Metric Characteristics of the Tests for Dynamic ...

Übersicht 135

Metric Characteristics of the Tests for Dynamic Balance Evaluation Metrische Eigenschaften von Tests für die Beurteilung der dynamischen Gleichgewichtsfähigkeit

N. Sarabon1, 2, 3, T. Zacirkovnik2, J. Rosker2, 3, S. Loefler1

Authors

1

Ludwig Boltzmann Institute of Electrical Stimulation and Physical Rehabilitation, Vienna, Austria Science and Research Center, Institute for Kinesiology Research, University of Primorska, Koper, Slovenia 3 S2P Ltd., Laboratory for Motor Control and Motor Behaviour, Ljubljana, Slovenia

Affiliations

Key words ▶ dynamic balance ● ▶ metric characteristics ● ▶ testing ● ▶ reliability ● ▶ validity ● ▶ sensitivity ● Schlüsselwörter ▶ dynamische Balance ● ▶ metrische Kennzeichen ● ▶ testen ● ▶ Zuverlässigkeit ● ▶ Gültigkeit ● ▶ Sensibilität ●

received accepted

07.10.2012 21.02.2013

Bibliography DOI http://dx.doi.org/ 10.1055/s-0033-1337977 Phys Med Rehab Kuror 2013; 23: 135–146 © Georg Thieme Verlag KG Stuttgart · New York ISSN 0940-6689 Correspondence N. Sarabon, PhD Institute for Kinesiology Research University of Primorska, Science and Research Centre Garibaldijeva 1 SI-6000 Koper Slovenia [email protected]

Abstract

Zusammenfassung

Different dynamic balance tests help to identify specific balance deficits in elderly and various population groups. In addition, dynamic balance tests have proved useful in identifying potential locomotor-system injury risk factors. These tests differ in the quality of their metric characteristics which could influence their applicability to measure balance in different clinical groups. Reliability, validity, sensitivity and objectivity define metric characteristics of an individual test. This paper provides an overview of the available scientific literature regarding measurement characteristics of the most commonly used dynamic balance tests. Reliability (intra-session, inter-session, inter-tester and intra-tester), sensitivity, validity and ability to predict possible balance impairing trauma and disease are presented. Clinical balance tests (balance assessed by analogue scales), functional reach, star excursion tests and out step tests were most frequently studied for their metric characteristics. In general intra-session reliability proved to be slightly higher than intersession reliability. On the contrary there is a lack of reports on inter-tester and intra-tester reliability, especially for star excursion tests and for the tests performed on an unstable surface. Tests differ in the validity; possibly resulting from use of different reference standards. In addition factors such as age, fatigue, learning, and anthropometric characteristics have been shown to affect dynamic balance. Majority of tests were successfully implemented in the process of identifying fall prone individuals. However the reports on potential of dynamic balance tests to help predict other lower extremity injury or sports performance are sparse. In general dynamic balance measuring tests (not all parameters) proved to have sufficient metric characteristics for their practical application. However individual parameters and goals of balance assessment should be considered, when choosing the most appropriate test.

Die Verwendung dynamischer Balance Tests ermöglicht die Identifikation spezieller Gleichgewichtsdefizite bei älteren Personen und verschiedenen Bevölkerungsgruppen. Zusätzlich haben dynamische Balance Tests ihre Fähigkeit potenzielle Verletzungsrisiken des Bewegungsapparates zu erkennen unter Beweis gestellt. Jedoch gibt es Unterschiede in der Qualität der metrischen Eigenschaften und Anwendbarkeit von Gleichgewichtsmessungen in verschiedenen Altersgruppen. Reliabilität, Validität, Sensitivität und Objektivität definieren die metrischen Eigenschaften von individuellen Tests. Diese Arbeit gibt einen Überblick über die vorhandene Literatur bezüglich der Messeigenschaften der meistverwendeten dynamischen Balance Tests. Reliabilität (Intrasession, Test-Retest, Interrater, Intrarater), Sensitivität, Validität und die Fähigkeit mögliche gleichgewichtsbeeinträchtigende Traumata oder Krankheiten prognostizieren zu können werden vorgestellt. Klinische Gleichgewichtstests (bewertet mit analogen Skalen), „Functional Reach“-Tests, Star-Excursion Tests und Schritt auslösende Tests wurden wegen ihrer metrischen Eigenschaften am häufigsten untersucht. Im Allgemeinen zeigt sich eine leicht höhere Intra- als Intersession Reliabilität. Jedoch fehlen Berichte über die Interrater und Intrarater Reliabilität speziell für Star-Excursion Tests und Tests auf labilen Untergründen. Die Validität der Tests unterscheidet sich. Möglicherweise lässt sich dies durch die Verwendung unterschiedlicher Referenzstandards erklären. Zusätzlich hat sich gezeigt, dass Alter, Ermüdung, Lerneffekte und anthropometrische Eigenschaften die dynamische Balance beeinflussen. Die Mehrheit der Tests konnte mögliche Stürze prognostizieren. Allerdings gibt es wenig Berichte über mögliche andere Prognosen (Verletzungen der unteren Extremitäten, sportliche Leistungen). Generell haben Tests für die Beurteilung der dynamischen

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Gleichgewichtsfähigkeit (wenn auch nicht alle Parameter) bewiesen, dass sie ausreichende metrische Eigenschaften besitzen. Es sollten jedoch individuelle Parameter und Ziele der angestrebten Balancemessung bei der Wahl eines geeigneten Tests berücksichtigt werden.

