Vol.:(0123456789)1 3

Aging Clin Exp Res (2018) 30:1–16 DOI 10.1007/s40520-017-0749-0

REVIEW

Tools for assessing fall risk in the elderly: a systematic review and meta-analysis

Seong-Hi Park1 

Received: 13 January 2017 / Accepted: 10 March 2017 / Published online: 3 April 2017 © Springer International Publishing Switzerland 2017

Keywords Accidental falls · Older adults · Sensitivity and specificity · Meta-analysis

Introduction

The world comprises aging societies [1]. Falls are second only to traffic accidents as the leading cause of injury-related deaths among the elderly worldwide [2, 3], and they are a major cause of both death and injury in people over 65 years of age [4, 5]. According to the World Health Organization (WHO) report, 28–35% of the population over age 65 fall each year, and the percentage of fallers among people over age 70 is 32–42%, suggesting an age-related increase in fall risk [6]. Falls cause 20–30% of mild-to-severe injuries [7], and >50% of those involve treatment requiring hospitalization [8]. Falls can limit daily activities and induce post-fall syndromes, such as dependence, loss of autonomy, immobilization, and depression [9, 10]. The WHO has warned that the number of injuries caused by falls will double by 2030 if a fall prevention strategy does not have a short-term effect [11].

Falls can occur in any setting; hence, fall prevention should start by considering environmental conditions and factoring them into the fall risk assessment [12]. Assess-ment tools for fall risks should accurately discriminate fall-ers from non-fallers in practice. Accordingly, before a fall risk assessment tool is used, its predictive validity should be sufficiently tested [13]. Although several studies have been conducted, results differ depending on subject charac-teristics, such as age, complications, and physical disability. Thus, the interpretation of findings is limited and unclear, and the findings cannot be generalized [14, 15]. Specifi-cally, diagnostic studies on fall risk assessment tools should include a quantitative report on the most representative

Abstract The prevention of falls among the elderly is arguably one of the most important public health issues in today’s aging society. The aim of this study was to assess which tools best predict the risk of falls in the elderly. Elec-tronic searches were performed using Medline, EMBASE, the Cochrane Library, CINAHL, etc., using the following keywords: “fall risk assessment”, “elderly fall screening”, and “elderly mobility scale”. The QUADAS-2 was applied to assess the internal validity of the diagnostic studies. Selected studies were meta-analyzed with MetaDisc 1.4. A total of 33 studies were eligible out of the 2,321 stud-ies retrieved from selected databases. Twenty-six assess-ment tools for fall risk were used in the selected articles, and they tended to vary based on the setting. The fall risk assessment tools currently used for the elderly did not show sufficiently high predictive validity for differentiating high and low fall risks. The Berg Balance scale and Mobil-ity Interaction Fall chart showed stable and high specific-ity, while the Downton Fall Risk Index, Hendrich II Fall Risk Model, St. Thomas’s Risk Assessment Tool in Falling elderly inpatients, Timed Up and Go test, and Tinetti Bal-ance scale showed the opposite results. We concluded that rather than a single measure, two assessment tools used together would better evaluate the characteristics of falls by the elderly that can occur due to a multitude of factors and maximize the advantages of each for predicting the occur-rence of falls.

* Seong-Hi Park shpark2015@sch.ac.kr

1 Department of Nursing, Soonchunhyang University, 22 Soonchunhyang-ro, Sinchang-myen, Asan-si, Chungcheongnam-do 31538, South Korea

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validity indices (i.e., sensitivity and specificity) and test outcome differences due to different fall risk factors. Fur-thermore, research is needed that will provide an overview of which assessment tool has the highest validity for the elderly at high risk for falls.

Some studies have attempted to do so, but they pre-sented results based only on a specific tool such as St. Thomas’s Risk Assessment Tool in Falling elderly inpa-tients (STRATIFY) [16] or different setting [17] or did not perform a quantitative analysis of the predictive validity [18–20]. Therefore, based on those studies, it is difficult to make a comprehensive conclusion as to which tools are effective for assessing fall risk among the elderly. Accord-ingly, this study was conducted to compare the diagnostic accuracy of several currently available fall risk assessment tools developed for the elderly, to identify the assessment tools most frequently used to discriminate fallers and non-fallers and the assessment tools having the highest predic-tive validity, and to provide scientific evidence for selecting the best tool to use in practice.

Methods

This study was conducted according to the guidelines of the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy [21] and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) State-ment [22].

Search strategy

We searched seven computer-based electronic databases: MEDLINE, EMBASE, the Cochrane Library, CINAHL Complete, KoreaMed, and National Digital Science Library. The search was performed in August 2014 and again in June 2016 to identify any new studies.

The search used the following terms: the name of vari-ous tools assessing fall risk (Berg Balance scale [BBS], Downton Fall Risk Index, Hendrich II Fall Risk Model etc.), “fall risk assessment”, “elderly fall screening”, “elderly mobility scale”, and “risk assessment”. In addi-tion, we used the search filters for diagnostic studies sug-gested by the Scottish Intercollegiate Guidelines Network [23]. For reference, an example of search strategy using Ovid-Medline was presented (Table 1).

Criteria for considering studies

Types of studies We included studies that reported the prog-nostic accuracy of a fall risk assessment tool and excluded studies reporting only sensitivity or specificity and provid-ing insufficient information to construct a 2 × 2 contingency

table. We excluded case–control studies, because they are prone to bias.

Types of participants We only included studies of people aged ≥60 years. We excluded studies in which people under age 60 participated, even if the average age was >60 years.

