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Sports Medicine

, Volume 45, Issue 12, pp 1671–1692 | Cite as

Associations Between Measures of Balance and Lower-Extremity Muscle Strength/Power in Healthy Individuals Across the Lifespan: A Systematic Review and Meta-Analysis

  • Thomas MuehlbauerEmail author
  • Albert Gollhofer
  • Urs Granacher
Open Access
Systematic Review
Part of the following topical collections:
  1. Exercise to improve mobility in healthy aging

Abstract

Background

It has frequently been reported that balance and lower-extremity muscle strength/power are associated with sports-related and everyday activities. Knowledge about the relationship between balance, strength, and power are important for the identification of at-risk individuals because deficits in these neuromuscular components are associated with an increased risk of sustaining injuries and falls. In addition, this knowledge is of high relevance for the development of specifically tailored health and skill-related exercise programs.

Objectives

The objectives of this systematic literature review and meta-analysis were to characterize and, if possible, quantify associations between variables of balance and lower-extremity muscle strength/power in healthy individuals across the lifespan.

Data Sources

A computerized systematic literature search was performed in the electronic databases PubMed, Web of Science, and SPORTDiscus up to March 2015 to capture all relevant articles.

Study Eligibility Criteria

A systematic approach was used to evaluate the 996 articles identified for initial review. Studies were included only if they investigated healthy individuals aged ≥6 years and tested at least one measure of static steady-state balance (e.g., center of pressure [CoP] displacement during one-legged stance), dynamic steady-state balance (e.g., gait speed), proactive balance (e.g., distance in the functional-reach-test), or reactive balance (e.g., CoP displacement during perturbed one-legged stance), and one measure of maximal strength (e.g., maximum voluntary contraction), explosive force (e.g., rate of force development), or muscle power (e.g., jump height). In total, 37 studies met the inclusionary criteria for review.

Study Appraisal and Synthesis Methods

The included studies were coded for the following criteria: age (i.e., children: 6–12 years, adolescents: 13–18 years, young adults: 19–44 years, middle-aged adults: 45–64 years, old adults: ≥65 years), sex (i.e., female, male), and test modality/outcome (i.e., test for the assessment of balance, strength, and power). Studies with athletes, patients, and/or people with diseases were excluded. Pearson’s correlation coefficients were extracted, transformed (i.e., Fisher’s z-transformed r z value), aggregated (i.e., weighted mean r z value), back-transformed to r values, classified according to their magnitude (i.e., small: r ≤ 0.69, medium: r ≤ 0.89, large: r ≥ 0.90), and, if possible, statistically compared. Heterogeneity between studies was assessed using I 2 and Chi-squared (χ 2) statistics.

Results

Three studies examined associations between balance and lower-extremity muscle strength/power in children, one study in adolescents, nine studies in young adults, three studies in middle-aged adults, and 23 studies in old adults. Overall, small-sized associations were found between variables of balance and lower-extremity muscle strength/power, irrespective of the age group considered. In addition, small-sized but significantly larger correlation coefficients were found between measures of dynamic steady-state balance and maximal strength in children (r = 0.57) compared with young (r = 0.09, z = 3.30, p = 0.001) and old adults (r = 0.35, z = 2.94, p = 0.002) as well as in old compared with young adults (z = 1.95, p = 0.03).

Limitations

Even though the reported results provided further insight into the associations between measures of balance and lower-extremity muscle strength/power, they did not allow us to deduce cause and effect relations. Further, the investigated associations could be biased by other variables such as joint flexibility, muscle mass, and/or auditory/visual acuity.

Conclusions

Our systematic review and meta-analysis showed predominately small-sized correlations between measures of balance and lower-extremity muscle strength/power in children, adolescents, and young, middle-aged, and old adults. This indicates that these neuromuscular components are independent of each other and should therefore be tested and trained complementarily across the lifespan. Significantly larger but still small-sized associations were found between measures of dynamic steady-state balance and maximal strength in children compared with young and old adults as well as in old compared with young adults. These findings imply that age/maturation may have an impact on the association of selected components of balance and lower-extremity muscle strength.

Keywords

Maximal Strength Muscle Power Jump Height Reactive Balance Explosive Force 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Key Points

The present systematic review and meta-analysis characterized and quantified associations between measures of balance and lower-extremity muscle strength/power in healthy individuals across the lifespan (≥6 years).

Irrespective of the investigated age group, our analyses revealed predominately small-sized correlations between measures of balance and lower-extremity muscle strength/power.

The primarily small-sized correlations between proxies of balance and lower-extremity muscle strength/power indicate that these components are independent of each other (i.e., task-specific) and should therefore be tested and trained complementarily across the lifespan.

The observed age-related differences in associations between measures of dynamic steady-state balance and maximal strength imply that maturity and biological aging may have an impact on selected components of balance and strength.

1 Introduction

Balance and muscle strength/power represent important health and skill-related components of physical fitness that have to be sufficiently developed across the lifespan to successfully perform sport and everyday activities without suffering injuries and falls [1]. In contrast, deficits in balance and lower-extremity muscle strength/power have been identified as important intrinsic (person-related) injury and fall risk factors in children [2], adolescents [3], adults [4], and seniors [5]. For example, Wang et al. [3] showed that large postural sway during one-legged stance was associated with a significant increase in the risk of sustaining ankle injuries (odds ratio [OR] = 1.2) in high school basketball players aged 17 years. Furthermore, deficits in lower-extremity muscle strength (i.e., isokinetic knee flexor/extensor strength asymmetry) were identified as a significant risk factor (OR = 3.9) for non-contact quadriceps and hamstrings strains in young soccer players aged 19–28 years [4]. Another meta-analysis revealed that low levels of eccentric inversion strength (relative risk [RR] = −0.34), low level of postural stability (RR = 2.06), and a low level of inversion proprioception (RR = 0.57) represent relevant causes of ankle injuries in athletes [6]. In adults above the age of 60 years, a meta-analysis consisting of 16 prospective and retrospective studies indicated that lower-extremity muscle weakness (OR = 4.9), balance (OR = 3.2), and gait deficits (OR = 3.0) are associated with an increased fall risk [5].

Given that annual medical treatment costs of sports-/fall-related injuries are high [7, 8], knowledge about the relationship between balance and lower-extremity muscle strength/power are important from two perspectives: (a) testing and identifying at-risk individuals; and (b) developing and implementing individually tailored injury- and fall-prevention programs. Large-sized correlations between measures of balance and muscle strength/power of the lower-extremities imply that these neuromuscular components are interlinked and not independent of each other. Thus, performance achieved in one component (e.g., balance) can be (partly) transferred to that of the other component (e.g., lower-extremity muscle strength). In addition, training-induced gains in lower extremity muscle strength (e.g., maximal strength of plantarflexors) may have an impact on balance performance (e.g., postural sway) or vice versa. In contrast, small-sized correlations between balance and lower-extremity muscle strength/power imply that these neuromuscular components are independent of each other and may thus have to be tested and trained complementarily. There are a number of reasons that imply large-sized correlations between balance and muscle strength/power of the lower-extremities. First, it has previously been shown that deficits in balance and strength are significantly associated with the occurrence of injuries and falls [2, 3, 4, 5] and therefore represent two important intrinsic risk factors. Second, similar neurophysiological structures appear to be responsible for the control of balance and lower-extremity muscle strength/power. For instance, information from Ia afferents is important for both the regulation of balance as well as for explosive force production through the mediation of presynaptic inhibition acting on the motor neuron [9, 10]. In addition, cortical excitability is an important mechanism responsible for voluntary muscle activations but also for the control of long latency reflexes during the performance of postural tasks [11, 12]. Third, numerous studies [10, 13, 14] proved a transfer of training-related gains from one component to the other and vice versa. For example, Gruber et al. [10] scrutinized changes in balance and strength performance following 4 weeks of balance or resistance training in healthy young adults. The authors observed significant improvements in rate of force development (RFD) after balance training and in postural sway after ballistic strength training.

In addition, age appears to be an important factor that may have an impact on associations between balance and lower-extremity muscle strength/power. Recently, it has been reported that these neuromuscular components behave in a U-shaped (i.e., postural sway) or inverted U-shaped (i.e., gait speed, maximal strength, muscle power) mode across the lifespan depending on the respective variable that is taken into consideration [15]. These age-related behavioral changes are mirrored in the underlying neurophysiological structures responsible for the control of balance and strength/power [16]. In children, the neuromuscular system is still emerging due to maturation (e.g., central nervous system myelinization) and it has not reached its full functionality yet [17, 18]. In seniors, the neuromuscular system has lost its full functionality due to, for instance, a decline in the number of sensory and motor neurons [19, 20, 21].