List of Abbreviations

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Ad BBT BEStest CI CV CVME FIM G ICC Kc ME SAS SEM SDD TE

absolute difference Berg balance scale The balance evaluation system confidence interval Coefficient of variation coefficient of variation of method error functional independence measure G-test statistics intraclass correlation coefficient Kandel coefficient of concordance for ordinal data method error stroke activity scale Standard error of measurement smallest detectable difference Typical error

PACS codes

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80 – Interdisciplinary Physic and Related Areas of Science and Technology 87 – Biological and medical Physics 87.10.-e – General theory and mathematical aspects 87.19.rs – movement 87.19.ru – locomotion

Background

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Ability to maintain postural equilibrium is important during locomotion and object manipulation tasks [1]. It is one of the major concerns for the movement controlling system. Secondary movement goals, such as power production (running, jumping and throwing) or movement precision (hand manipulation tasks), can be compromised if postural equilibrium is lost. Moreover, poor balance can affect every-day tasks that are usually taken for granted, like rising from a bed, standing and reaching. Compromised balance can be a result of injury [2], disease [3, 4] or aging [5, 6]. Balance training has been incorporated into different rehabilitation and injury prevention protocols with the intent to positively affect body equilibrium and balance. Increased awareness of the importance of body equilibrium promotes a constant drive in research on mechanical end neurophysiological backgrounds of body equilibrium [7]. Moreover, the understanding of balance training effects and quality of functional balance assessment approaches is constantly improving. Human bodies can be considered mechanically unstable as proposed by the inverted pendulum model [8]. Two thirds of body’s mass are located at the two thirds of body height [9]. Human body, as represented by the inverted pendulum, pivots around the ankle joint. When the centre of mass is positioned vertically above the ankle axis, the body is considered to be in neutral stable position. As soon as the gravity or other perturbations pull

the centre of mass from its neutral position, the body becomes less stable. The further the centre of mass is from the neutral position, the more unstable the body becomes. Muscles of the lower limbs and torso are activated to counteract the oscillation of the body. When the ground projection of the centre of body mass leaves the area of the support surface, balance is lost. Constant oscillations of the centre of mass during postural and locomotion tasks, make balancing a dynamic process. Different strategies are used by the human motor controlling system to maintain balance. Smaller oscillations are usually counteracted by the muscles around the ankles [10]. If body sway overreaches the capacity of the ankle musculature, hips and trunk are employed to compensate for the sway. In the case of more complex postural tasks the combination of the two is used. When loosing balance, other strategies are adopted; step initiation, moving the support surface under the newly positioned centre of mass or lowering the centre of body mass [9]. In daily, occupational or sports routine, all strategies and their combinations are used. Various pathologies and aging can significantly affect equilibrium maintaining abilities. Parkinson disease and stroke, for example, have been shown to increase the sway of the body [11, 12]. Some injuries and pathologies of the locomotor-system, like knee [13], or ankle injury [14], low back pain [15] or scoliosis [2] have been shown to negatively affect postural equilibrium and balance as well. Compromised balance affects independence of patients during daily routine. Moreover, in the elderly, deterioration of balance has been shown to be one of the causes of falls [16]. These have been reported to result in serious healthcare problems such as complicated fractures and activity restriction. Consequently, assessment of balance has become an important consideration in rehabilitation and injury prevention. Different approaches towards balance testing have been introduced, differing in the type of the balance assessed and in the measuring methods used. Some balance measuring methods rely on the subjects or testers personal judgment on equilibrium control, however others use more sophisticated measuring devices. These methods should be able to distinguish between people that are at risk for an injury development, detect effects of training or detraining and point out specific effects of occupational stress. However, measuring methods differ in accuracy, validity and reliability of the results measured. It is important to know specific measurement characteristics of the balance tests, as some tests turn out to be more appropriate than others. The goal of this review is to present measurement characteristics of the dynamic balance tests that are most frequently used in clinical and rehabilitation practice. Based on a literature overview, reliability, validity, sensitivity and predictive power of the balance measuring methods will be presented.

Dynamic and Static Balance Testing

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Different balance assessment approaches have been proposed. Based on the static or dynamic properties of the support surface and stationary or changing posture, balance tests have been commonly classified as static or dynamic. Tests measuring body sway during standing on a stabile support surface during simple stationary postural tasks (i. e., quiet stance) have been termed static balance tests [17]. During these tests, no perturbations from the environment or additional body movements or move-

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136 Übersicht

ment tasks are present. Different stance positions (parallel stance, tandem and single legged stance) or sensory interventions (open and closed eyes) are often used to increase the intensity of the static balance tests. Daily, occupational and sports movements occur in more dynamic conditions. The environment (opponents, sports equipment), body movement itself (task specific movement of other body parts), or unstable support surface is the origin of perturbation to the body equilibrium [17]. Usually the ground projection of the body’s centre of mass comes close to the borders of the support surface. In dynamic conditions, the challenge to the balance controlling centres is to constantly re-establish balance or maintain the constantly perturbed centre of mass inside the borders of the support surface. Balance during dynamically more demanding conditions is considered to be functionally more important, than during less dynamic-static conditions [18]. Due to the diversity of the situations where dynamic balance is required, various tests have been designed and applied to practice. There is no generally accepted classification of dynamic balance tests although these can be sorted by the type of dynamic perturbations. Some tests use unstable support surfaces, replicating conditions, where lover limb injury may develop. Secondly, tests can use different balance tasks, for example measuring balance during body transfer (rising from a chair, turning, reaching for an object etc.). In clinical practice, balance is assessed during different locomotion tasks (walking, turning). Finally, some tests measure the extremes of ones limits of stability. Each test is designed to expose a specific balance property. Some approaches use balance mats or balance boards, which decrease support surface stability [19, 20]. Usually reactive bodily movements are required to compensate for rotational or translational movements resulting from the movement of the support surfaces. The closer the joint is to the support surface, the higher its involvement in the compensation activity. For example, balance of patients suffering from low-back-pain was tested in a seated position, stressing the involvement of pelvis and trunk [21]. These tasks have been used especially in rehabilitation of lower limb joint injuries [22]. The balancing ability is described by various parameters such as time elapsed on various sides of the tilt boards, frequency of changing directions of movements and angular velocities. However simpler balance assessment approaches measure only time elapsed on the balancing board until ground contact with the board or loss of balance occurs. Other approaches assess the ability to walk, rise from a chair and walk, perform various turns, step over obstacles, to initiate a step or move ones limbs to a specific positions [23]. Usually time elapsed is measured for the locomotion tests. Additionally, number, length of steps and covered distance are measured as well [24–26]. These tests are relatively simple to perform in practice and require no sophisticated measuring devices. These tests are usually used in clinics to assess balance in fragile patient populations such as stroke patients, elderly [27]. Locomotion tests can be considered to assess functional aspects of balance rather more specific aspects of balance controlling mechanisms. Other tests like chair rising, step initiation, touching predefined points on the ground (star excursion balance test) are less influenced by other abilities. These tests have also a high strength dependent component. Well-developed strength of the lower limbs and torso can improve results in such balance tasks [28]. For healthy young adults, elderly or athletes, strength doesn’t compromise balance. For these groups the challenge is to apply proper balance controlling strategies. Strength deficits should be