Indexed tests All fall risk assessment tools used in the elderly were included. We used the cut-off points suggested by the researchers in each study for the corresponding assessment tools.

Reference standard The reference standard was defined as ≥1 fall in which part of a person’s body other than the bottoms of the feet came to rest inadvertently on the ground [2, 13]. Each study’s own criteria were used to define a faller.

Types of outcome and assessment measures The true positive (TP), false positive (FP), true negative (TN), and false negative (FN) values were described for diag-nostic accuracy. Based on these indices, sensitivities, specificities, positive- and negative-likelihood ratios, diagnostic odds ratios, and summary receiver-operating

Table 1 Search strategy used to search Ovid-Medline

Searches

1 Morse fall risk assessment2 Johns Hopkins-fall risk assessment3 Hendrich fall risk assessment4 Berg Balance scale5 Conley fall risk assessment6 Schmid fall risk assessment7 St. Thomas risk assessment tool in fall-

ing elderly inpatient8 Downton fall risk assessment9 Fall risk assessment10 Berg balance scale11 Tinetti Balance scale12 Timed Up and Go13 Innes fall risk assessment14 Elderly fall screening15 Elderly Mobility scale16 Or/1-1517 Exp risk assessment/18 16 and 1719 Exp “Sensitivity and Specificity”/20 Sensitivity.tw21 Specificity.tw22 ((Pre-test or pre-test) adj probability).tw23 Post-test probability.tw24 Predictive value$.tw25 Likelihood ratio$.tw26 Or/19-2527 18 and 26

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characteristic (sROC) curves of the fall risk assessment tools were compared.

Study selection and data extraction

Literature selection criteria were as follows: (1) studies in which participants were aged ≥60 years; (2) studies in which TP, FP, TN, and FN findings were provided as indi-ces of the fall risk assessment tool’s predictive validity; and (3) peer-reviewed publications. We excluded retrospective studies, such as case–control studies, and studies that were not original, not diagnostic, and/or did not provide infor-mation required to compute the fall risk assessment tool’s diagnostic accuracy.

All duplicates among the initially retrieved articles were removed. Then, the inclusion and exclusion criteria were applied by examining the title and abstract, or the main text if it was difficult to make an accurate judgment based on the title and abstract. Data were extracted to a basic evi-dence table, which was used after examining the format’s appropriateness. The following items were listed in the evi-dence table: study type, study setting, subject age and sex distributions, fall risk assessment tools used and the cut-off points, fall criteria, follow-up duration, and diagnostic out-comes of the fall risk assessment tool (TP, FP, FN and TN). Based on those, a 2 × 2 contingency table was constructed. All processes were independently reviewed by two review-ers and determined by consensus. If their opinions did not converge, a third person was involved.

Quality assessment

Article quality was assessed by two reviewers using Qual-ity Assessment of Diagnostic Accuracy Studies-2 (QUA-DAS-2) [24]. QUADAS-2 is a tool to evaluate a diagnostic study’s quality by examining quality separately for “risk of bias” and “concerns regarding applicability”. Evaluation items for risk of bias were organized into four domains: patient selection, index test, reference standard, and flow and timing. Applicability of studies was evaluated for the first three key domains in each study and judged as “yes, no, or unclear”; “yes” indicated a low risk of bias, “no” indicated a high risk of bias, and “unclear” indicated a lack of sufficient information. Two reviewers independently coded the domains; a third person resolved disagreement.

Statistical analysis

The meta-analysis was performed with MetaDiSc 1.4 [25]. When pooled estimates are calculated in diagnos-tic meta-analyses, the use of a random-effects model is recommended to reflect inter-study heterogeneity [21]. Accordingly, pooled sensitivity, specificity, positive- and

negative-likelihood ratios, and diagnostic odds ratios were generally analyzed with a random-effects model. For sROC curve statistics, the area under the curve (AUC] and index Q* were used to measure test accuracy. Test accu-racy based on the AUC was judged as follows: uninforma-tive if AUC = 0.5; low accuracy if 0.5 < AUC ≤ 0.7; mod-erate accuracy if 0.7 < AUC ≤ 0.9; very high accuracy if 0.9 < AUC < 1; and perfect if AUC = 1 [26]. The value of index Q*, where sensitivity equals specificity in a ROC curve, was defined as “1” if accuracy was 100% [27]. Inter-study heterogeneity was assessed with Higgins’s I2 homo-geneity test (I2 test) with significance set at 5%, and I2 test values were interpreted as follows: low heterogeneity if I2 ≤ 25%; moderate heterogeneity if 25% <I2 ≤ 75%; and high heterogeneity if I2 > 75% [28].

Results

Selection process and bias risk assessment

A total of 2571 articles were retrieved from the electronic databases. After 250 duplicates were removed, the titles and abstracts of the remaining 2321 articles were exam-ined to determine the appropriateness of each study. If it was difficult to make a decision, the main text was reviewed to make a selection decision. Finally, 2287 (98.5%) articles were excluded, and 33 articles were selected for review. The process and outcome of the literature selection are pre-sented in detail in Fig. 1.

Quality assessment results of the final 33 articles [29–61] were as follows: 31/33 articles (93.9%) had a low risk of bias in all domains and items. All studies had a low risk of bias for patient selection, and 15 used continuous or random sampling [30, 32, 34, 36–38, 44, 47, 52, 55–60]. For index tests, two studies [31, 40] could not be assessed for risk of bias with certainty. However, because the current review aimed to assess fall risk with a fall risk assessment tool in advance and to compare the fall incidents occurring later, we decided that results interpretation would not be affected, even though the administrations of the index test and the reference standard were not blinded (Fig. 2).

