using European quality of life–five domains, EQ5D) [22]. 2.2.3. Gait speed measurement A timed 6-m walk was performed for all participants at their usual walking speed with a static start throughout a 6-m distance without deceleration [23,24], and the time consumed was taken by a fixed study nurse with a stop watch (HS-70 W, Casio computer co. LTC, Tokyo, Japan). The test allowed the subjects to start with a cane or a walker as needed. Three different cut-offs for slower gait speed (< 0.5 m/s [25], 0.8 m/s [11,12], and < 1 m/s [13,14]) were tested to evaluate the effect in improving fall prevention among the study subjects.
2. Methods
2.2.4. Definitions of falls In this study, a fall was defined as an unintentional change in position resulting in coming to rest on the ground or other lower levels [26]. For all study subjects, the occurrence of falls was carefully recorded during the 12-month follow-up period.
2.1. Participants and study design
2.3. Statistical analysis
This prospective observational cohort study invited all residents living in the Veterans Home, a retirement community, in southern Taiwan in January of 2012. For those who participated in the study, demographic data were collected and the comprehensive geriatric assessments were preformed to them twice a year after the enrollment. Subjects with the following conditions were excluded for study:
In this study, continuous variables in the text and tables were expressed as means with standard deviation, and categorical data were expressed as percentages. Comparisons between dichotomous and ordinal variables were done by using the Chi2 test or Fisher’s exact test when appropriate, and comparisons between continuous variables were done using the independent Student’s t-test or Mann-Whiney U test when appropriate. Multivariate logistic regression analysis was used to determine the independent predictive factors for subsequent falls in the following year and the candidate predictors with a P value < 0.2 in univariate analysis were selected to enter the regression model. For the interaction of history of falls and gait speed, we combined the history of falls and slower gait speeds using different definitions, i.e. < 0.5 m/s, 0.8 m/s or < 1 m/s. The predictive effect was also analyzing by multivariate stepwise logistic regression analysis after adjusting confounders.
unable to walk with a walking aid; unable to communicate with research staff; unable to obtain informed consent from participants; subjects with their expected life expectancy shorter than 12 months.
A total of 278 people aged 80 years and older were screened, and 7 of them were excluded (5 people were unable to walk, 2 person with incomplete fall history) for study. Among eligible study subjects (n = 271), 41 people did not complete the 6-m walk test, so, only a total of 230 residents were enrolled in this study. The whole study was approved by the Institutional Review Board of Kaohsiung Veterans General Hospital. 2.2. Data collection 2.2.1. Demographic characteristics Three well-trained research nurses interviewed all participants to collect the demographic data, including age, smoking habit, habitual alcohol use status, presence of pain, sleep problems, urinary incontinence, medical history, co-morbidities by using Charlson Comorbidity Index [17], and body mass index (BMI) were obtained for each study subject. 2.2.2. Comprehensive Geriatric Assessment (CGA) The research nurses performed CGA for all participants, which included visual and hearing impairment, polypharmacy (defined
3. Results 3.1. Demographic characteristics and functional status Overall, 230 subjects (mean age: 85.5 4.0 years, range: 80–101 years, all males) participated in this study and 27.4% of them (63/230) reported falls in the previous year. Among them, 26.1% (60/230) reported fall events during the follow-up period. Table 1 summarized the demographic characteristics and functional status of the study participants. Approximately 40% of the study subjects had sleeping problems, urinary incontinence, cognitive impairment, or depressive symptoms. Those who developed falls in the follow-up period had significantly slower gait speed than those who developed no fall event (0.67 0.33 m/s vs 0.78 0.32 m/s, P = 0.021), lower scores in EQ5D (61.1 22.9 vs 68.1 15.7, P = 0.039), and higher prevalence of urinary incontinence (46.7% vs 27.6%, P = 0.007), presence of pain (61.7% vs 41.2%, P = 0.001), and depressive symptoms (43.3% vs 24.7%, P = 0.007).