considered only when performing such test in fragile clinical groups. Some tests assess the borders of the support surface which limit the stability during postural tasks. Besides the muscular system, support surface size and shape affect the results. In these tests, subjects are asked to move their centre of mass ground projection as close as possible to the limits of falling. Other methods use anterior or lateral arm reaching. These tests have been considered as functionally relevant, because human activity constantly demands movement of ones hands during a stationary posture. Some authors report on decreased limits of stability in elderly, compared to their younger counterparts [29, 30]. Reaching tests are functionally relevant, applicable and easily to measure. They are especially useful in patient populations, where decreased balance can affect their daily independence. Simple parameters such as centre of pressure sway path or arm reach distance are used as parameters [31, 32]. Sophisticated measurement equipment is not required. Often clinical scales or questionnaires are applied. Different functional tasks are evaluated by the clinicians. Qualitative rating gives a less accurate quantitative data on balance. Additionally sensitivity to balance improvements is decreased. Clinical scales are oriented towards estimating the functional impairments, rather than focusing exclusively on certain balance specifics.

Metric Characteristics of Dynamic Balance Tests

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Balance in patients, elderly and athletic populations is usually assessed on a longer time-scale, using inter-session repeated measures designs. Based on the measurement results, changes in balance due to training or deconditioning are monitored. Additionally, results of an individual can be compared to different population groups (reference values), in order to identify potential deficits or possible advantages. From the injury prevention perspective (preventing falls or lower limb injury), using balance tests, individuals with higher risks can be identified. To enable acquisition of time-efficient and task-relevant results, only tests with sufficient metric characteristic should be administered. Internal metric characteristics define the quality of a specific balance assessment method. Metric characteristics are commonly described by reliability, validity and sensitivity. 4 major types of reliability are important from a clinical perspective; i) inter-tester, ii) intra-tester, iii) inter-session and iv) intra-session reliability. Intra-session and inter-session reliability are 2 types of intra-tester reliability that are most commonly investigated. Intra-session reliability considers the consistency of the repeated measures taken in one session. On the contrary, intersession reliability considers consistency of results taken on 2 or more sessions, separated by a longer time periods. Inter-tester and intra-tester reliability are of primary concern in clinical, rehabilitation and sports practice. Measurement error resulting from the lower reliability must be taken into account when interpreting data. Measurement errors can be as high as actual changes in balance. An important category defining the tests metric characteristics is validity. Usually it is assessed by comparing a balance test with other well established tests. Using clinical balance evaluation systems as a standard, detection of generalized balance ability is validated. Validity of a test to measure more

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detailed characteristics of balance, other tests must be taken as a standard. Sensitivity and specificity enable a test to properly identify individuals with potential risk of injury (predictive power). An important aspect of sensitivity is identification of bias effects, systematically influencing balance measurements. Various factors, such as fatigue, age, gender and effects of motor learning can influence balance measurement results that are not due to balance conditioning. During the protocol preparation, care should be taken to exclude systematic bias effects, such as fatigue. Moreover, effects of aging must be considered in balance testing. This is especially important in longitudinal comparisons of results for an inter-subject or inter-group comparison. In order to exclude skill learning effects adequate familiarization protocols (i. e., introductory trials) should be used before the test starts. To summarise, all these measurement characteristics should be considered before choosing the test for applied clinical use or research. This paper gives a thorough review on the metric characteristics of those dynamic balance tests which are most commonly used in practice and reported in the scientific literature.

Methods

Statistics used for measuring reliability and validity

Results

Pubmed search engine was used to extract articles studying metric characteristics of dynamic balance tests. Search limits included articles published from 1990 to 2011. Review and meta-analysis articles were excluded. Following descriptors were used: excursion balance test, star excursion balance test, berg balance test, berg balance test reliability, berg balance test validity, tinetti balance test, tinetti balance test reliability, balance test reliability, balance test validity, balance test/tests/testing, dynamic balance test, balance test stroke/elderly/ankle. Together 246 articles were found. 2 researchers performed the overview and extracted the texts studying the reliability, validity and sensitivity of dynamic balance tests. Reports on relevant statistics and use of control groups in the sensitivity studies were used as inclusion criteria. 49 articles meet the inclusion criteria. 2 researchers excluded data on metric characteristics from each study. Additionally, the tests analysed, populations studied, presence of bias-effect and power to predict injury were observed.