Characteristics of selected studies

Thirty-three studies tested the predictive validity of the fall risk assessment tools in people aged ≥60 for a total of 9743 subjects. Seven studies (4067 subjects) [29–35] were conducted in the acute care setting and most patients were admitted to a geriatric or a rehabilitation ward, and 3 studies (364 subjects) [36–38] were conducted in long-term care settings, such as residential care or nursing homes. Twenty-three studies (5312 subjects) [39–61] were conducted in the

4 Aging Clin Exp Res (2018) 30:1–16

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community of elderly persons living independently in their homes or in a retirement community. Regarding the mean age of study subjects, a mean age in the 70  s was most common (23 studies) [29, 31–34, 40, 41, 43, 45, 47–59,

61], followed by a mean age in the 80 s (7 studies) [35–39, 44, 60] and a mean age in the 60 s (3 studies) [30, 42, 46]. In all studies except for one [35], a fall was defined in terms of fallers. In 3 studies [47, 49, 58], a criterion of ≥2 falls

Fig. 1 Flow diagram of article selection

Fig. 2 Quality assessment results of the selected studies by Quadas-2

5Aging Clin Exp Res (2018) 30:1–16

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in a person was used to define falls, while, in most studies, the criterion was ≥1 falls. Follow-up durations varied, until hospital discharge in most studies with hospitalized elderly persons, 6 months in studies using long-term care settings, and 2–18 months in studies conducted in community set-tings. Typical follow-up durations were 6 months (6 stud-ies) [41, 42, 50, 53, 57, 61] and 12 months (14 studies) [40, 43–49, 51, 52, 54, 56, 59, 60]. Selected studies were con-ducted in 15 countries, including the US and Canada, and there was active research worldwide without a bias toward any particular country (Table 2).

Fall risk assessment tools used in each setting

Twenty-six assessment tools for fall risk were used in the selected articles, and they tended to vary based on the set-ting (Table 3). Three fall risk assessment tools were used in the acute care setting: Hendrich II Fall Risk Model, STRATIFY, and Timed Up and Go (TUG) test. Of those, the Hendrich II Fall Risk Model and STRATIFY were used only for elderly hospitalized patients, whereas TUG test was also used for community-dwelling elderly. The number of studies conducted in the long-term care setting was too small to analyze a trend, but two studies used the Mobil-ity Interaction Fall (MIF) chart [36, 38] and one [37] used the Downton Fall Risk Index. Studies conducted with the community-dwelling elderly used a variety of 23 fall risk assessment tools. Assessment tools used in two or more studies were BBS, TUG test, Downton Fall Risk Index, and Tinetti Balance scale, while the remaining 20 assessment tools were each used in a single study.

Predictive validity of fall risk assessment tools in the elderly

The predictive validities of each assessment tool for fall risk are presented in Table 4 and Fig. 3.

BBS was used in 5 studies [39, 46, 48, 56, 61] of com-munity-dwelling elderly for a total of 570 subjects. The pooled sensitivity was 0.73 (95% confidence interval [CI], 0.65–0.79), and heterogeneity among studies was high (82.7%; χ2 = 23.09, P = .0001). The pooled specificity was 0.90 (95% CI 0.86–0.93), and heterogeneity among articles was low (31.9%; χ2 = 5.87, P = .21). The sROC AUC was 0.97 (standard error [SE] = 0.02).

The Downton Fall Risk Index was used in two studies [37, 44] and a total of 231 elderly persons. The pooled sen-sitivity was 0.84 (95% CI 0.76–0.91), and pooled specific-ity was 0.26 (95% CI 0.20–0.33). Heterogeneity between the articles was moderate: 47.5% (χ2 = 3.81, P = .15) and 43.7% (χ2 = 3.55, P = .17) for sensitivity and specificity, respectively.

The Hendrich II Fall Risk Model was used only in stud-ies of hospitalized elderly persons, for a total of 3 articles [30–32] and 1,754 subjects. The pooled sensitivity was 0.76 (95% CI 0.68–0.83) and heterogeneity among the arti-cles was 0.0% (χ2 = 1.10, P = .58), while the pooled speci-ficity was 0.60 (95% CI 0.57–0.62). Heterogeneity among the articles was high, 97.7% (χ2 = 87.03, P < .001). The sROC AUC was 0.75 (SE = 0.05).

The MIF chart was used in two studies (286 elderly persons) [36, 38]. Pooled sensitivity was 0.53 (95% CI 0.44–0.61) and heterogeneity between the articles was high, 94.8% (χ2 = 19.36, P < .001). Pooled specificity was 0.73 (95% CI 0.65–0.80) and heterogeneity between arti-cles was moderate, 64.8% (χ2 = 2.84, P = .09).

STRATIFY was used in 3 studies (2245 elderly persons admitted to an acute care hospital) [33–35]. The pooled sensitivity was 0.89 (95% CI 0.85–0.93), and heterogeneity among the articles was 64.0% (χ2 = 5.56, P = .06). Pooled specificity was 0.67 (95% CI 0.65–0.69), and heterogeneity among the articles was high, 96.5% (χ2 = 57.13, P < .001). The sROC AUC was 0.81 (SE = 0.03).