Thus, the aims of this systematic literature review and meta-analysis were to characterize and quantify associations between variables of balance and lower-extremity muscle strength/power in healthy individuals across the lifespan. With reference to the relevant literature [2, 3, 4, 5, 12, 13, 15], we expected (i) large-sized associations between balance and strength/power of the lower-extremities; and (ii) that the correlations between those components are modulated by age.

2 Methods

2.1 Literature Search

We performed a computerized systematic literature search in PubMed, Web of Science, and SPORTDiscus up to March 2015. The following Boolean search strategy was applied using the operators AND, OR, NOT: ((((postural balance [MeSH] OR posture [MeSH]) AND (muscle strength [MeSH] OR power) AND (correlation study OR association OR relationship) NOT (athletes [MeSH] OR patients OR disease)))). With respect to the PubMed database, Medical Subject Headings (MeSH) were used, as indicated before. The search was limited to the English language, human species, and full-text original articles. Further, we checked the reference lists of each included article and analyzed relevant review articles [22, 23] in an effort to identify additional suitable studies for inclusion in the database.

2.2 Selection Criteria

To be eligible for inclusion, studies had to meet the following criteria: (a) participants of the experimental groups had to be healthy subjects; (b) participants were aged 6 years and older; and (c) at least one measure of balance and lower-extremity strength/power had to be tested in the study. Studies were excluded if: (a) they investigated athletes, patients, or people with diseases; or (b) it was not possible to extract correlation coefficients from the results section (see, for example, Gomes et al. [24]), or if authors did not reply to our inquiries sent by email. Based on the predefined inclusion and exclusion criteria, two independent reviewers (TM, UG) screened potentially relevant papers by analyzing the titles, abstracts, and full texts of the respective articles to elucidate their eligibility. If no consensus was achieved between the two reviewers, a third reviewer (AG) was contacted.

2.3 Coding of Studies

Each study was coded for the following variables: number of participants, sex, and age. Further, we coded test modalities/outcomes in tests for the assessment of balance, muscle strength, and power. In accordance with the classification of postural control introduced by Shumway-Cook and Woollacott [25], balance performance was separated into four different categories: static steady-state (i.e., maintaining a steady position while sitting or standing), dynamic steady-state (i.e., maintaining a steady position while walking), proactive (i.e., anticipation of a predicted postural disturbance), and reactive balance (i.e., compensation of a postural disturbance). Lower-extremity muscle strength was divided in measures of maximal strength (i.e., maximal voluntary force/torque of the force-/torque-time curve [MVC]), explosive force (i.e., rate of force/torque development [RFD/RTD] as indicated by the slope of the force/torque-time curve), and power (i.e., jump distance, force, height, and power). If multiple measures were reported within one of the aforementioned categories, the most representative measure was used for analysis. With regards to static steady-state balance, center of pressure (CoP) displacement during one-legged stance was defined as the most important parameter. In terms of dynamic steady-state balance, gait speed was used. Concerning proactive and reactive balance, maximal reach distance in the functional-reach-test and CoP displacements during perturbed one-legged stance were defined as the most representative outcome. In terms of muscle strength, MVC was defined as the most important variable representing maximal strength. With regards to explosive force, RFD was used, and for muscle power, countermovement jump (CMJ) height was applied. If test parameters/outcomes and/or results of correlative analyses were not reported, the authors were contacted and missing information was requested. In two cases [26, 27], authors responded and provided the respective data. If authors did not respond, the study was excluded [28].

2.4 Statistical Analyses

Associations between variables of balance and lower-extremity muscle strength/power were assessed in healthy individuals using the Pearson product-moment correlation coefficient (r value). To pool r values derived from different studies, “Fisher’s z’ transformation” was used, i.e., Pearson product-moment correlation coefficients were converted to the normally distributed variable z’ (i.e., z-transformed r z value). The formula for the transformation is (Eq. 1):
$$ z^{\prime} = 0. 5\left[ {{ \ln }\left( { 1 + {{r}}} \right) {-} { \ln }\left( { 1 {-} {{r}}} \right)} \right] $$
(1)
where ln is the natural logarithm [29]. In addition, the included studies were weighted according to the magnitude of the respective standard error (SE). The formula for the calculation of the SE is (Eq. 2):
$$ {\text{SE }} = { 1}/\sqrt{ \left( {N - 3} \right)} $$
(2)
where N stands for the respective sample size [29]. Afterwards, weighted mean r z values were computed. To classify and interpret the correlation sizes, r z values were back-transformed to r values. Based on the recommendations of Vincent [30], values of 0.00 ≤ r ≤ 0.69 indicate small, 0.70 ≤ r ≤ 0.89 medium, and r ≥ 0.90 large sizes of correlation. Finally, a statistical analysis was conducted to calculate differences between the mean r values by age groups (children vs. young adults vs. old adults) [29, 31]. The corresponding formula is (Eq. 3):
$$ z = (z1 - z2)/\sqrt{(1/(n1 - 3) + 1/(n2 - 3))}. $$
(3)

3 Results

3.1 Study Characteristics

Figure 1 displays a flow chart that illustrates the different stages of the systematic search and the selection of articles over the course of the literature search. Our initial search identified 996 studies that were potentially eligible for inclusion in this systematic review. After removal of duplicates and exclusion of ineligible articles, 36 studies remained. We identified another three articles from the reference lists of the included papers and from already published review articles. Therefore, 39 studies were included in the final analysis. Table 1 illustrates the main characteristics of the included studies. Three studies examined associations of balance and lower-extremity muscle strength/power in children (n = 145 subjects), one study in adolescents (n = 28 subjects), nine studies in young adults (n = 285 subjects), three studies in middle-aged adults (n = 68 subjects), and 23 studies in old adults (n = 3766 subjects). All eligible studies included at least one measure of balance (e.g., CoP displacement during one-legged stance) and lower-extremity muscle strength (e.g., MVC)/power (e.g., jump height). Irrespective of the age category, 16 studies reported correlations between static steady-state balance (e.g., CoP displacement during one-legged stance) and maximal strength (e.g., MVC), four studies between static steady-state balance and explosive force (e.g., RFD), and nine studies between static steady-state balance and muscle power (e.g., jump height). In terms of dynamic steady-state balance (e.g., gait speed), 22 studies investigated correlations with maximal strength (e.g., MVC), three studies with explosive force (e.g., RFD), and nine studies with muscle power (e.g., jump height). Correlations of proactive balance (e.g., maximal reach distance in the functional-reach-test) with maximal strength (e.g., MVC) were reported in 12 studies, with explosive force (e.g., RFD) in one study, and with muscle power (e.g., jump height) in six studies. Lastly, ten studies examined correlations of reactive balance (e.g., CoP displacement during perturbed one-legged stance) with maximal strength (e.g., MVC), nine studies with explosive force (e.g., RFD), and ten studies with muscle power (e.g., jump height).
Fig. 1

Flow chart illustrating the different phases of the search and selection

Table 1

Studies examining associations between measures of balance and lower-extremity muscle strength/power by age group

Study

No. of subjects; sex; age [years (range or mean ± SD)]

Balance test parameter/outcome

Strength/power test parameter/outcome

z-transformed r z values; explained variance (r 2)

Children (n = 3)

 Granacher and Gollhofer [32]

30; F (16), M (14); 6–7

RB: 20-s two-legged stance after perturbation with eyes opened, CoP in ap-/ml-direction

sSSB: 20-s two-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: ankle plantarflexor

RFD: ankle plantarflexor

P: CMJ, height, power

RB-MVC: 0.16, 3 %

RB-RFD: 0.19, 4 %

RB-P: 0.16, 3 %

sSSB-MVC: 0.13, 2 %

sSSB-RFD: 0.11, 1 %

sSSB-P: 0.15, 2 %

 Ibrahim et al. [33]

94; M; 6–10

dSSB: 20-s two-legged stance on Biodex Balance System, overall stability index

MVC: hip flexors/extensors, knee flexors/extensors, ankle dorsiflexors/plantarflexors

dSSB-MVC: 0.95, 55 %

 Muehlbauer et al. [34]