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The summary of the literature overview is presented in ▶ Table 1. Each table presents a related group of dynamic bal●

ance tests. The healthy or clinical population used to study metric characteristics are reported in the first 2 columns. 4 different types of reliability are reported in the third to sixth columns. And in the last 3 columns validity, bias-effects and sensitivity are presented. Most studies regarding metric characteristics were found for different clinical scale tests (21 studies), star excursion balance test (18 studies), limits of stability (17 studies), functional reach test (14 studies), out step tests (7 studies) and balance tests using movable support surface (2 studies). Most complete reports on inter- intra- session, inter- and intra-tester reliability, validity and specificity were found for functional reach tests and different clinical scale tests. Fewer reports were found for validity and specificity/sensitivity of star excursion balance tests, limits of stability, out step tests and balance tests using movable support surface. Different clinical and healthy groups were used to study metric characteristics. The functional reach test and limits of stability were applied to the widest array of clinical and age groups. Star excursion balance test and tests using unstable support surface were studied using generally younger groups suffering from lower limb injuries. The clinical scales and the step tests were studied mainly using elderly clinical and healthy groups. In general intra-session reliability was higher as inter-session reliability. Limits of stability balance test had lowest inter-session reliability (poor to good). Other tests had moderate to very good reliability. Intra-session reliability was in general good to very good. There are limited reports on validity of star excursion and movable surface balance tests. Other tests have been mainly validated using different clinical scales or gait velocity.

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Balance tests reliability can be considered as a measure of variability of repeated measurements. In practice, the amount of variability states the change in measured results, which cannot be considered as a change in postural equilibrium or balance maintaining ability. Reliability is most commonly represented by the intra-class correlation coefficient (ICC). There are various classifications of ICC parameters. These are more or less comparable between each other, some being stricter than others. Altman’s classification [33] is based on 5 class of ICC (from 0.81 to 1 very good, 0.61 to 0.80 good, 0.41 to 0.60 moderate, 0.21 to 0.40 fair and below 0.2 poor reliability). However, ICC gives only limited insight into the data dispersion of a sample. More practical information on relative error of measurement is given by the standard error of the measurement (SEM) or similar typical error (TE). It expresses the variability of the measured data in relevance to samples mean. Coefficient of variation expresses the relative TE (as a percent of mean). However, no standards are reported for the SEM, TE, CV and other related parameters. These parameters must be interpreted in parallel to mean values and the characteristics of a specific balance describing parameter. For example 10 % error can be high in tests, where a 5 % change is expected. ICC and CV are not concerned with the changes in sample mean, which can give additional insight into the background of the measurement error. If the mean between 2 sessions of the same subjects’ balance measurements differentiate, the difference can be considered as an error of measurement. Systematic bias effects on measurement data are explored using RANOVA and post hoc t-test models. Usually differing populations (age groups, gender, patient populations) or other conditions (number of trials, consecutive sessions) are used for comparisons. These results represent sensitivity of a test. Validity is expressed by correlation coefficients between the test and the “golden standard”. The reference test must be approved by the experts and thoroughly tested.

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Functional reach

Test

elderly after hip fracture [42]

Children with Cerebral palsy (2–14) [36]

Middle age adults (41– 69) [37]

Elderly (> 60) [37, 38, 40, 43]

Children (2–12) [36]

Right side (ICCa = 0.94; Ad = 0.7) [47] Left side (ICCa = 0.91; Ad = 1.0) [47] Backward (ICCa = 0.96; Ad = 0.5) [47]

Right side (ICCs = 0.93; t-test (p) < 0.05; effect size = 0.18) [46] Left side (ICCs = 0.95; t-test (p) < 0.05; effect size = 0.21) [46] Backward (ICCs = 0.93; t-test (p) < 0.05; effect size = 0.14) [46] Crombach alpha = 0.84 – 0.89 [46, 47]

V al i d i ty

Sarabon N et al. Metric Characteristics of the … Phys Med Rehab Kuror 2013; 23: 135–146 Movement path – FB (ICCa = 0.60; Md = 2.5; S est diff = 9.9; CI = –16.9 – 21.8) [32]

Movement path – FB (ICCa = 0.23 – 0.67; ANOVA (p) = 0.27 – 0.51; SEM = 7.0 %LOS; Md = 2.8; Ad = 11.3, S est diff = 8.1; CI = –13.0 – 18.7) [26, 31, 32]

Middle age adults and elderly with hemiparesis (42–75) [26], elderly after surgical treatment of hip fractures (78.2 ± 5.7) [32]

young (20–32) [31]

Activity level, fear of falling, health status, fall history [46]

Age (F = 3.42) [44, 45], anthropometric properties [44]

Age (–0.37 – –0.54) [36, 37], anthropometric properties (> 0.80) [37], trunk rotation angle, shoulder protraction [40]

Effect (r)

BBS (–0.53 – –0.67), gait velocity (–0.53 – –0.67), LR (0.51 – 0.75) [26]

Identifying falls risk (< 25.4 cm) [41]

Predictive power

S p e c i f ic y /s e n s i tiv i ty

BBS (–0.51 – –0.48) [26],

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Average amplitude of the movement path (ICCa = 0.70; Md = 1.8; S est diff = 6.4; CI = –10.8 – 14.4) [32]

ABC (0.41 – 0.59) [47]

TUG (–0.26 – –0.44) [46]

BBS (0.36 – 0.48; 0.53 – 0.78) [46, 47]

LOS M-L (0.33 – 0.56)[36, 38, 44], 3D LR (0.65) [44]

Amplitude parametric COP (0.60 – 0.72) [37, 40], electronic FR (0.69) [37], LOS A-P (0.50 – 0.77[36][35][34]) [36, 38], FIM (0.45 – 0.49) [38], SAS (0.45) [38]

Correlation (r)

young (20–32) [31]

Meter FR (ICCs = 0.54 – 0.88) [36]

Intrarater

Movement path – LR (ICCa = 0.28 – 0.76; ANOVA (p) = 0.37 – 0.93; SEM = 10.0 %LOS, Md = 3; Ad = 12.2; S est diff = 4.7; CI = –6.2 – 12.2) [26, 31, 32]