TUG test was used in 5 studies (427 elderly) [29, 42, 44, 50, 55]. Pooled sensitivity was 0.76 (95% CI 0.68–0.83), and article heterogeneity was 0.0% (χ2 = 2.20, P = .85). Pooled specificity was 0.49 (95% CI 0.43–0.54) and het-erogeneity among the articles was high, 94.8% (χ2 = 95.87, P < .001). The sROC AUC was 0.80 (SE = 0.04).

Finally, the Tinetti Balance scale was used in two stud-ies (284 elderly) [59, 60]. The pooled sensitivity was 0.68 (95% CI 0.56–0.79) and heterogeneity between the articles was 0.0% (χ2 = 0.32, P = .57); the pooled specificity was 0.56 (95% CI 0.49–0.62) and heterogeneity between the articles was high, 79.2% (χ2 = 4.80, P = .03).

Data from 20 fall risk assessment tools used in 888 community-dwelling elderly were combined and meta-ana-lyzed. The pooled sensitivity was 0.63 (95% CI 0.60–0.65) and the pooled specificity was 0.60 (95% CI 0.59–0.62). Heterogeneity among articles was high in both measures, 88.4% (χ2 = 164.29, P < .001) and 98.9% (χ2 = 1751.76, P < .001), respectively. The sROC AUC was 0.76 (SE = 0.05).

Discussion

Although falls can occur at any age, the frequency of falls increases with age [62]. Falls occur in the elderly when physical, perceptual, and cognitive changes occurring with aging are combined with an environment unsuited to elderly safety [5]. To do so, above all, appropriate tools for assessing fall risk in the elderly should be identified and their predictive validity investigated [13]. Hence, we con-ducted this study to address the aforementioned needs by

6 Aging Clin Exp Res (2018) 30:1–16

1 3

Tabl

e 2

Cha

ract

erist

ics o

f sel

ecte

d stu

dies

Ref.

no.

Year

of

publ

ica-

tion

Aut

hors

Loca

tion

Sub

ject

sA

ge in

ye

ars

(M ±

SD

)

Tota

l (n)

Fem

ale

(%)

Tool

Cut

off

Fall

crite

riaFo

llow

-up

(m

os)

2 × 2

Tabl

eVa

lue

(95%

con

fiden

ce in

terv

al)

TPFP

FNTN

SNSP

DO

R

Acu

te c

are

 29

2015

Mar

tinez

et

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Bra

zil

≥60

70.4

± 7.

768

41.2

Tim

ed

Up

and

Go

test

≥10

.85s

ec1

UD

145

742

0.67

(0

.43–

0.85

)

0.89

(0

.77–

0.96

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16.8

0 (4

.59–

61.4

8) 3

020

15Zh

ang

et a

l.C

hina

≥60

66.0

± 10

.598

949

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endr

ich

IIU

R1

UD

2329

79

660

0.72

(0

.53–

0.86

)

0.69

(0

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0.72

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5.68

(2

.60–

12.4

2) 3

120

13C

alde

-vi

lla

et a

l.

Portu

gal

≥65

78.0

586

53.9

Hen

dric

h II

UR

≥1

UD

7925

725

225

0.76

(0

.67–

0.84

)

0.47

(0

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0.51

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2.77

(1

.70–

4.49

) 3

220

11Iv

ziku

et

 al.

Italy

≥65

79.5

± 9.

517

958

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ich

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5≥

1U

D12

942

710.

86

(0.5

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98)

0.43

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4.53

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20.8

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320

08W

ebste

r et

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Aus

tralia

≥65

77.7

± 7.

978

867

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T-IF

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276

1344

60.

82

(0.7

1–0.

90)

0.62

(0

.58–

0.65

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7.33

(3

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13.6

2) 3

420

04Pa

paio

-an

nou

et a

l.

Can

ada

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78.0

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762

054

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T-IF

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234

335

20.

91

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98)

0.60

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15.5

4 (4

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51.4

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519

97O

liver

et

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UK

≥65

8083

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STR

AT-

IFY

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UR

139

155

1153

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93

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96)

0.77

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0.81

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43.3

7 (2

2.88

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Long

-term

car

e 3

620

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din-

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son

Swed

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6583

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6.8

208

72.6

MIF

ch

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645

3260

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43

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53)

0.69

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0.78

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1.69

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2.98

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720

03Ro

sen-

dahl

et

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Swed

en≥

6581

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7871

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k In

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644

184

120.

92

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96)

0.40

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7.33

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2000

Ludi

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8278

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628

85

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85

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95)

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unity

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8)

7Aging Clin Exp Res (2018) 30:1–16

1 3

Tabl

e 2

(con

tinue

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Ref.

no.

Year

of

publ

ica-

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Aut

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62

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105

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9.46

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2013

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6579

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0.53

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220

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6661

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0.66

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5.40

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320

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139

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0)

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0.99

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Dow

nton

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ll R

isk

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1223

576

180.

79

(0.6

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92)

0.24

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0.35

)

1.21

(0

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3.44

)

 45

2011

Panz

er

et a

l.U

SA≥

6575

.1 ±

6.5

74–

Entry

Fa

lls-

Stat

us

Crit

e-rio

n

UR

≥1

1238

79

200.

81

(0.6

7–0.

91)

0.74

(0

.54–

0.89

)

12.0

6 (3

.91–

37.2

1)

 46

2010

Sant

os

et a

l.B

razi

l≥

6066

.0 ±

9.0

187

78.1

Ber

g B

al-

ance

sc

ale

≥49

≥1

1234

53

540.

92

(0.7

8–0.