21; F (8), M (13); 7–10

dSSB: 10-m walk, speed

PB: TUG, time; FRT, distance

RB: 10-s two-legged stance after perturbation with eyes opened, SO in ap-/ml-direction

sSSB: 30-s two-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: leg extensors

P: CMJ, height

dSSB-MVC: 0.28, 7 %

dSSB-P: 0.46, 18 %

PB-MVC: TUG = 0.42, 16 %; FRT = 0.61, 30 %

PB-P: TUG = 0.45, 18 %; FRT = 0.44, 17 %

RB-MVC: 0.15, 2 %

RB-P: 0.17, 3 %

sSSB-MVC: 0.09, 1 %

sSSB-P: 0.31, 9 %

Adolescents (n = 1)

 Granacher and Gollhofer [35]

28; F (15), M (13); 16–17

RB: 10-s one-legged stance after perturbation with eyes opened, SO in ap-/ml-direction

sSSB: 30-s one-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: leg extensors

RFD: leg extensors

P: CMJ, force, height

RB-MVC: 0.07, 0 %

RB-RFD: 0.09, 1 %

RB-P: 0.12, 1 %

sSSB-MVC: 0.08, 1 %

sSSB-RFD: 0.07, 0 %

sSSB-P: 0.11, 1 %

Young adults (n = 9)

 Izquierdo et al. [37]

12; M; 21 ± 1

dSSB: alternating knee raise, total CoP displacement area, length

RB: 30-s two-legged stance with eyes opened and to score a target in response to an external signal, time to reach/remain in the target

MVC: leg extensors

RFD: leg extensors

P: CMJ, height; SJ, height; SLJ, distance

dSSB-MVC: 0.10, 1 %

dSSB-RFD: 0.34, 11 %

dSSB-P: 0.31, 9 %

RB-MVC: 0.25, 6 %

RB-RFD: 0.18, 3 %

RB-P: 0.30, 8 %

 Katayama et al. [38]

57; F; 23 ± 2

sSSB: 30-s two-legged stance with eyes opened/closed; 10-s one-legged stance with eyes opened/closed, total CoP displacement area, length

P: knee flexors/extensors

sSSB-P: 0.29, 8 %

 McCurdy and Langford [39]

42; F (25), M (17); 22 ± 2

dSSB: one-legged stance with eyes opened on unstable ground; time

sSSB: one-legged stance with eyes opened, time

MVC: 1RM unilateral squat

sSSB-MVC: 0.21, 4 %

dSSB-MVC: 0.09, 1 %

 Oshita and Yano [40]

14; M; 21 ± 1

sSSB: one-legged stance with eyes closed, time

MVC: ankle plantarflexors

sSSB-MVC: 0.13, 2 %

 Piirainen et al. [42]

10; M; 27 ± 3

RB: 30-s two-legged stance following a perturbation impulse with eyes opened, CoP displacements in ap-/ml-direction

sSSB: 5-s two-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: knee extensors, ankle plantarflexors

RFD: knee extensors, ankle plantarflexors

RB-MVC: NR

RB-RFD: 0.27, 7 %

sSSB-MVC: NR

sSSB-RFD: NR

 Oshita and Yano [41]

12; M; 21 ± 1

sSSB: two-legged stance with eyes opened, total CoP displacement velocity

MVC: ankle plantarflexors

sSSB-MVC: 0.33, 10 %

 Hesari et al. [44]

47; F (24), M (23); 20 ± 4

PB: star-excursion-balance test

MVC: hip flexors/extensors/abductors/adductors

PB-MVC: 0.29, 8 %

 Muehlbauer et al. [43]

27; F (19), M (8); 23 ± 4

RB: 10-s one-legged stance following a perturbation impulse with eyes opened, CoP displacements in ap-/ml-direction

sSSB: 30-s one-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: ankle plantarflexors

RFD: ankle plantarflexors

P: CMJ, height, power

RB-MVC: 0.24, 6 %

RB-RFD: 0.30, 8 %

RB-P: 0.12, 1 %

sSSB-MVC: 0.16, 3 %

sSSB-RFD: 0.05, 0 %

sSSB-P: 0.09, 1 %

 Teyhen et al. [36]

64; F (11), M (53); 25 ± 4

PB: Y-balance test, reach distance

MVC: hip external rotators/abductors

P: 6-m timed hop test, time; triple crossover hop test, distance

RB-MVC: NR

RB-P: 0.35, 11 %

Middle-aged adults (n = 3)

 Izquierdo et al. [37]

10; M; 40 ± 2

dSSB: alternating knee raise, total CoP displacement area, length

RB: 30-s two-legged stance with eyes opened and to score a target in response to an external signal, time to reach/remain in the target

MVC: leg extensors

RFD: leg extensors

P: CMJ, height; SJ, height; SLJ, distance

dSSB-MVC: 0.09, 1 %

dSSB-RFD: 0.30, 8 %

dSSB-P: 0.38, 13 %

RB-MVC: 0.38, 13 %

RB-RFD: 0.77, 42 %

RB-P: 0.22, 5 %

 Holviala et al. [46]

26; F; 53 ± 2

dSSB: 10-m walk, time

MVC: leg extensors

dSSB-MVC: 0.69, 36 %

 Muehlbauer et al. [45]

32; F (9), M (23); 56 ± 4

RB: 10-s one-legged stance following a perturbation impulse with eyes opened, CoP displacements in ap-/ml-direction

sSSB: 30-s one-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: ankle plantarflexors

RFD: ankle plantarflexors

P: CMJ, height, power

RB-MVC: 0.09, 1 %

RB-RFD: 0.10, 1 %

RB-P: 0.09, 1 %

sSSB-MVC: 0.09, 1 %

sSSB-RFD: 0.15, 2 %

sSSB-P: 0.24, 6 %

Old adults (n = 23)

 Iverson et al. [47]

54; M; 60–90

sSSB: sharpened Romberg test, time; one-legged stance test with eyes opened/closed, time

MVC: hip flexors/extensors/abductors

sSSB-MVC: 0.42, 16 %

 Buchner et al. [48]

409; F (245), M (164); 60–96

dSSB: 15.2-m walk, gait speed

MVC: knee extensors/flexors, ankle dorsiflexors/plantarflexors

dSSB-MVC: 0.45, 18 %

 Judge et al. [49]

26; NR; 79 ± 6

dSSB: 5-m walk, step length

MVC: knee extensors, ankle plantarflexors

dSSB-MVC: 0.62, 30 %

 Izquierdo et al. [37]

10; M; 71 ± 5

dSSB: alternating knee raise, total CoP displacement area, length

RB: 30-s two-legged stance with eyes opened and to score a target in response to an external signal, time to reach/remain in the target

MVC: leg extensors

RFD: leg extensors

P: CMJ, height; SJ, height; SLJ, distance

dSSB-MVC: 0.07, 0 %

dSSB-RFD: 0.22, 5 %

dSSB-P: 0.42, 16 %

RB-MVC: 0.19, 4 %

RB-RFD: 0.37, 13 %

RB-P: 0.43, 16 %

 Ringsberg et al. [50]

230; F; 75

dSSB: 30-m walk, time, cadence

RB: 20-s two-legged stance with eyes opened on a moving platform, total CoP displacement length

sSSB: one-legged stance, time; 20-s two-legged stance with eyes opened/closed, total CoP displacement length

MVC: knee extensors/flexors, ankle dorsiflexors

dSSB-MVC: 0.34, 11 %

RB-MVC: NR

sSSB-MVC: 0.19, 4 %

 Burnfield et al. [51]

81; M; 60–92

dSSB: 10-m walk, gait speed

MVC: hip extensors/flexors, knee extensors/flexors, ankle dorsiflexors/plantarflexors

dSSB-MVC: 0.55, 25 %

 Suzuki et al. [52]

34; F; 65–84

dSSB: 10-m forward/backward tandem walk at habitual/maximal speed, time

MVC: ankle plantarflexors/dorsiflexors

P: ankle plantarflexors/dorsiflexors

dSSB-MVC: 0.46, 18 %

dSSB-P: 0.47, 19 %

 Menz et al. [53]