Backward (ICCa = 0.71; Ad = 1.4) [47]

Left side (ICCa = 0.83; Ad = 1.4) [47]

Right side (ICCa = 0.66; Ad = 1.9) [47]

Forward (ICCa = 0.98; Ad = 0.4) [47]

Forward (ICCs = 0.92; t-test (p) > 0.05; effect size = 0.09) [46]

Forward (ICCa = 0.75; Ad = 1.4[47][46][45][44][43]) [47]

Meter MDRT

Meter MDRT

Meter LR (ICC = 0.90; CI = 0.89 – 0.96; ICCs = 0.90 – 0.97; CI = 0.86 – 0.98; d = 0.57) [38, 43]

Meter FR (ICCa = 0.50 – 0.98; CI = 0.06 – 0.98[36][35][34]) [36, 37]

Inter-rater

Meter MDRT

3D analyse (ICCa = 0.943) [44]

Meter FR (ICCs = 0.94 – 0.9; CI = 0.92 – 0.99; d = 0.60[38][37][36]; ICCa = 0.85– 0.94[39][38][37]) [38, 39]

Intra-session

R e l i ab i li ty

Middle age adults and elderly with hemiparesis (42–75) [51], elderly after surgical treatment of hip fractures (78.2 ± 5.7) [52]

Elderly (74 ± 7.9; 74–92) [46, 47]

Middle age adults with ischemic stroke [38]

middle age adults with Parkinson's disease (44–79) [41], and with ischemic stroke [38],

Middle age adults (40– 59) [45] Elderly (> 60) [38, 43–45]

Meter FR (ICCs = 0.86; CI = 0.56 – 0.96; ICCa = 0.89 – 0.92; CV = 2.5%; CI = 0.77 – 0.94) [37, 42]

Meter LR (ICCs = 0.88 – 0.94; CI = 0.60 – 0.99; ICCa = 0.99) [36, 44]

Electronic FR (ICCa = 0.81; CV = 4.4%) [37]

young adults with spinal cord injuries [39],

Young Adults (20–40) [37]

Amplitude parameter COP (ICCa = 0.52; CV = 7.5%) [37]

Inter-session

Children with Cerebral palsy (2–14) [36]

Patient

P artic i p a n t

Children (2–12) [36]

Healthy

Rhythmic Rhythmic weight shifting weight shifting forward/backw side to side ard

Multi-Directional (MDRT)

Lateral (LR)

Forward (FR)

Table 1 Metric characteristics of dynamic balance tests.

Übersicht 139

Test

Weight shifting to 8 targets

Limit of stability test (LOS)

Star excursion balance test (SEBT)

Healthy

Sarabon N et al. Metric Characteristics of the … Phys Med Rehab Kuror 2013; 23: 135–146

Young recreational athletes (22.3 ± 3.7; 23.2 ± 3.8 ) [56, 57], Young adults (18–35; 21.8 ± 3.74; 25.9 ± 6.7) [49, 57–59] young basketball players [50]

Patient

Movement velocity (MV) (G = 0.69) [54] Directional control (DC) (G = 0.44) [54] End point excursion (EE) (G = 0.69) [54] Maximum excursion (ME) (G = 0.80) [54]

Movement velocity (MV) (G =0.54 – 0.80; SEM = 0.30 – 0.53; p > 0.05) [54, 55] Directional control (DC) (G =0.58 – 0.75; SEM = 0.04 – 0.13; p > 0.05) [54, 55] End point excursion (EE) (G = 0.71 – 0.88; SEM = 4.92 – 7.63; p > 0.05) [54, 55] Maximum excursion (ME) (G =0.74 – 0.91; SEM = 3.53 – 5.61; p > 0.05) [54, 55]

Medial distance (ICCa = 0.86; SDD = 7.40; SEM = 2.67) [49, 56] Lateral distance (ICCa = 0.91; SDD = 7.68; SEM = 2.77) [49, 56] Posterior distance (ICCa = 0.92; SDD = 7.73; SEM = 2.79) [49, 56] Postero-medial distance (ICCa/ICCsum = 0.82 – 0.93; SDD = 8.15; SEM = 2.94 – 3.99; ME = 3.9; CVME = 3.5) [49, 50, 56] Postero-lateral distance (ICCa/ICCsum = 0.87 – 0.92; SDD = 7.11; SEM = 2.62 – 3.48; ME = 4.6; CVME = 4.4) [49, 50, 56]

Antero-lateral distance (ICCa = 0.87; SDD = 7.71; SEM = 2.78 – 3.43) [49, 56]

Antero-medial distance (ICCa = 0.67 – 0.85; SDD = 6.13; SEM = 2.21 – 4.78) [49, 56]

Postero-medial distance ICCsum = 0.82; ME = 2.5; CVME = 2.9) [50] Postero-lateral distance ICCsum = 0.87; ME = 2.9; CVME = 3.4) [50]

Interrater

Movement velocity – endpoint COG excursion (0.41 – 0.42) [45]

Movement velocity – reaction time (–0.56 – 0.57) [45]

BBS (–0.55 – –0.61), gait velocity (–0.55 – –0.61), FB (0.47 – 0.82) , LR (0.47 – 0.70) [26]

Correlation (r)

V al i d i ty

Predictor of lower extremity injury [50], sensitive to the ACL injury [60], chronic ankle instability [61]

Target facet [55], age [45]

Predictive power

S p e c i fi c y /s e n s i ti v i ty Effect (r)

Learning effect (stabilised by trial 4 in all direction) [56], time of day [57], anthropometric properties [57], range of motion [58, 59]

Intrarater

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Young adults with anterior cruciate ligament (ACL) injuries (20–40) [60], chronic ankle instability (19.8 ± 1.4; 20.9 ± 3.2) [61, 62]

Movement path (LoA = 12.1 – 127; p < 0.001; ICCa = 0.41 – 0.81; CI = 0.04 – 0.82) [53]