98)

0.92

(0

.81–

0.97

)

122.

40

(27.

47–

545.

44)

8 Aging Clin Exp Res (2018) 30:1–16

1 3

Tabl

e 2

(con

tinue

d)

Ref.

no.

Year

of

publ

ica-

tion

Aut

hors

Loca

tion

Sub

ject

sA

ge in

ye

ars

(M ±

SD

)

Tota

l (n)

Fem

ale

(%)

Tool

Cut

off

Fall

crite

riaFo

llow

-up

(m

os)

2 × 2

Tabl

eVa

lue

(95%

con

fiden

ce in

terv

al)

TPFP

FNTN

SNSP

DO

R

 47

2010

Peet

ers

et a

l.N

ethe

r-la

nds

≥65

77.9

± 7.

140

873

.3LA

SA

fall

risk

profi

le

≥8

≥2

1243

9533

237

0.57

(0

.45–

0.68

)

0.71

(0

.66–

0.76

)

3.25

(1

.95–

5.43

) 4

820

10M

uir

et a

l.C

anad

a≥

6579

.7 ±

5.3

117

30.8

Ber

g B

al-

ance

sc

ale

≥50

≥1

1226

1226

530.

50

(0.3

6–0.

64)

0.82

(0

.70–

0.90

)

4.42

(1

.93–

10.1

2) 4

920

10Ti

ede-

man

n et

 al.

Aus

tralia

≥63

75.3

± 5.

876

478

.7Pe

rfor-

man

ce-

base

d FR

AT

≥1

≥2

1217

913

913

531

10.

57

(0.5

1–0.

63)

0.69

(0

.65–

0.73

)

2.97

(2

.20–

4.00

)

 50

2010

Wris

ley

and

Kum

ar

USA

≥60

72.9

± 7.

835

51.4

Tim

ed

Up

and

Go

test

≥11

.0 s

≥1

65

41

250.

83

(0.3

6–1.

00)

0.86

(0

.68–

0.96

)

31.2

5 (2

.86–

341.

88)

Dyn

amic

G

ait

Inde

x

≤19

≥1

66

70

221.

00

(0.5

4–1.

00)

0.76

(0

.56–

0.90

)

39.0

0 (1

.96–

777.

72)

Func

-tio

nal

Gai

t A

sses

s-m

ent

≤20

≥1

66

50

241.

00

(0.5

4–1.

00)

0.83

(0

.64–

0.94

)

57.9

1 (2

.82–

1188

.36)

 51

2008

Her

nan-

dez

and

Rose

Can

ada

≥65

77.0

± 6.

519

2–

FAB

sc

ale

≥25

≥1

1244

6315

690.

75

(0.6

2–0.

85)

0.52

(0

.43–

0.61

)

3.21

(1

.63–

6.33

) 5

220

08Ru

ssel

l et

 al.

Aus

tralia

≥60

75.9

± 8.

534

469

.2FR

OP-

Com

to

ol

≥20

≥1

1210

864

5611

60.

66

(0.5

8–0.

73)

0.64

(0

.57–

0.71

)

3.50

(2

.24–

5.45

) 5

320

08Th

iam

-w

ong

et a

l.

Thai

land

≥60

71.0

± 7.

714

456

9Th

ai-

FRA

T≥

4≥

16

3318

390

0.92

(0

.78–

0.98

)

0.83

(0

.75–

0.90

)

55.0

0 (1

5.21

–19

8.94

) 5

420

07La

esso

e et

 al.

Den

mar

k≥

7073

.7 ±

2.9

9474

.4Te

st B

at-

tery

UR

≥1

127

467

340.

50

(0.2

3–0.

77)

0.43

(0

.32–

0.54

)

0.74

(0

.24–

2.31

) 5

520

05W

hitn

ey

et a

l.U

K≥

6379

.3 ±

7.2

110

75.5

Tim

ed

Up

and

Go

test

≥15

 s≥

12

3938

924

0.81

(0

.67–

0.91

)

0.39

(0

.27–

0.52

)

2.74

(1

.13–

6.64

) 5

620

04La

joie

an

d G

al-

lagh

er

Can

ada

≥65

75.5

± 3.

112

564

.0B

erg

Bal

-an

ce

scal

e

≥46

≥1

1237

68

740.

82

(0.6

8–0.

92)

0.93

(0

.84–

0.97

)

57.0

4 (1

8.43

–17

6.2)

9Aging Clin Exp Res (2018) 30:1–16

1 3

Tabl

e 2

(con

tinue

d)

Ref.

no.

Year

of

publ

ica-

tion

Aut

hors

Loca

tion

Sub

ject

sA

ge in

ye

ars

(M ±

SD

)

Tota

l (n)

Fem

ale

(%)

Tool

Cut

off

Fall

crite

riaFo

llow

-up

(m

os)

2 × 2

Tabl

eVa

lue

(95%

con

fiden

ce in

terv

al)

TPFP

FNTN

SNSP

DO

R

AB

C

scal

e≥

67≥

112

3810

770

0.84

(0

.71–

0.94

)

0.88

(0

.78–

0.94

)

38.0

0 (1

3.38

–10

7.89

) 5

720

04N

andy

et

 al.

UK

≥65

74.4

± 6.

434

355

.1FR

AT

≥2

≥1

651

5135

202

0.59

(0

.48–

0.70

)

0.80

(0

.74–

0.85

)

5.77

(3

.40–

9.79

) 5

820

02St

alen

-ho

ef

et a

l.