176; F (120), M (56); 62–96

dSSB: 6-m walk, gait speed

PB: alternate step test, time

sSSB: two-legged stance with eyes opened, total CoP displacement area, length

MVC: knee extensors, ankle dorsiflexors

dSSB-MVC: 0.64, 32 %

PB-MVC: 0.56, 26 %

sSSB-MVC: 0.34, 11 %

 Tsang and Hui-Chan [65]

48; F (24), M (24); 70 ± 5

sSSB: 30-s two-legged stance with eyes opened, total CoP displacement angle

RB: one-legged stance while perturbation with eyes opened, CoP displacement angle

MIT: knee flexors/extensors

sSSB-MIT: 0.07, 0 %

RB-MIT: 0.47, 19 %

 Callisaya et al. [64]

278; F (124), M (154); 60–86

dSSB: 4.6-m walk, speed, cadence, step length/width

sSSB: 30-s two-legged stance with eyes closed/opened, total CoP displacement length

MVC: leg extensors

dSSB-MVC: 0.28, 7 %

sSSB-MVC: NR

 Melzer et al. [54]

43, F (27), M (16);78 ± 6

PB: LOS test, total CoP displacement length

sSSB: 30-s two-legged stance with eyes opened, total CoP displacement area, length, velocity

MVC: ankle dorsiflexors/plantarflexors

PB-MVC: 0.46, 18 %

sSSB-MVC: 0.08, 1 %

 Piirainen et al. [42]

20; M; 64 ± 3

RB: 30-s two-legged stance while perturbation with eyes opened, CoP displacements in ap-/ml-direction

sSSB: 5-s two-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: knee extensors, ankle plantarflexors

RFD: knee extensors, ankle plantarflexors

RB-MVC: NR

RB-RFD: 0.54, 24 %

sSSB-MVC: NR

sSSB-RFD: NR

 Bouchard et al. [55]

1,280, F (645), M (635);68 ± 9

dSSB: 20-foot walk, gait speed

MVC: leg extensors

dSSB-MVC: 0.24, 6 %

 Shimada et al. [56]

213; F (130), M (83); 65–96

dSSB: 6-m walk, time

PB: TUG, time

sSSB: one-legged stance with eyes opened, time

P: CST, time

dSSB-P: 0.39, 14 %

PB-P: 0.41, 15 %

sSSB-P: 0.05, 0 %

 Spink et al. [57]

305; 211 (F), M (94); 65–93

dSSB: 6-m walk, gait speed

PB: alternate step test, time

sSSB: two-legged stance with eyes opened, total CoP displacement area, length

MVC: ankle plantarflexors/dorsiflexors/inversors/eversors

P: CST, time

dSSB-MVC: 0.22, 5 %

dSSB-P: NR

PB-MVC: 0.38, 13 %

PB-P: NR

sSSB-MVC: 0.19, 4 %

sSSB-P: NR

 Marcus et al. [58]

109; F (77), M (32); 74 ± 7

PB: TUG, time

MVC: knee extensors

PB-MVC: 0.48, 20 %

 Muehlbauer et al. [59]

24; F (13), M (11); 70 ± 5

dSSB: 10-m walk, gait speed

PB: TUG, time; FRT, distance

RB: 10-s two-legged stance following a perturbation impulse with eyes opened, SO in ap-/ml-direction

sSSB: 30-s two-legged stance with eyes opened, CoP displacements in ap-/ml-direction

MVC: leg extensors

P: CMJ, height, power

dSSB-MVC: 0.03, 0 %

dSSB-P: 0.17, 3 %

PB-MVC: TUG = 0.10, 1 %; FRT = 0.51, 22 %

PB-P: TUG = 0.36, 12 %; FRT = 0.21, 4 %

RB-MVC: 0.15, 2 %

RB-P: 0.08, 1 %

sSSB-MVC: 0.32, 10 %

sSSB-P: 0.23, 5 %

 Shin et al. [60]

72; F (43), M (29); 70 ± 6

dSSB: 8.3-m walk, stride length/time variability

MVC: leg extensors/flexors, ankle dorsiflexors/plantarflexors

dSSB-MVC: 0.07, 0 %

 Forte et al. [27]

57; F (33), M (24); 70 ± 3

dSSB: 10-m walk, gait speed

MVC: knee extensors

P: CMJ, power

dSSB-MVC: 0.37, 13 %

dSSB-P: 0.35, 11 %

 Miyazaki et al. [61]

124; M; 73 ± 7

dSSB: 11-m walk, gait speed

PB: TUG, time

sSSB: one-legged stance with eyes opened, time

MVC: leg extensors

dSSB-MVC: 0.60, 29 %

PB-MVC: 0.69, 36 %

sSSB-MVC: 0.59, 28 %

 Forte et al. [26]

57; F (33), M (24); 65–75

dSSB: 10-m walk, gait speed

sSSB: Romberg test, total CoP displacement length, velocity; two-legged tandem stance test with eyes opened, total CoP displacement length, velocity

MVC: knee extensors

P: CMJ, power

dSSB-MVC: 0.28, 7 %

dSSB-P: 0.27, 7 %

sSSB-MVC: 0.35, 11 %

sSSB-P: 0.33, 10 %

 Jenkins et al. [62]

16; M; 72 ± 7

PB: FRT, distance

MVC: leg extensors

RTD: leg extensors at 60–180 °/s

P: leg extensors

PB-MVC: 0.23, 5 %

PB-RTD: 0.22, 5 %

PB-P: 0.54, 24 %

 Pisciottano et al. [63]

100; F; 71 ± 5

PB: TUG, time

MVC: knee extensors/flexors

PB-MVC: 0.30, 8 %

1RM one-repetition maximum, ap anterior-posterior, CMJ countermovement jump, CoP center of pressure, CST chair-stand-test, dSSB dynamic steady-state balance, F female, FRT functional-reach-test, LOS limits-of-stability-test, M male, ml medio-lateral, MIT maximum isokinetic torque, MVC maximum voluntary contraction, NR not reported, P Power, PB proactive balance, RB reactive balance, RFD rate of force development, RTD rate of torque development, r z weighted z-transformed Pearson’s correlation coefficients, SD standard deviation, sSSB static steady-state balance, SJ squat jump, SLJ standing long jump, SO summed oscillations, TUG timed-up-and-go-test

3.2 Associations Between Measures of Balance and Lower-Extremity Muscle Strength/Power

3.2.1 Children

Three studies investigated associations between variables of balance and lower-extremity muscle strength/power in children [32, 33, 34]. Figures 2a and 3a illustrate the associations of measures of static steady-state balance with maximal strength and muscle power. Weighted mean r z values amounted to 0.11 for measures of maximal strength (I 2 = 0 %, Chi-squared (χ 2) = 0.02, degrees of freedom [df] = 1, p = 0.90, two studies [32, 34]) and 0.21 for outcomes of muscle power (I 2 = 0 %, χ 2 = 0.27, df = 1, p = 0.60, two studies [32, 34]). Back-transformed r values of 0.11 and 0.21 indicated small-sized correlations. Only one study [32] reported a small correlation (r z = 0.11, r = 0.11) between static steady-state balance (i.e., 20-s two-legged stance) and explosive force (i.e., RFD ankle plantarflexors) (Table 1). Additionally, associations between dynamic steady-state balance and maximal strength revealed a weighted mean r z value of 0.65 (I 2 = 85 %, χ 2 = 6.64, df = 1, p = 0.01, two studies [33, 34], Fig. 4a). The corresponding back-transformed r value of 0.57 is indicative of small-sized correlations. No study reported associations of dynamic steady-state balance with explosive force and muscle power. Only one study [34] observed small associations between proactive balance (i.e., FRT) and maximal strength (i.e., MVC leg extensors) (r z = 0.61, r = 0.54) and muscle power (i.e., CMJ) (r z = 0.44, r = 0.41) (Table 1). No study reported associations of proactive balance with explosive force. Lastly, associations of reactive balance with maximal strength and muscle power are shown in Figs. 5a and 6a, respectively. Weighted mean r z values amounted to 0.16 for measures of maximal strength (I 2 = 0 %, χ 2 = 0, df = 1, p = 0.97, two studies [32, 34]) and 0.16 for outcomes of muscle power (I 2 = 0 %, χ 2 = 0, df = 1, p = .97, two studies [32, 34]). Back-transformed r values of 0.16 and 0.16 indicated small-sized correlations. In addition, one study [32] observed a small association (r z = 0.19, r = 0.19) between measures of reactive balance (i.e., perturbed 20-s two-legged stance) and explosive force (i.e., CMJ) (Table 1).
Fig. 2