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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Table 1 Comparisons of baseline characteristics among subjects with or without falls during the 12-month follow-up. Total
Fall (+)
Fall (–)
Variables
% or mean SD (n)
% or mean SD (n)
% or mean SD (n)
P value
Age Current smoker (yes) Current drinker (yes) Sleep problems (yes) Urine incontinence (yes) Hx of fall in past 1 year Presence of pain (yes) BMI CCI Polypharmacy (yes) Visual impairment (yes) Hearing impairment (yes) Baseline IADL EQ5D VAS Scores Cognitive impairment (yes) Depressive symptoms (yes) Risk of malnutrition (yes) Gait speed (m/s)
85.5 4.0 (n = 230) 43/230 (18.7%) 65/230 (28.3%) 88/230 (38.3%) 75/230 (32.6%) 63/230 (27.4%) 107/230 (46.5%) 24.1 3.5 (n = 169) 1.01 1.48 (n = 230) 146/230 (63.5%) 183/230 (79.6%) 149/230 (64.8%) 6.9 1.3 (n = 230) 66.2 18.1 (n = 203) 88/230 (38.3%) 68/230 (29.6%) 51/230 (22.2%) 0.75 0.32 (n = 230)
86.1 4.2 (n = 60) 10/60 (16.7%) 15/60 (25.0%) 26/60 (43.3%) 28/60 (46.7%) 29/60 (48.3%) 37/60 (61.7%) 24.2 3.9 (n = 37) 1.08 1.52 (n = 60) 45/60 (75.0%) 48/60 (80.0%) 44/60 (73.3%) 6.6 1.6 (n = 60) 61.1 22.9 (n = 54) 24/60 (40.0%) 26/60 (43.3%) 12/60 (20.0%) 0.67 0.33 (n = 60)
85.3 3.9 (n = 170) 33/170 (19.4%) 50/170 (29.4%) 62/170 (36.5%) 47/170 (27.6%) 34/170 (20.0%) 70/170 (41.2%) 24.1 3.4 (n = 132) 0.99 1.46 (n = 170) 101/170 (59.4%) 135/170 (79.4%) 105/170 (61.8%) 7.1 1.1 (n = 170) 68.1 15.7 (n = 149) 64/170 (37.6%) 42/170 (24.7%) 39/170 (22.9%) 0.78 0.32 (n = 170)
0.110 0.639 0.514 0.347 0.007 < 0.001 0.001 0.850 0.669 0.031 0.923 0.107 0.038 0.039 0.747 0.007 0.637 0.021
BMI: Body mass index; CCI: Charson Comorbisity Index.
3.2. Independent risk factors for falls All candidate predictors with the P value < 0.2 in the univariate analysis were entered into the logistic regression analysis, including age, polypharmacy, hearing impairment, urinary incontinence, history of falls, EQ5D scores, presence of pain, IADL, depressive symptoms, and gait speed. Results showed that history of falls in the previous year (odds ratio [OR]: 4.255, 95% confidence interval [CI]: 2.089–8.667; P < 0.001), presence of pain (OR: 2.674, 95% CI: 1.332–5.369; P = 0.006) and older age (OR: 1.128, 95% CI 1.031–1.234; P = 0.008) were all independent risk factors for falls (Table 2). Although the gait speed per se was not an independent predictive factor for falls, but the slower gait speed (defined by < 0.5 m/s) was independently associated with falls in the subsequent year (OR: 2.964, 95% CI: 1.394–6.300; P = 0.005). 3.3. Synergic effect of history of falls and gait speed Adjusted for age, polypharmacy, hearing impairment, urinary incontinence, EQ5D scores, presence of pain, IADL, and depressive symptoms, we evaluated the potentially synergic effect of history of falls and slower gait speed in fall prediction (Table 3). A slower gait speed did not significantly add predictive value on history of falls if it was defined as 0.8 m/s (OR: 4.044, 95% CI: 1.504– 10.869, P = 0.006 for subjects with history of falls and slower gait speed; OR: 4.290, 95% CI: 1.379–13.346, P = 0.012 for subjects with history of falls and faster gait speed). Moreover, if slower gait speed was defined as < 0.5 m/s, a strong synergic effect was identified