Net

her-

land

s≥

7077

.2 ±

4.9

287

59.9

RM

RF

≥0.

3≥

29

2731

1921

00.

59

(0.4

3–0.

73)

0.87

(0

.82–

0.91

)

9.63

(4

.79–

19.3

4) 5

920

02Ve

rghe

se

et a

l.U

SA≥

6579

.6 ±

6.3

5957

.6Ti

netti

B

al-

ance

sc

ale

≤10

≥1

128

145

320.

62

(0.3

2–0.

86)

0.70

(0

.54–

0.82

)

3.66

(1

.02–

13.1

8)

Tim

ed

Gai

t≥

12 s

≥1

1210

223

240.

77

(0.4

6–0.

95)

0.52

(0

.37–

0.67

)

3.64

(0

.88–

14.9

5)W

WT-

sim

ple

≥20

 s≥

112

65

741

0.46

(0

.19–

0.75

)

0.89

(0

.76–

0.96

)

7.03

(1.6

8–29

.43)

WW

T-co

m-

plex

≥26

 s≥

112

77

639

0.54

(0

.25–

0.81

)

0.85

(0

.71–

0.94

)

6.50

(1

.68–

25.2

0) 6

020

00R

aȋch

e et

 al.

Can

ada

≥75

80.0

± 4.

422

5–

Tine

tti

Bal

-an

ce

scal

e

≤36

≥1

1236

8316

890.

70

(0.5

6–0.

82)

0.52

(0

.44–

0.59

)

2.48

(1

.28–

4.79

)

 61

1996

Thor

bahn

an

d N

ewto

n

USA

≥65

79.2

± 6.

265

75.8

Ber

g B

al-

ance

sc

ale

≥45

≥ 1

613

34

450.

76

(0.5

0–0.

93)

0.94

(0

.83–

0.99

)

48.7

5 (9

.66–

246.

14)

mos

mon

ths,

TP tr

ue p

ositi

ve, F

P fa

lse

posi

tive,

FN

fals

e ne

gativ

e, T

N tr

ue n

egat

ive,

SN

sen

sitiv

ity, S

P sp

ecifi

city

, DO

R di

agno

sis

odds

ratio

, UD

unt

il di

scha

rge,

UR

unre

porte

d, H

endr

ich

II

Hen

dric

h II

Fal

l Ris

k M

odel

, STR

ATIF

Y St

. Tho

mas

’s R

isk

Ass

essm

ent T

ool i

n Fa

lling

Eld

erly

Inpa

tient

s, M

IF c

hart

mob

ility

Inte

ract

ion

Fall

char

t, m

JH-F

RAT

mod

ified

Joh

ns H

opki

ns-fa

ll ris

k as

sess

men

t too

l, TM

IG-F

RAT

Toky

o M

etro

polit

an In

stitu

te o

f ger

onto

logy

fall

risk

asse

ssm

ent c

hart,

DFR

A ch

art D

emur

a’s

Fall

Ris

k A

sses

smen

t cha

rt, L

ASA

fall

risk

pro

file

long

itudi

nal

Agi

ng S

tudy

Am

sterd

am fa

ll ris

k pr

ofile

, Per

form

ance

-bas

ed F

RAT

perfo

rman

ce-b

ased

Fal

l Ris

k A

sses

smen

t Too

l, FR

AT fa

ll R

isk

Ass

essm

ent T

ool,

FAB

scal

e Fu

llerto

n A

dvan

ced

Bal

ance

sc

ale,

FRO

P-C

om to

ol F

all R

isk

for O

lder

Peo

ple

in th

e C

omm

unity

ass

essm

ent t

ool,

Thai

-RAT

Tha

i Fal

ls R

isk

Ass

essm

ent T

est,

ABC

scal

e ac

tiviti

es-s

peci

fic B

alan

ce C

onfid

ence

scal

e, R

MRF

ris

k M

odel

for R

ecur

rent

Fal

ls, W

WT-

sim

ple

wal

king

Whi

le T

alki

ng T

asks

-Sim

ple,

WW

T-co

mpl

ex W

alki

ng W

hile

Tal

king

Tas

ks-C

ompl

ex

10 Aging Clin Exp Res (2018) 30:1–16

1 3

performing a systematic review and meta-analysis of 33 studies.

The results of the systematic review provided scientific evidence for validated fall risk assessment tools to predict the occurrence of falls in the elderly. First, we found that different tools for assessing fall risk in the elderly were used depending on the setting. The Hendrich II Fall Risk Model and STRATIFY were used for the elderly admitted to the hospital, whereas the BBS and TUG test were gen-erally used for the community-dwelling elderly, and quite diverse (as many as 24) fall risk assessment tools were also used. Regarding the long-term care setting, only 3 studies [36–38] were included in the current review, and thus, it was difficult to elucidate the characteristics of the fall risk assessment tools used in the setting. How-ever, upon reviewing the studies [63–65] conducted in long-term care settings that were excluded from the cur-rent review, because they were retrospective case–control studies or included subjects under age 60, it was found that a mixture of fall risk assessment tools used in the

elderly admitted to the hospital or living in a community were used in this setting, as well.

The fall risk assessment tools divided into those for the elderly admitted to the hospital and those for the community-dwelling elderly showed differences in the items and methods assessing fall risk. In the Hendrich II Fall Risk Model and STRATIFY, fall risk is assessed by medical staff (e.g., nurses) making decisions on fall risk [30–35], involving items such as conscious state, urinary function, and drug-taking. On the other hand, fall risk is assessed in the BBS and TUG test by directly measuring balancing ability in everyday activities such as walking up and down stairs and mobility as well as the speed [29, 39, 42, 44].