Movement path (ICCa = 0.52 – 0.85; CI = 0.52 – 0.98; LoA = 87.4 – 173; p > 0.05; Ad = 21.68) [26, 31, 53]

Anterior distance (ICCsum = 0.84; ME = 2; CVME = 2.9) [50]

Movement time (LoA = 0.6 – 3.7; p < 0.001; ICCa = 0.16 – 0.64; CI = 0.04 – 0.82) [53]

Intra-session

R e li ab i l ity

Movement time (ICCa = 0.25 – 0.88; CI = 0.58 – 0.99; LoA = 1.1 – 2.3; p > 0.05; Ad = 0.67) [26, 31, 53]

Inter-session

Anterior distance (ICCa/ICCsum = 0.84 – 89; SDD = 6.87; SEM = 2.48; ME = 3.0; CVME = 3.6) [49, 50, 56]

Middle age adults and elderly with hemiparesis (42–75) [26]

P arti c i p a n t

Meale firefighters (42.2 ± 7.7) [53], elderly with a history of falls [54], elderly without a history of falls (51–84) [55], young (20–32) [31]

Table 1 Continued.

140 Übersicht

Movable surface

Test

Elderly (60–85) [63]

Elderly (> 65) [51, 52]

Elderly with hemiplegia (67 ± 11) [64]

Sarabon N et al. Metric Characteristics of the … Phys Med Rehab Kuror 2013; 23: 135–146 Max length (ICC = 0.95; SEM = 1.75) [52]

Max length (ICC = 0.96; SEM = 1.83 – 1.87) [52]

Max length (ICCs = 0.97) [64]

Intrarater

Correlation (r)

V ali d i ty

Rapid step test (–0.38), Tandem stance time (0.62), unipedal stance time (0.68 – 0.70) [52] [51], tandem walk time (–0.63), TUG (–0.68 – –0.65) [51][52], SMW (0.73), POMA (0.75), ABC (0.66) [51], FR (0.65) [52]

Max–waking speed (0.84), stride length (0.86 – 0.89), one footed standing duration (0.74 – 0.83), cadence max walking speed (0.48) [64]

Interrater

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Connecting time (ICCa = 0.86 – 0.98) [63]

Tilt left, eyes open (ICCmedian = 0.53 – 0.54) [19]

Tilt right, eyes closed (ICCmedian = 0.49 – 0.82) [19]

Tilt left, eyes open (ICCmedian = 0.52 – 0.53) [19]

Tilt right, eyes open (ICCmedian = 0.54 – 0.61) [19]

(ICCa = 0.90; CV = 16.2; SEM = 0.6; RMANOVA (p) > 0.05) [20]

Average frequency of changing the direction of movement (AF)

Average frequency of changing the direction of movement (AF) (ICCa = 0.90; CV = 8.4; SEM = 0.2; RMANOVA (p) > 0.05) [20]

Average angular velocity during active balancing (AAV) (ICCa = 0.96; CV = 6.9; SEM = 2.7; RMANOVA (p) < 0.05 – 0.01) [20]

Average angular velocity during active balancing (AAV ) (ICCa = 0.77; CV = 13.9; SEM = 8.7; RMANOVA (p) > 0.05 – 0.01) [20]

I ntra - s e s s io n Percentage of active time (% AT) (ICCa = 0.92; CV = 6.9; SEM = 4.8; RMANOVA (p) < 0.05 – 0.01) [20]

I nte r- s e s s ion

R e li ab i l i ty

Percentage of active time (% AT ) (ICCa = 0.79; CV = 11.5; SEM = 5.9; RMANOVA (p) > 0.05 – 0.01) [20]

Max length (ICC = 0.90 – 0.91; SEM = 2.63 – 2.63) [52]

Developmentally delayed children (4–7) [19]

P a tie nt

P arti c ip an t

Young (20.6– 23.4) [20], children (4–7) [19]

H e a lthy

Side step test

Maximal step length

Balance board

Table 1 Continued.

Effect (r)

Predictor of falls [51]

Predictive power

S p e c i fi c y /s e n s i ti v i ty

Übersicht 141

Step test

Foot-off time (ICC = 0.88 – 0.91 [65] Foot contact time (ICC = 0.74 – 0.76 [65] Dual task - Step initiation, (ICC = 0.73 – 0.84) [65] Preparation phases (ICC = 0.62 – 0.79) [65] Swing phases (ICC = 0.42 – 0.54) [65] Foot-off time (ICC = 0.85 – 0.93) [65] Foot contact time (ICC = 0.71 – 0.85) [65]

Foot-off time (ICC = 0.79 – 0.82) [65] Foot contact time (ICC = 0.78 – 0.82) [65] Dual task - Step initiation, (ICC = 0.70 – 0.73) [65] Preparation phases (ICC = 0.70 – 0.88) [65] Swing phases (ICC = 0.32 – 0.77) [65] Foot-off time (ICC = 0.68 – 0.83) [65] Foot contact time (ICC = 0.74 – 0.82) [65]

Total values (ICCs = 0.77 – 0.99; CI = 0.57 – 0.99; ANOVA (p) = 0.04 – 0.27; Ad = 1.4 – 4.6; SEM = 1.48 – 1.51) [26, 31, 47, 68, 70, 72, 73]

Total values (ICCs = 0.88 – 0.997; CI = 0.88 – 0.98; Ad = 2.7; p = 0.11) [47, 68, 71, 74, 75]

Time (ICCs = 0.99; p > 0.05) [66]

Single task – Step initiation, (ICC = 0.77 – 0.86) [65] Preparation phases (ICC = 0.83 – 0.88) [65] Swing phases (ICC = 0.41 – 0.47) [65]