(OR: 3.308, 95% CI: 1.280–8.549, P = 0.014 for subjects with slow gait speed without history of falls; OR: 4.423, 95% CI: 1.857– 10.535; P = 0.001 for subjects with history of falls and no slow gait speed; OR: 10.920, 95% CI: 3.423–34.839, P < 0.001 for subjects with history of falls and slow gait speed). In addition, among subjects with history of previous falls, a faster gait speed ( 1 m/s) was a significant protective factor for subsequent falls. 4. Discussion 4.1. Risk for falls among older people This prospective cohort study evaluated the effectiveness of adding gait speed to history of previous falls in predicting falls in the subsequent year among male people aged 80 years and older in Taiwan. Results of this study provided risk stratification for octogenarians with the same history of falls in subsequent falls, which highlighted the benefits of this combined approach to improve identifying people with higher risk of falls. In this study, 26.1% of all study subjects fell at least once in the follow-up period. Older age, history of falls in the previous year, and presence of pain were all independent risk factors for falls in this study, which were similar to previous studies. However, subjects with previous history of fall plus slower gait speed (defined as < 0.5 m/s) were at a very high risk of falls. On the contrary, subjects with their gait speed 1 m/s were not at a higher risk of falls even if they had a previous history of falls. These findings strengthened the AGS-BGS guidelines in the fall prevention programs, and were a simple and efficient approach to stratify risk of falls for people aged 80 years and older.
Table 2 Independent risk factors for falls among men aged 80 years and older during 12-month follow-up. Independent Variablesa
Unadjusted OR
95% CI
P value
Adjusted OR
95% CI
P value
History of falls in past 1 year Age (years) Presence of pain (yes) Gait speed (m/s) Gait speed with cut-off point < 1 m/s Gait speed with cut-off point 0.8 m/s Gait speed with cut-off point < 0.5 m/s
3.742 1.059 2.298 0.319 – – 2.987
1.992–7.030 0.987 1.257–4.202 0.119–0.854 – – 1.577–5.656
< 0.001 1.136 0.007 0.023 – – 0.001
4.255 1.128 2.674 – – – 2.964
2.089–8.667 1.031–1.234 1.332–5.369 – – – 1.394–6.300
< 0.001 0.008 0.006 – – – 0.005
a Covariates adjusting for age, polypharmacy, hearing impairment, urine incontinence, history of falling in past 1 year, EQ5D VAS scores, with symptoms of pain, IADL, and depressive symptoms based on GDS-15, and gait speed (m/s).
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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Table 3 Synergistic effect of history of falls and slow gait speed in predicting subsequent falls. Cut-off point of slower gait speed < 1 m/s Dependent variablesa History History History History
of of of of
falls falls falls falls
(–) (–) (+) (+)
and and and and
slow slow slow slow
gait gait gait gait
speed speed speed speed
(–) (+) (–) (+)
Cut-off point of slower gait speed 0.8 m/s
Cut-off point of slower gait speed < 0.5 m/s
Adjusted OR
95% CI
P value
Adjusted OR
95% CI
P value
Adjusted OR
95% CI
P value
Reference – – 3.052
– – – 1.054–8.840
– – – 0.040
Reference – 4.290 4.044
– – 1.379–13.346 1.504–10.869
– – 0.012 0.006
Reference 3.308 4.423 10.920
– 1.280–8.549 1.857–10.535 3.423–34.839
– 0.014 0.001 < 0.001
a Covariates adjusting for age, polypharmacy, hearing impairment, urine incontinence, EQ5D VAS scores, with symptoms of pain, IADL, and depressive symptoms based on GDS-15.