Therefore, the clinical context for utilizing an assess-ment tool according to a specific purpose should be consid-ered when determining which fall risk assessment tool to use. Accordingly, if fall risk is suspected to be high, instead of using a single assessment tool, using several assessment tools that have distinctive characteristics to make multiple

Table 3 Fall-risk assessment tools by type and number of validation studies in each setting

FROP-Com tool Fall Risk for Older People in the Community assessment tool, LASA fall risk profile Lon-gitudinal Aging Study Amsterdam fall risk profile, STRATIFY St. Thomas’s Risk Assessment Tool in Fall-ing Elderly Inpatients, TIMG fall risk assessment chart Tokyo Metropolitan Institute of gerontology fall risk assessment chart

Tools Acute care Long-term care Community

Activities-specific Balance Confidence scale 56Berg Balance scale 39,46,48,56,61Demura’s Fall Risk Assessment chart 43Downton Fall Risk Index 37 44Dynamic Gait Index 50Entry Fall Status Criterion 45Falls Risk Assessment Tool 57FROP-Com tool 52Fullerton Advanced Balance Scale 51Functional Gait Assessment 50Hendrich II Fall Risk Model 30–32LASA fall risk profile 47Mobility Interaction Fall chart 36,38Modified Johns Hopkins-fall risk assessment tool 41Performance-based fall risk assessment tool 49Posturographic Fall Risk Index 40Risk Model for Recurrent Falls 58STRATIFY 33–35Test battery 54Thai Falls Risk Assessment Test 53Timed Gait 59Timed Up and Go test 29 42,44,50,55TIMG fall risk assessment chart 43Tinetti Balance scale 59,60Walking While Talking Tasks (Simple, Complex) 59Zur Balance scale 39

11Aging Clin Exp Res (2018) 30:1–16

1 3

Tabl

e 4

Sum

mar

y re

sults

of m

eta-

anal

ysis

ROC

cur

ve re

ceiv

er-o

pera

ting

char

acte

ristic

cur

ve, A

UC

are

a un

der t

he c

urve

, SE

stan

dard

err

or, S

TRAT

IFY

St. T

hom

as’s

Ris

k A

sses

smen

t Too

l in

Falli

ng E

lder

ly In

patie

nts

Tool

sSt

udie

s (k)

Subj

ects

(n)

Pool

ed d

iagn

ostic

test

accu

racy

(95%

con

fiden

ce in

terv

al)

Sum

mar

y RO

C c

urve

Sens

itivi

tySp

ecifi

city

Posi

tive-

likel

ihoo

d ra

tio

Neg

ativ

e-lik

elih

ood

ratio

Dia

gnos

tic

odds

ratio

AU

C (S

E)Q

* (S

E)

Valu

esI2 (%

)χ2

PVa

lues

I2 (%)

χ2P

Ber

g B

al-

ance

scal

e5

570

0.73

(0.6

5–0.

79)

82.7

23.0

90.

0001

0.90

(0.8

6–0.

93)

31.9

5.87

0.21

7.35

(3.7

9–14

.24)

0.26

(0.1

2–0.

58)

29.7

3 (7

.81-

113.

17)

0.97

(0.0

2)0.

92 (0

.04)

Dow

nton

Fa

ll R

isk

Inde

x

223

10.

84 (0

.76–

0.91

)47

.53.

810.

150.

26 (0

.20–

0.33

)43

.73.

550.

171.

14 (0

.90–

1.45

)0.

60 (0

.25–

1.47

)1.

95 (0

.61–

6.20

)

Hen

dric

h II

Fa

ll R

isk

Mod

el

31,

754

0.76

(0.6

8–0.

83)

0.0

1.10

0.58

0.60

(0.5

7–0.

62)

97.7

87.0

3<

0.00

11.

69 (1

.26–

2.27

)0.

47 (0

.35–

0.63

)3.

61 (2

.22–

5.87

)0.

75 (0

.05)

0.70

(0.0

4)

Mob

ility

In

tera

ctio

n ch

art

228

60.

53 (0

.44–

0.61

)94

.819

.36

<0.

001

0.73

(0.6

5–0.

80)

64.8

2.84

0.09

2.50

(0.7

4–8.

41)

0.41

(0.0

8–2.

08)

6.26

(0.4

3–91

.38)

STR

ATI

FY3

2,24

50.

89 (0

.85–

0.93

)64

.05.

560.

060.

67 (0

.65–

0.69

)96

.557

.13

<0.

001

227

(1.7

7–4.

17)

0.16

(0.0

7–0.

37)

17.1

1 (4

.97–

58.9

3)0.

81 (0

.30)

0.75

(0.2

7)

Tim

ed U

p an

d G

o te

st

442

70.

76 (0

.68–

0.83

)0.

02.

020.

850.

49 (0

.43–

0.54

)94

.895

.87

<0.

001

1.87

(1.1

9–2.

93)

0.50

(0.3

3–0.

75)

3.99

(1.5

1–10

.51)

0.80

(0.0

4)0.

74 (0

.03)

Tine

tti B

al-

ance

scal

e2

284

0.68

(0.5

6–0.

79)

0.0

0.32

0.57

0.56

(0.4

9–0.

62)

79.2

4.80

0.03

1.51

(1.2

1–1.

89)

0.58

(0.4

0–0.

83)

2.69

(1.5

0–4.

83)

Oth

ers

1688

80.