Associations between static steady-state balance (e.g., postural sway during one-legged stance) and maximal strength (e.g., maximum voluntary contraction) of the lower-extremities in children (a), young adults (b), and old adults (c). CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

Fig. 3

Pearson’s r values (z-transformed) for associations between static steady-state balance (e.g., postural sway during one-legged stance) and muscle power (e.g., jump height) of the lower-extremities in children (a), young adults (b), and old adults (c). CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

Fig. 4

Pearson’s r values (z-transformed) for associations between dynamic steady-state balance (e.g., gait speed) and maximal strength (e.g., maximum voluntary contraction) of the lower-extremities in children (a), and young (b), middle-aged (c), and old (d) adults. CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

Fig. 5

Pearson’s r values (z-transformed) for associations between reactive balance (e.g., postural sway during perturbed one-legged stance) and maximal strength (e.g., maximum voluntary contraction) of the lower-extremities in children (a), and young (b), middle-aged (c), and old (d) adults. CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

Fig. 6

Pearson’s r values (z-transformed) for associations between reactive balance (e.g., postural sway during perturbed one-legged stance) and muscle power (e.g., jump height) of the lower-extremities in children (a), and young (b), middle-aged (c), and old (d) adults. CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

3.2.2 Adolescents

Only one study [35] analyzed associations between measures of balance and muscle strength/power of the lower-extremities in adolescents. As a result, small-sized correlations were detected for measures of static steady-state balance (i.e., 30-s one-legged stance) with maximal strength (i.e., MVC leg extensors) (r z  = 0.08, r = 0.08), explosive force (i.e., RFD leg extensors) (r z  = 0.07, r = 0.07), and muscle power (i.e., CMJ) (r z  = 0.11, r = 0.11) (Table 1). In addition, small correlations were obtained for reactive balance (i.e., perturbed 10-s one legged stance) with maximal strength (r z  = 0.07, r = 0.07), explosive force (r z  = 0.09, r = 0.09), and muscle power (r z  = 0.12, r = 0.12) (Table 1). No study reported associations for measures of dynamic steady-state and proactive balance with maximal strength, explosive force, and muscle power.

3.2.3 Young Adults

Nine studies reported associations between proxies of balance and lower-extremity muscle strength/power in young adults [36, 37, 38, 39, 40, 41, 42, 43, 44]. Figures 2b and 3b illustrate associations of static steady-state balance with maximal strength and muscle power, respectively. Weighted mean r z values amounted to 0.20 for variables of maximal strength (I 2 = 0 %, χ 2 = 0.25, df = 3, p = 0.97, four studies [39, 40, 41, 43]) and 0.22 for measures of muscle power (I 2 = 0 %, χ 2 = 0.67, df = 1, p = 0.41, two studies [38, 43]). Back-transformed r values of 0.20 and 0.22 indicated small-sized correlations. Only one study [43] observed a small-sized correlation (r z  = 0.05, r = 0.05) between static steady-state balance (i.e., 30-s one-legged stance) and explosive force (i.e., CMJ) (Table 1). In addition, associations between dynamic steady-state balance and maximal strength resulted in a weighted mean r z value of 0.09 (I 2 = 0 %, χ 2 = 0, df = 1, p = 0.98, two studies [37, 39], Fig. 4b). The corresponding back-transformed r value of 0.09 is indicative of small-sized correlations. Only one study [37] reported small-sized associations of dynamic steady-state balance (i.e., alternating knee raise) with explosive force (i.e., RFD leg extensors) (r z = 0.34, r = 0.33) and muscle power (i.e., CMJ) (r z = 0.31, r = 0.30) (Table 1). Furthermore, only one study [44] stated a small-sized relationship (r z = 0.29, r = 0.28) between proactive balance (i.e., maximal reach distance in the star-excursion-balance-test) and maximal strength (i.e., MVC) (Table 1). Yet, no study reported associations of proactive balance with explosive force and muscle power. Lastly, associations of reactive balance with maximal strength, explosive force, and muscle power are illustrated in Figs. 5b, 6b, and 7a, respectively. Weighted mean r z values amounted to 0.24 for measures of maximal strength (I 2 = 0 %, χ 2 = 0, df = 1, p = 0.98, two studies [37, 43]), 0.27 for variables of explosive force (I 2 = 0 %, χ 2 = 0.10, df = 2, p = 0.95, three studies [37, 42, 43]), and 0.28 for outcomes of muscle power (I 2 = 0 %, χ 2 = 0.93, df = 2, p = 0.63, three studies [36, 37, 43]). Back-transformed r values of 0.24, 0.26, and 0.27 indicated small-sized correlations.
Fig. 7

Pearson’s r values (z-transformed) for associations between reactive balance (e.g., postural sway during perturbed one-legged stance) and explosive force (e.g., rate of force development) of the lower-extremities in young (a), middle-aged (b), and old adults (c). CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

3.2.4 Middle-Aged Adults

Three studies provided associations between outcomes of balance and muscle strength/power of the lower-extremities in middle-aged adults [37, 45, 46]. Only one study [45] reported small-sized correlations of static steady-state balance (i.e., 30-s one-legged stance) with maximal strength (i.e., MVC ankle plantarflexors) (r z = 0.09, r = 0.09), explosive force (i.e., RFD ankle plantarflexors) (r z = 0.15, r = 0.15), and muscle power (i.e., CMJ) (r z = 0.24, r = 0.24) (Table 1). Additionally, associations between dynamic steady-state balance and maximal strength revealed a weighted mean r z value of 0.47 (I 2 = 48 %, χ 2 = 1.91, df = 1, p = 0.17, two studies [37, 46], Fig. 4c). The corresponding back-transformed r value of 0.44 is indicative of small-sized correlations. Additional small associations of dynamic steady-state balance (i.e., alternating knee raise) with explosive force (i.e., RFD leg extensors) (r z = 0.30, r = 0.29) and muscle power (i.e., CMJ) (r z = 0.38, r = 0.36) were observed in one study only [37] (Table 1). No study reported associations of proactive balance with maximal strength, explosive force, and muscle power. Furthermore, associations of reactive balance with maximal strength, explosive strength, and muscle power are shown in Figs. 5c, 6c, and 7b, respectively. Weighted mean r z values amounted to 0.15 for measures of maximal strength (I 2 = 0 %, χ 2 = 0.47, df = 1, p = 0.49, two studies [37, 45]), 0.35 for variables of explosive force (I 2 = 60 %, χ 2 = 2.49, df = 1, p = 0.11, two studies [37, 45]), and 0.12 for outcomes of muscle power (I 2 = 0 %, χ 2 = 0.09, df = 1, p = 0.76, two studies [37, 45]). Back-transformed r values of 0.15, 0.34, and 0.12 indicated small-sized correlations.