4.2. Gait speed and falls Slower gait speed has been recognized to be associated with risk of falls for the elderly, which was a simple and effective test in clinical practice [8,23,27,28]. The E´pidemiologie de l’Osteoporose study, EPIDOS, surveyed 7575 community-dwelling French women aged 75 years and older showing that the lowest quartile of gait speed had a 1.4 higher risk (95% CI 1.1–1.6) of femoral neck fracture than the highest quartile during a mean 1.9 years followup [27]. Besides, Chu et al. enrolled 1517 elderly Hong Kong people and disclosed that the faster gait speed was a significant protective factor against falls [8]. Despite all the supporting evidences, the cut-off of gait speed in predicting falls remained unclear, especially for those who aged 80 years and older. Montero-Odasso et al. reported that the gait speed < 0.7 m/s was significantly predictive for falls in the coming two years (RR = 5.4, 95% CI: 2.0– 4.3) by a study sample with similar age to ours (102 participants aged 75 years and older) [28]. Compared to the previous studies, CGA was performed for all study subjects to identify potential confounders in predicting subsequent falls, including visual and hearing impairment, malnutrition, presence of pain, and comorbidities. In 2012, Taekema et al. found that the gait speed of < 0.46 m/s in men was associated with a higher risk of mortality during 12-year follow-up [14]. In this study, a gait speed of < 0.5 m/s was a stronger predictive factor for falls than the slower gait speed using the cut-off of 0.8 or 1 m/s.
physiological, functional and psychological conditions of old women with a recent history of falls [32]. Results of this study showed that older people with history of falls eventually were at no higher risk for falls when their gait speed exceeded 1.0 m/s, so they may not be the most prioritized population for fall prevention programs even though they reported a previous history of falls. In particular, those who had a previous history of falls and the gait speed of < 0.5 m/s, a stronger intervention program should be introduced to prevent subsequent falls because they were at very high risk of falls. 4.4. Limitations Despite all the research efforts went into this study, there were still several limitations. First, the study sample was homogenous in their demographic characteristics that they were all males, and all veterans were living in the same retirement community. However, we believe results of this study were still of great implications to effectively conduct fall prevention programs in the communities. Second, among all eligible study subjects, 27 refused the evaluation of EQ5D, and 61 of them refused being measured for their BMI. These conditions may lessened the statistic power, however, results of this study still clearly showed the benefits of adding gait speed measurements to history of falls in predicting falls. Third, a self-report bias of fall episodes may exist in this study because older veterans may be reluctant to report falls due to their self-esteem.
4.3. Combined approach of history of falls and gait speed 4.5. Conclusions The ‘‘history of fall’’ was a strong risk factor for subsequent falls traditionally, but physical conditions of older people with the same history of falls may differ extensively. Rekeneire et al. reported that fallers might eventually have certain subclinical deficits that were overlooked by clinicians [29]. Moreover, analysis of gait characteristics among older people with a previous history of falls showed that their gait speed varied extensively from 0.18 to 1.07 m/s [30], which clearly demonstrated the great heterogeneity of these people that were classified as at the same risk for falls due to the history of previous falls. Therefore, adding the dynamic gait speed measurement to the static history of falls improved the effectiveness in risk stratification for fall prevention programs. In particular, history of falls is not a modifiable risk factor, but a slower gait speed may be improved through certain intervention programs. In this study, the history of fall remained to be a strong independently risk factor for falls no matter slow gait speed was defined as < 0.5 or 0.8 m/s. It has been reported that adequate resistance training would improve the gait speed, even among people aged 90 years and older [31], and the 6month multidimensional training program may sustain the beneficial effects upon the increasing gait speed, as well as the
Among men aged 80 years and older living in the retirement communities, the history of previous falls alone was not a single best risk factor for subsequent falls. A gait speed 1 m/s was protective against subsequent falls in spite of the presence of history of previous falls. On the other hand, for subjects with the history of previous falls and a slow gait speed (< 0.5 m/s), a stronger intervention program should be introduced due to the disproportionately high risk of fall in the following year. Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgement The study was supported by the Veteran Affairs Council, R.O.C (Grant number: VAC101-C1 and VAC102-C1) and all authors declare no conflicts of interest. The study group thanks all staff in the Gangshan Veterans Home for their valuable assistance in obtaining the information.
Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034
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EURGER-511; No. of Pages 5 C.-K. Liang et al. / European Geriatric Medicine xxx (2014) xxx–xxx
Ethical approval: The whole study has been approved by the Institutional Review Board of Kaohsiung Veterans General Hospital, Taiwan.
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Please cite this article in press as: Liang C-K, et al. Gait speed and risk assessment for falls among men aged 80 years and older: A prospective cohort study in Taiwan. Eur Geriatr Med (2014), http://dx.doi.org/10.1016/j.eurger.2014.06.034