Single task – Step initiation, (ICC = 0.70 – 0.79) [65] Preparation phases (ICC = 0.76 – 0.78) [65] Swing phases (ICC = 0.46 – 0.64) [65]

Time (ICCs = 0.93; CI = 0.86 – 0.96) [67]

I ntra - ra te r

I nte r- ra te r

Time (ICCs = 0.99, p > 0.05)[66]

Connecting time (ICCa = 0.67 – 0.97) [63]

I ntra - s e s s io n

R e l i ab i li ty

Gait velocity (0.81), DGI (0.78), TUG (–0.62) DI (–0.74), ABC (0.48), DHI (–0.32) [26, 70]

Step test (–0.83), TUG (0.69 – 0.88), FR (–0.47), gait speed (0.65), DGI (–0.56) [66, 67]

C o rre la tio n (r)

V al i d i ty

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Middle age adults and elderly with hemiparesis (42–75) [26], multiple sclerosis [70, 71], children with moderate motor impairment [72]

I nte r- s e s s io n

Elderly (74–92) [47, 68], elderly dependent in activities of daily living [69], young (20–32) [31],

P a tie nt

Elderly with balance deficits secondary to vestibular disorders (63.7 ± 17.8) [67]

Elderly (60–87) [63, 65], young adults (35.5 ± 15.5) [65]

H e a lthy

P arti c i p an t

Elderly (> 65) [66]

Test

Four square step test

Sarabon N et al. Metric Characteristics of the … Phys Med Rehab Kuror 2013; 23: 135–146

BBS

Voluntary step

Table 1 Continued.

Age, gender [63]

E ffe c t (r)

Predictor risk of falls [70, 74]

Predictor of falls [66]

Predictive power

S p e c i fi c y /s e n s i ti v i ty

142 Übersicht

Übersicht 143 Discussion Final conclusions on the applicability of dynamic balance test are hindered by incomplete Information on their metric characteristics, especially for the validity and ability to predict injury. Inter-session and intra-session reliability have been most commonly studied. Reports on validity and ability to predict injury risks have been less often reported in the scientific literature. Additionally, reports on the effects of age, gender, fatigue and other factors were reported for only a few dynamic balance tests. Most frequently studied metric characteristics were those for the functional reach tests, star excursion balance test, and sets of clinical balance tests. Additionally, parameters for an individual dynamic balance test proved to differ in measurement characteristics. The overview can aid clinicians, physical therapists and coaches to appreciate strengths and weaknesses of individual dynamic balance tests most commonly used in their practice. Metric characteristics of clinical scales and functional reach tests were most frequently studied, enabling a complete analysis of metric characteristics. Reports on metric characteristics of other dynamic tests are not as complete. Frequency and long history of using clinical scales and functional reach test has probably made them the most studied tests. The main focus of clinical practice is to assess functional balance, by using different daily tasks such as walking, standing from a chair and turning during walking. From this perspective the balance during these tests is affected by other abilities such as strength, flexibility and coordination. Tests such as step test, limit of stability, weight transfer, balancing on unstable support surface and star excursion balance test assess more specific sub-categories of balance. These become relevant in studying more specific systems enabling balance and posture control. Future research should focus on metric characteristics of these tests. Balance tests have been frequently applied to elderly and other clinical populations, where functional deficits might affect balance and consequently result in possible injury. Most widely applied was the functional reach test. Metric characteristics were studied in all age groups and on a wide variety of clinical populations. Step test and clinical scales have been studied only using elderly clinical populations. On the contrary star excursion balance test and tests using unstable support surface have been studied primarily using younger clinical populations. It can be concluded, that tests are used for specific clinical and age groups. One can speculate that reliability of tests is dependent on the clinical population and age group. Consequently the sensitivity of a test can be affected. Future research should compare metric characteristics for healthy and patient populations as well as different age groups. Reliability analysis showed higher intra-session then inter-session reliability. This observation was expected. Daily changes in the task focus, physical fitness and motivation might influence more pronounced differences in session to session balance test results. An important aspect could be the learning effect that might significantly improve the test outcomes. This effect was studied only for the star excursion balance test, where it proved to be of importance. Future research should study the effect of learning in individual balance tests. All tests observed had good to very good inter-session reliability. The highest inter-session reliability was observed for clinical scales. The limits of stability had lowest inter-session reliability. There are no reports on sensitivity of the limits of stability that

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Total value (ICCs = 0.88 – 0.96; CI = 0.72 – 0.96) [68, 74] Adults with idiopatic Parkinson’s disease [74] Elderly [68, 79]

method error (ME), coefficient of variation of method error (CVME), coefficient of variation (CV), Kendall’s coefficient of concordance for ordinal data (Kc)

Total value (ICCs = 0.91 – 0.98; CI = 0.89 – 0.99; Kc = 0.79; SEM = 1.26) [68, 74, 78] Sub task value (ICCs = 0.79 – 0.96; CI = 0.63 – 0.99; Kc = 0.91 – 0.95) [79]

functional independence measure (FIM), average ICC (ICCa), confidence interval (CI), absolute difference (Ad), limit of agreement (LoA), standard error of measurement (SEM), G-test statistics (G), smallest detectable difference (SDD),

ABC (0.69 – 0.76) [74, 79], BBS (0.87), FGA (0.88) [74]

TUG (–0.55), FR (0.48), walking speed (0.53 – 0.54), ADL (–0.45) [76, 78] Total value (ICCs = 0.79 – 0.99) [76, 78] Total value (ICCs = 0.82 – 0.99; CI = 0.67 – 0.99) [76, 78] Total value (r = 0.60 – 1) Total value (ICCs = 0.93 – 0.99) [76] Elderly with Parkinson’s disease (65 ± 10.9) [78] Elderly (>65) [76, 77]

H e a lthy

Number of trials for individual studies; 1 [36, 38, 40, 46], 2 [48], 3 [20, 37, 39, 42, 44, 47, 49, 50] 6 [26, 32], 8 [26]. Following abbreviations are used: Berg balance scale (BBS), the balance evaluation system (BES test), stroke activity scale (SAS),

Predictor risk of falls [74]

Predictor risik of falls [76, 78]

Predictive power Effect (r) Correlation (r) P a tie nt

I nte r- s e s s io n

Intrasession

Inter-rater

Intra-rater

V al i d ity R e l i ab il i ty P arti c i p an t Test

Tinetti balance (TB)

Table 1 Continued.