3.2.5 Old Adults

Twenty-three studies reported associations between parameters of balance and lower-extremity muscle strength/power in old adults [26, 27, 37, 42, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]. Figures 2c and 3c illustrate the associations of static steady-state balance with maximal strength and muscle power, respectively. Weighted mean r z values amounted to 0.28 for measures of maximal strength (I 2 = 65 %, χ 2 = 20.06, df = 7, p = 0.005, eight studies [26, 50, 53, 54, 57, 59, 61, 65]) and 0.16 for outcomes of muscle power (I 2 = 43 %, χ 2 = 3.49, df = 2, p = 0.17, three studies [26, 56, 59]). Back-transformed r values of 0.27 and 0.16 indicated small-sized correlations. No study reported associations between static steady-state balance and explosive force. Further associations of dynamic steady-state balance with maximal strength and muscle power are shown in Figs. 4d and 8, respectively. Weighted mean r z values amounted to 0.37 for measures of maximal strength (I 2 = 74 %, χ 2 = 56.91 df = 15, p < 0.001, 16 studies [26, 27, 37, 47, 48, 49, 50, 51, 52, 53, 55, 57, 59, 60, 61, 64]) and 0.36 for outcomes of muscle power (I 2 = 0 %, χ 2 = 1.74, df = 5, p = 0.88, six studies [26, 27, 37, 52, 56, 59]). Back-transformed r values of 0.35 and 0.35 indicated small-sized correlations. Only one study [37] reported a small-sized association (r z = 0.22, r = 0.22) between dynamic steady-state balance (i.e., alternating knee raise) and explosive force (i.e., RFD leg extensors) (Table 1). In addition, Figs. 9 and 10 illustrate the associations of proactive balance with maximal strength and muscle power, respectively. Weighted mean r z values amounted to 0.47 for measures of maximal strength (I 2 = 47 %, χ 2 = 13.16, df = 7, p = 0.07, eight studies [53, 54, 57, 58, 59, 61, 62, 63]) and 0.40 for outcomes of muscle power (I 2 = 0 %, χ 2 = 1.02, df = 2, p = 0.60, three studies [56, 59, 62]). Back-transformed r values of 0.44 and 0.38 indicated small-sized correlations. Furthermore, a small-sized association (r z = 0.22, r = 0.22) was found between proactive balance (i.e., timed-up-and-go-test) and explosive force (i.e., MVC knee extensors) in one study only [63] (Table 1). Lastly, associations of reactive balance with maximal strength, explosive force, and muscle power are shown in Figs. 5d, 6d, and 7c, respectively. Weighted mean r z values amounted to 0.35 for measures of maximal strength (I 2 = 0 %, χ 2 = 1.64, df = 2, p = 0.44, three studies [37, 59, 65]), 0.49 for variables of explosive force (I 2 = 0 %, χ 2 = 0.14, df = 1, p = 0.71, two studies [37, 42]), and 0.17 for outcomes of muscle power (I 2 = 0 %, χ 2 = 0.64, df = 1, p = 0.43, two studies [37, 59]). Back-transformed r values of 0.34, 0.45, and 0.17 indicated small-sized correlations.
Fig. 8

Pearson’s r values (z-transformed) for associations between dynamic steady-state balance (e.g., gait speed) and muscle power (e.g., jump height) of the lower-extremities in old adults. CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

Fig. 9

Pearson’s r values (z-transformed) for associations between proactive balance (e.g., distance in the functional-reach-test) and maximal strength (e.g., maximum voluntary contraction) of the lower-extremities in old adults. CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

Fig. 10

Pearson’s r values (z-transformed) for associations between proactive balance (e.g., distance in the functional-reach-test) and muscle power (e.g., jump height) of the lower-extremities in old adults. CI confidence interval, df degrees of freedom, IV inverse variance, r back-transformed Pearson’s correlation coefficients, r z weighted z-transformed Pearson’s correlation coefficients, SE standard error

3.2.6 Age Differences

Table 2 shows the comparison of correlation coefficients between children, young adults, and old adults. Statistically significant differences between age groups were obtained for the associations of measures of dynamic steady-state balance with maximal strength only. More precisely, the r value in children (r = 0.57) was significantly larger than that in young (r = 0.09, z = 3.30, p = 0.001) and old (r = 0.35, z = 2.94, p = 0.002) adults. Further, the r value in old adults was significantly larger (z = 1.95, p = 0.03) than that in young adults.
Table 2

Comparison of correlation coefficients between children, young adults, and old adults

Comparison

sSSB

dSSB

RB

Maximal strength

 Children vs. young adults

0.52 (0.30)

3.30 (0.001)

0.38 (0.35)

 Children vs. old adults

1.23 (0.13)

2.94 (0.002)

1.05 (0.15)

 Young vs. old adults

0.68 (0.25)

1.95 (0.03)

0.54 (0.29)

Explosive force

 Children vs. young adults

NA

NA

NA

 Children vs. old adults

NA

NA

NA

 Young vs. old adults

NA

NA

0.90 (0.18)

Muscle power

 Children vs. young adults

0.06 (0.48)

NA

0.66 (0.26)

 Children vs. old adults

0.33 (0.37)

NA

0.05 (0.48)

 Young vs. old adults

0.50 (0.31)

NA

0.51 (0.30)

Data are presented as z values with p values in parentheses. For proactive balance, no comparison was performed due to an insufficient number of studies available

dSSB dynamic steady-state balance, NA not available, RB reactive balance, sSSB static steady-state balance

4 Discussion

The present systematic review and meta-analysis characterized and quantified associations between measures of balance and lower-extremity muscle strength/power in healthy individuals across the lifespan. We hypothesized large-sized correlations between proxies of balance and strength/power of the lower extremities, which is based on the premise that similar neurophysiological structures (e.g., activation of corticospinal pathways during perturbed stance and explosive force production) are responsible for the control of balance and lower-extremity muscle strength/power [9, 10, 11, 12]. Moreover, transfer effects in terms of training-induced improvements from balance training to an unpracticed strength task, and vice versa, were reported in the literature [10, 13, 14], indicating an interaction between these neuromuscular components. However, our analyses revealed predominately small-sized correlations between variables of balance and muscle strength/power of the lower extremities, which is why our initial hypothesis has to be rejected. This finding was independent from the investigated balance (i.e., static/dynamic steady-state, proactive, reactive balance), strength (i.e., maximal strength, explosive force), and power (e.g., jumps) components.

What are likely explanations for the observed small-sized correlations between measures of balance and lower-extremity muscle strength/power? First, although similar neurophysiological mechanisms (e.g., activation of corticospinal pathways) are involved in the regulation of balance (i.e., perturbed stance) and strength (i.e., explosive force production), it seems that their function during balance control and strength/power production is task specific [9, 10, 11, 12]. Indeed, studies investigating spinal and corticospinal excitability during the execution of a strength- or balance-related task showed different activation patterns. For example, short-latency responses induced by transcranial magnetic stimulation were facilitated during the execution of isometric ankle dorsi- and plantarflexions [66] but were unchanged when performing a reactive balance task (i.e., perturbed stance) [67].

Second, transfer effects in terms of strength gains after balance training and balance gains after strength training were reported in the literature [9, 10, 13, 14], yet the underlying adaptations were found to be task specific [9, 10]. For example, Gruber et al. [9, 10] investigated the effects of 4 weeks of balance compared to ballistic strength training on measures of strength using biomechanical (i.e., maximum isometric ankle plantarflexor strength) and electrophysiological (i.e., surface electromyography [EMG], H-reflex, and stretch reflex recording) testing equipment in healthy young adults. The authors reported significant improvements in maximal RFD [9, 10] following both balance and ballistic strength training. However, significant differences in muscle activation and spinal reflex excitability during the execution of strength tasks were found between the two training regimens. More specifically, ballistic strength, but not balance training, resulted in significant increases in EMG activities of the soleus and gastrocnemius muscle during the execution of maximal isometric ankle plantarflexions [9]. In contrast, balance training produced significantly lower peak-to-peak amplitudes of soleus stretch reflexes when performing fast dorsiflexions, whereas no changes occurred after ballistic strength training. In addition, the ratio of the maximum H-reflex to the maximum efferent motor response (H max:M max) was significantly reduced following balance training but not after ballistic strength training [10]. Thus, the authors concluded that different neural mechanisms are responsible for similar improvements in measures of lower-extremity muscle strength following balance compared to ballistic strength training.

Third, meta-analyses reflect the highest level on the evidence-based medicine pyramid as compared with original research work [68]. More specifically and in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement guidelines [69], findings from studies that investigated associations between proxies of balance and lower-extremity muscle strength/power and fulfilled predefined selection criteria (e.g., reported a least one measure of balance and strength/power of the lower extremities) were extracted and aggregated. However, a limitation of our meta-analytical approach is that the potential moderating effect of age on associations between the variables of interest (i.e., balance and lower-extremity strength/power) cannot be directly studied. In other words, the influence of age cannot be separated from those of other factors (e.g., sex, training status). Thus, the present findings are preliminary and have to be interpreted with caution. To further our knowledge in this area, studies should be conducted that examine associations between measures of balance and lower-extremity muscle strength/power and that control for potential moderator variables such as age, sex, and training status. This could be realized by conducting a single study that considers, for example, the potential moderating effect of age on the relationship between balance and muscle strength/power in different age groups (i.e., children, adolescents, and young, middle-aged, and old adults).

Furthermore, we hypothesized that age has an impact on the associations between measures of balance and lower-extremity muscle strength/power. As a result, we can partially confirm our second hypothesis. Significant age differences were found for associations between measures of dynamic steady-state balance and maximal strength. More specifically, correlations were larger in children than in young and old adults as well as in old than in young adults. However, the analyses failed to detect further significant age differences in the relationship between other components of balance and strength/power of the lower extremities. Thus, it can be postulated that maturational and biological aging processes of the neuromuscular system may have an influence on the associations between balance and lower-extremity muscle strength/power that is limited to the relationship of dynamic steady-state balance with maximal strength. As a consequence, further research is needed to determine whether age-related differences are specific to the observed associations between measures of dynamic steady-state balance and maximal strength of the lower extremities or if they could also be detected for other proxies of balance and lower-extremity strength/power. To control for this issue, the same test equipment/procedure and outcome measures should be used when comparing different age groups.