63 (0

.60–

0.66

)88

.416

4.29

<0.

001

0.60

(0.5

9–0.

62)

98.9

1751

.76

<0.

001

2.57

(1.7

7–3.

73)

0.48

(0.3

0–0.

76)

5.77

(2.3

6–14

.09)

0.76

(0.0

4)0.

70 (0

.04)

12 Aging Clin Exp Res (2018) 30:1–16

1 3

Fig. 3 Predictive validity of fall risk assessment tools

13Aging Clin Exp Res (2018) 30:1–16

1 3

assessments of fall risk would be necessary to increase the overall predictive accuracy.

The goal of using a fall risk assessment tool is to dis-criminate high and low risks of fall rather than reducing fall risk, and therefore, an assessment tool with high sen-sitivity and high specificity should be chosen [66]. In addi-tion, inter-study heterogeneity should be low, so that the interpretation of a test score may be stable. Overall, all fall risk assessment tools used for the elderly, except for the MIF chart and Tinetti Balance scale, showed a sensitivity of ≥0.7 and low or no inter-study heterogeneity. In most assessment tools except for BBS, specificity was lower

than sensitivity (i.e., under 0.6) and inter-study heteroge-neity was high (i.e., over 90%). However, the primary goal of using a fall risk assessment tool is to identify people at a high risk of falling to minimize the occurrence of fall. Accordingly, we conjectured that if a fall risk assessment tool has a high sensitivity, it achieves the primary goal of using the assessment tool, even though the specificity is low.

Upon examining the results for each assessment tool, BBS showed both pooled sensitivity and pooled specific-ity >0.7, but inter-study heterogeneity in sensitivity was high. The Downton Fall Risk Index was a relatively stable

Fig. 3 (continued)

14 Aging Clin Exp Res (2018) 30:1–16

1 3

tool with a moderate level of inter-study heterogeneity in both sensitivity and specificity and had a pooled sensitivity of 0.84. However, the pooled specificity was too low (0.26). The pooled sensitivity and specificity of the Hendrich II Fall Risk Model were acceptable, at 0.7 and 0.6, respec-tively, but inter-study heterogeneity was high, particularly in specificity. The pooled sensitivity of the MIF chart was low (0.53) and inter-study heterogeneity was high. STRAT-IFY had the highest pooled sensitivity (0.89), and a pooled specificity at a level similar to that of Hendrich II Fall Risk Model. In both the TUG test and Tinetti Balance scale, the pooled sensitivity was approximately 0.7 with no inter-study heterogeneity, whereas the pooled specificity was low (0.5) and inter-study heterogeneity was high. Regarding the assessment tools used each in a single study, the sensitiv-ity was >0.7 for the majority of the assessment tools, while it was <0.5 for quite a few assessment tools. Overall, both pooled sensitivity and pooled specificity were <0.6 and inter-study heterogeneity was high in both measures.

Therefore, we concluded that the predictive validity of the fall risk assessment tools currently used for the elderly is not sufficient. Moreover, the evidence shows that the use of a large variety of fall risk assessment tools in the elderly does not predict elderly fallers with sufficient accuracy. Focusing on the primary goal of each selected tool, as men-tioned above, however, we recommend the Downton Fall Risk Index, Hendrich II Fall Risk Model, STRATIFY, and TUG test, which showed a pooled sensitivity >0.7 and low inter-study heterogeneity. However, the pooled specific-ity of the Downton Fall Risk Index and TUG test was very low compared to that for the other assessment tools; thus, it would be undesirable to use either test alone to assess fall risk. Regarding specificity, only BBS had a pooled specific-ity of 0.9, showing that the highest level of predictability and inter-study heterogeneity was low. Thus, BBS is the most useful tool in identifying the elderly with low fall risk.

An important limitation of the present study was that we selected only prospective studies with elderly subjects aged ≥60 or more and thus did not include all fall risk assessment tools used in practice. By limiting the study population to the elderly aged 60 years or more and exclud-ing retrospective studies, however, we were able to make relatively strong conclusions on each fall risk assessment tool based on the meta-analytic results. In addition, the limitation of this study concerns the low number of stud-ies included by setting, especially acute care and long-term care facilities. Although not a limitation which concerns the design of this review, it is the limit of this study that the validity of the fall tool used in each setting was not fully verified.

To summarize the study findings, rather than a single assessment tool, two assessment tools used in combina-tion will maximize the characteristics and predictability of

each test. For instance, in assessing the risk of fall among the community-dwelling elderly, the TUG test, which has a relatively stable sensitivity, and BBS, which has a relatively stable specificity, can be used in combination to increase diagnostic accuracy of the risk of fall. In addition, the most desirable strategy to consider both the disease state and physical balancing ability of the elderly will be to use, in combination, STRATIFY or the Hendrich II Fall Risk Model, which has a high sensitivity, and BBS or MIF chart which has a stable specificity. This approach will increase the predictability of the risk of fall regardless of the setting.

Acknowledgements The author thanks Dr. Jeong-Hae Hwang and Dr. Yun-Kyung Choi for the assistance and advice during data selec-tion, extraction, and the quality assessment of the studies.

Compliance with ethical standards

Conflict of interest No conflict of interest to declare.

Statement of human and animal rights This article does not con-tain any studies with human participants or animals performed by the author.

Informed consent For this type of study, formal consent is not required.

Funding This work was supported by the Soonchunhyang Univer-sity Research Fund (No. 20150692). The funder had no further role in the conduct of the research.

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