BESTest

S p e c i f ic y /s e n s itiv i ty

▼

might explain the lower inter-session reliability. A potential reason in lower reliability could be in instructions given to the subjects. These should be standardised and understandable. Especially in elderly or children populations the instructions are crucial. Considerably fewer studies are reported on intra-tester and inter-tester reliability. No studies were found for the limits of stability dynamic balance tests. Intra-tester reliability proved to be slightly higher than inter-tester, comparable to within/ between session reliability. Weaker inter-tester reliability suggests that strict measurement protocols accepted by the clinical community should be followed to enable reliable results. This is of special importance as balance of an individual might be assessed by different clinicians in different clinics. By these means higher consensus between testers can be achieved. Validation is reported for the majority of the presented dynamic balance tests. Star excursion balance test and tests using movable surface were the exceptions. Future research must provide these missing data. Sets of clinical tests were usually used as a balance measuring reference method in validation studies. Clinical tests assess balance from a functional perspective. The goal is to assess balance during activities relevant to daily and occupational tasks. Usually different balance tasks, from quiet stance, to stand up and go tests are performed. The down side of these tests is the analogue scaling. Consequently the sensitivity of the data is compromised. Based on these disadvantages, clinical test can be used only as a general validation standard. Newer studies try to focus on more specific deficits in balance controlling mechanisms [7]. The validity of these tests to measure subcomponents of the balance controlling mechanisms must be more precisely studied in the future. Balance tests have mainly been applied to identify potential fallers in elderly and clinical populations. Only star excursion balance test has been applied to identify potential risks of lower limb injury. Other applications of balance testing require future work. It has been suggested that low back pain might be correlated to decreased balance [34], therefore additional applications are warranted. Force platforms can be used to assess body sway during functional reach, star excursion balance test and limits of stability by measuring the movement of the centre of pressure. This approach does not enable measurement of the functional outcome of the task. For example functional reach test and star excursion balance test can be performed by applying hip and ankle balancing strategy, allowing for greater reach distance. The reach distance cannot be measured by centre of pressure movement, because it is influenced by other factors. This prediction is confirmed by sensitivity studies, stressing the influence of anthropometric characteristics on the reaching distance. Additionally age proved to be of importance as well. The decline in strength with age can significantly affect the movement strategy applied by elderly during reaching. Consequently measures of reaching distance should be used in functional reaching and star excursion balance tests. Interestingly the reliability studies analysed in this work confirm higher reliability of distance measures as compared to measures of the centre of pressure path. Studies regarding the influence of strength in elderly populations might be of interest. An important aspect of dynamic stability testing is the direction of specific balance control. Balance deficits in specific directions have been shown to affect the type of fall in elderly and conse-

quently the type of injury [35]. The direction specific testing is enabled by lateral functional reach test, medio-lateral limits of stability, side step test and star excursion balance test. The reliability proved to be comparable to the antero-posterior tests. Influence of specific factors on dynamic balance test results has been seldom studied. In general gender and anthropometrics can influence test results. No data were reported for the tests using unstable support surface and limits of stability. For the tests dependent on the arm or leg length (functional reach and star excursion balance test) anthropometrics can affect results. Normalization to one’s body height, leg or arm length can enable more direct inter-subject comparisons. Additionally star excursion balance test, where higher level of motor skill is demanded, was shown to be affected by learning. Tests where motor skills are important, subjects should perform familiarization trials, to overcome the learning effect. Finally, age was shown to affect dynamic balance results considerably. Results between elderly and their younger counterparts cannot be directly compared. Future research assessing metric characteristics of dynamic balance tests on all age groups will enable inter-age comparisons.

Conclusions

▼

Metric characteristics of clinical balance scales and functional reach tests have been fully studied, but not for other dynamic balance tests. These have been shown to be reliable, valid and sensitive to balance deficiencies (fall prediction). However, clinical dynamic balance tests are less appropriate for younger age groups due to their insensitivity. Most applicable to different population and clinical groups are functional reach tests. All tests have been successful in identifying risk of falling. For the identification of locomotor-system deficits after injury star excursion balance tests can be used, especially for young adults. Additionally tests using unstable support surface are promising, but need additional confirmation before they should be applied to practice. Clinicians can improve metric characteristics of dynamic balance tests by following defined measurement protocols. These should be standardized for all testers. If possible, measuring devices should be used to enable objective results and smaller measurement errors. In anthropometric dependent tests (star excursion balance test, functional reach,) higher intersubject comparisons can be achieved by normalizing results to body height, leg, foot or arm length. As for skill demanding balance tests (star excursion balance test), familiarization trials should be incorporated prior to testing.

Authors’ Contributions

▼

TZ carried out the literature search, acquired the relevant articles and performed the preliminary analysis on the topic. NS, JR and SL performed the detailed study of the papers and wrote the manuscript. All authors read and approved the final manuscript.

Authors’ Information

▼

About author relevant information (qualification. current position…). not obligatory.

Sarabon N et al. Metric Characteristics of the … Phys Med Rehab Kuror 2013; 23: 135–146

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144 Übersicht

Acknowledgements

▼

The authors would like to acknowledge the support of the EU Interreg-IVa programme Österreich-Slowakei Mobilität im Alter, MOBIL, N_00033.

Competing Interests

▼

The authors of this paper have no competing interests.

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