A possible reason for the larger correlations between measures of dynamic steady-state balance and maximal strength in children and old adults than in young adults could be caused by differences in the level of task automation. In this regard, previous research [70, 71] showed that during stages of less movement automation (i.e., early in practice or low levels of movement experience), the control of a motor task is relatively unspecific. In other words, muscle selection, computation, and their sequenced activation is not effectively developed and therefore coded on a rather abstract level [72]. As a consequence, the execution of movements with different task characteristics (e.g., balance task vs. strength/power task) can easily be performed, as is shown by larger correlation coefficients. Despite the fact that the performance level of the executed task is low, the result of the movement outcome is more stable because the motor program does not have to be specifically coded. This might be a likely scenario in children and old adults because both age groups show reduced levels of motor control either due to maturation (i.e., children) or biological aging (i.e., old adults) [16, 17, 18, 19, 20, 21]. However, with an increasing level of movement automation (i.e., late in practice or high levels of movement experience), the control of a motor task becomes more specific [72]. This is achieved through appropriate movement coding, which results in high performance levels. However, as movement control becomes more specific, the ability to switch between different tasks and their execution is reduced, which is reflected in lower correlation coefficients. Even though performance levels are high, movement execution is more susceptible (less stable) because motor programs have to be coded to achieve relatively specific (more automated) movements. This scenario is likely for young adults because their neuromuscular system is fully developed and enables adequate muscle selection and computation to achieve the desired activation sequence.

Despite the fact that significant age differences were found for the association between measures of dynamic steady-state balance with maximal strength, their size was still small (i.e., r values ≤0.69), as was observed for the relationship between other components of balance and lower-extremity muscle strength/power. In general, this indicates that these components are independent of each other (i.e., task specific) and should therefore be tested and trained complementarily across the lifespan. More specifically, testing of individuals at risk of suffering injuries and/or falls should include the assessment of balance and muscle strength/power. For example, Granacher et al. [22] provided recommendations for the assessment of balance and muscle strength/power in healthy older adults. In terms of balance assessment, they recommend that tests for dynamic steady-state (e.g., time/speed while walking 10 m) and reactive (e.g., push-and-release test) balance should be primarily conducted because falls predominantly occur during ambulation and balance perturbations [73]. With respect to the assessment of muscle strength/power, Granacher and colleagues [22] further recommend the application of tests for the assessment of muscle power (e.g., plyometric tests such as CMJ or more functional tests such as the five times chair rise test) because muscle power is more strongly associated with performance in everyday activities (e.g., rising from a chair, stair climbing) as compared with muscle strength [74].

With regards to the implications for training, our findings indicate that programs including all three components (i.e., balance, strength, and power) should be conducted to increase balance and muscular strength/power. This is supported by a study of Lacroix A, Kressig RW, Muehlbauer T, et al. unpublished data who conducted a combined balance and strength/power training program in healthy older adults (age range: 65–80 years). The program included task-specific exercises to improve static/dynamic steady-state, proactive, and reactive balance and strength/power of the lower extremities. Following 12 weeks of training (three times per week), the authors observed significant improvements in measures of static (i.e., Romberg test) and dynamic (i.e., 10-m walk test) steady-state, proactive (i.e., timed-up-and-go test, functional reach test), and reactive balance (e.g., push-and-release test) as well as in lower-extremity muscle strength (i.e., chair rise test) and power (i.e., stair-ascent-and-descent test).

4.1 Limitations

A limitation of the systematic review and meta-analysis is that correlative studies were analyzed representing cross-sectional designs. Therefore, cause and effect relations cannot be deduced. In addition, the investigated associations could be affected by other variables such as joint flexibility, muscle mass, and/or auditory/visual acuity. Further, the observed age-related effects regarding the association between dynamic steady-state balance and maximal strength could be biased due to differences in the number of studies available on that issue for children (two studies), young adults (two studies), and old adults (16 studies). As a consequence, further research is needed to determine whether age influences the relationship between balance and lower-extremity muscle strength/power. For example, an intergenerational approach could be used that incorporates children, adolescents, and young, middle-aged, and old adults when testing components of balance and lower-extremity muscle strength/power. The obtained results should be analyzed using traditional methods (i.e., Pearson product-moment correlation) as well as by applying more sophisticated statistical models such as regression analysis to examine the specific role of age. Moreover, such analyses have to be controlled for potential confounding factors such as the test condition and test parameter.

5 Conclusions

The present systematic review and meta-analysis revealed predominately small-sized correlations between measures of balance and lower-extremity muscle strength/power in children, adolescents, and young, middle-aged, and old adults. Significantly different but still small-sized correlation coefficients (i.e., larger r-value in children than in young and old adults as well as in old than in young adults) were found for associations between measures of dynamic steady-state balance and maximal strength. Our findings indicate that balance and muscle strength/power of the lower extremities are independent of each other and should therefore be tested (i.e., identification of people at risk of suffering injuries and/or falls) and trained (i.e., development of injury- and fall-prevention programs) complementarily across the lifespan. Further, it appears that maturational processes and biological aging of the neuromuscular system may have an effect on the associations between selected components of balance and lower-extremity muscle strength (e.g., the relationship of dynamic steady-state balance with maximal strength).

Notes

Compliance with Ethical Standards

This work was supported by a grant from the German Research Foundation (No. MU 3327/2-1) and by the Open Access Publication Fund of University of Potsdam. Thomas Muehlbauer, Albert Gollhofer, and Urs Granacher declare that they have no conflicts of interest.

References

  1. 1.
    Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100(2):126–31.PubMedCentralPubMedGoogle Scholar
  2. 2.
    Razmus I, Wilson D, Smith R, et al. Falls in hospitalized children. Pediatr Nurs. 2006;32(6):568–72.PubMedGoogle Scholar
  3. 3.
    Wang HK, Chen CH, Shiang TY, et al. Risk-factor analysis of high school basketball-player ankle injuries: a prospective controlled cohort study evaluating postural sway, ankle strength, and flexibility. Arch Phys Med Rehabil. 2006;87(6):821–5.CrossRefPubMedGoogle Scholar
  4. 4.
    Fousekis K, Tsepis E, Poulmedis P, et al. Intrinsic risk factors of non-contact quadriceps and hamstring strains in soccer: a prospective study of 100 professional players. Br J Sports Med. 2011;45(9):709–14.CrossRefPubMedGoogle Scholar
  5. 5.
    Rubenstein LZ. Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing. 2006;35(Suppl 2):ii37–41.Google Scholar
  6. 6.
    Witchalls J, Blanch P, Waddington G, et al. Intrinsic functional deficits associated with increased risk of ankle injuries: a systematic review with meta-analysis. Br J Sports Med. 2012;46(7):515–23.CrossRefPubMedGoogle Scholar
  7. 7.
    Stevens JA, Corso PS, Finkelstein EA, et al. The costs of fatal and non-fatal falls among older adults. Inj Prev. 2006;12(5):290–5.PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Mathers LJ, Weiss HB. Incidence and characteristics of fall-related emergency department visits. Acad Emerg Med. 1998;5(11):1064–70.CrossRefPubMedGoogle Scholar
  9. 9.
    Gruber M, Gruber SB, Taube W, et al. Differential effects of ballistic versus sensorimotor training on rate of force development and neural activation in humans. J Strength Cond Res. 2007;21(1):274–82.CrossRefPubMedGoogle Scholar
  10. 10.
    Gruber M, Taube W, Gollhofer A, et al. Training-specific adaptations of H- and stretch reflexes in human soleus muscle. J Mot Behav. 2007;39(1):68–78.CrossRefPubMedGoogle Scholar
  11. 11.
    Beck S, Taube W, Gruber M, et al. Task-specific changes in motor evoked potentials of lower limb muscles after different training interventions. Brain Res. 2007;1179:51–60.CrossRefPubMedGoogle Scholar
  12. 12.
    Schubert M, Beck S, Taube W, et al. Balance training and ballistic strength training are associated with task-specific corticospinal adaptations. Eur J Neurosci. 2008;27(8):2007–18.CrossRefPubMedGoogle Scholar
  13. 13.
    Granacher U, Gollhofer A, Kriemler S. Effects of balance training on postural sway, leg extensor strength, and jumping height in adolescents. Res Q Exerc Sport. 2010;81(3):245–51.CrossRefPubMedGoogle Scholar
  14. 14.
    Granacher U, Gruber M, Gollhofer A. Resistance training and neuromuscular performance in seniors. Int J Sports Med. 2009;30(9):652–7.CrossRefPubMedGoogle Scholar
  15. 15.
    Granacher U, Muehlbauer T, Gollhofer A, et al. An intergenerational approach in the promotion of balance and strength for fall prevention—a mini-review. Gerontology. 2011;57(4):304–15.CrossRefPubMedGoogle Scholar
  16. 16.
    Woollacott MH, Shumway-Cook A. Changes in posture control across the life span–a systems approach. Phys Ther. 1990;70(12):799–807.PubMedGoogle Scholar
  17. 17.
    Shumway-Cook A, Woollacott MH. The growth of stability: postural control from a development perspective. J Mot Behav. 1985;17(2):131–47.CrossRefPubMedGoogle Scholar
  18. 18.
    Woollacott MH, Shumway-Cook A. Maturation of feedback control of posture and equilibrium. In: Fedrizzi E, Avanzini G, Crenna P, editors. Motor development in children. New Barnet: John Libbey & Company Ltd; 1994. p. 59–70.Google Scholar
  19. 19.
    Maisonobe T, Hauw JJ, Dani SU, et al. Changes in the peripheral nervous system. In: Dani SU, Hori A, Walter GG, editors. Principles of neural aging. Amsterdam: Elsevier; 1997. p. 304–16.Google Scholar
  20. 20.
    McNeil CJ, Doherty TJ, Stashuk DW, et al. Motor unit number estimates in the tibialis anterior muscle of young, old, and very old men. Muscle Nerve. 2005;31(4):461–7.CrossRefPubMedGoogle Scholar
  21. 21.
    Terao S, Sobue G, Hashizume Y, et al. Age-related changes in human spinal ventral horn cells with special reference to the loss of small neurons in the intermediate zone: a quantitative analysis. Acta Neuropathol. 1996;92(2):109–14.CrossRefPubMedGoogle Scholar
  22. 22.
    Granacher U, Muehlbauer T, Gruber M. A qualitative review of balance and strength performance in healthy older adults: impact for testing and training. J Aging Res. 2012;2012:708905.PubMedCentralCrossRefPubMedGoogle Scholar
  23. 23.
    Granacher U, Gollhofer A, Hortobagyi T, et al. The importance of trunk muscle strength for balance, functional performance, and fall prevention in seniors: a systematic review. Sports Med. 2013;43(7):627–41.CrossRefPubMedGoogle Scholar
  24. 24.
    Gomes MM, Reis JG, Carvalho RL, et al. Analysis of postural control and muscular performance in young and elderly women in different age groups. Braz J Phys Ther. 2015;19(1):1–9.PubMedCentralCrossRefPubMedGoogle Scholar
  25. 25.
    Shumway-Cook A, Woollacott MH. Motor control: translating research into clinical practice. Philadelphia: Lippincott Williams & Wilkins; 2007.Google Scholar
  26. 26.
    Forte R, Boreham CA, De Vito G, et al. Measures of static postural control moderate the association of strength and power with functional dynamic balance. Aging Clin Exp Res. 2014;26(6):645–53.CrossRefPubMedGoogle Scholar
  27. 27.
    Forte R, Pesce C, Leite JC, et al. Executive function moderates the role of muscular fitness in determining functional mobility in older adults. Aging Clin Exp Res. 2013;25(3):291–8.CrossRefPubMedGoogle Scholar
  28. 28.
    Lee DK, Kim GM, Ha SM, et al. Correlation of the Y-balance test with lower-limb strength of adult women. J Phys Ther Sci. 2014;26(5):641–3.PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    Kenny DA. Statistics for the social and behavioral sciences. London: Longman; 1987.Google Scholar
  30. 30.
    Vincent WJ. Statistics in kinesiology. Champaign: Human Kinetics; 1995.Google Scholar
  31. 31.
    Preacher KJ. Testing the significance of correlations. 2002. http://www.psychometrica.de/correlation.html. Accessed 1 Sept 2015.
  32. 32.
    Granacher U, Gollhofer A. Is there an association between variables of postural control and strength in prepubertal children? J Strength Cond Res. 2012;26(1):210–6.CrossRefPubMedGoogle Scholar
  33. 33.
    Ibrahim AI, Muaidi QI, Abdelsalam MS, et al. Association of postural balance and isometric muscle strength in early- and middle-school-age boys. J Manipulative Physiol Ther. 2013;36(9):633–43.CrossRefPubMedGoogle Scholar
  34. 34.
    Muehlbauer T, Besemer C, Wehrle A, et al. Relationship between strength, balance and mobility in children aged 7–10 years. Gait Posture. 2013;37(1):108–12.CrossRefPubMedGoogle Scholar
  35. 35.
    Granacher U, Gollhofer A. Is there an association between variables of postural control and strength in adolescents? J Strength Cond Res. 2011;25(6):1718–25.CrossRefPubMedGoogle Scholar
  36. 36.
    Teyhen DS, Shaffer SW, Lorenson CL, et al. Clinical measures associated with dynamic balance and functional movement. J Strength Cond Res. 2014;28(5):1272–83.CrossRefPubMedGoogle Scholar
  37. 37.
    Izquierdo M, Aguado X, Gonzalez R, et al. Maximal and explosive force production capacity and balance performance in men of different ages. Eur J Appl Physiol Occup Physiol. 1999;79(3):260–7.CrossRefPubMedGoogle Scholar
  38. 38.
    Katayama Y, Senda M, Hamada M, et al. Relationship between postural balance and knee and toe muscle power in young women. Acta Med Okayama. 2004;58(4):189–95.PubMedGoogle Scholar
  39. 39.
    McCurdy K, Langford G. The relationship between maximum unilateral squat strength and balance in young adult men and women. J Sports Sci Med. 2006;5(2):282–8.PubMedCentralPubMedGoogle Scholar
  40. 40.
    Oshita K, Yano S. Relationship between force fluctuation in the plantar flexor and sustainable time for single-leg standing. J Physiol Anthropol. 2010;29(3):89–93.CrossRefPubMedGoogle Scholar
  41. 41.
    Oshita K, Yano S. Association of force steadiness of plantar flexor muscles and postural sway during quiet standing by young adults. Percept Mot Skills. 2012;115(1):143–52.CrossRefPubMedGoogle Scholar
  42. 42.
    Piirainen JM, Avela J, Sippola N, et al. Age dependency of neuromuscular function and dynamic balance control. Eur J Sport Sci. 2010;10(1):69–79.CrossRefGoogle Scholar
  43. 43.
    Muehlbauer T, Gollhofer A, Granacher U. Association of balance, strength, and power measures in young adults. J Strength Cond Res. 2013;27(3):582–9.PubMedGoogle Scholar
  44. 44.
    Hesari AF, Maoud G, Ortakand SM, et al. The relationship between star excursion balance test and lower extremity strength, range of motion and anthropometric characteristics. Med Sportiva. 2013;17(1):24–8.Google Scholar
  45. 45.
    Muehlbauer T, Gollhofer A, Granacher U. Relationship between measures of balance and strength in middle-aged adults. J Strength Cond Res. 2012;26(9):2401–7.CrossRefPubMedGoogle Scholar
  46. 46.
    Holviala JH, Sallinen JM, Kraemer WJ, et al. Effects of strength training on muscle strength characteristics, functional capabilities, and balance in middle-aged and older women. J Strength Cond Res. 2006;20(2):336–44.PubMedGoogle Scholar
  47. 47.
    Iverson BD, Gossman MR, Shaddeau SA, et al. Balance performance, force production, and activity levels in noninstitutionalized men 60 to 90 years of age. Phys Ther. 1990;70(6):348–55.PubMedGoogle Scholar
  48. 48.
    Buchner DM, Larson EB, Wagner EH, et al. Evidence for a non-linear relationship between leg strength and gait speed. Age Ageing. 1996;25(5):386–91.CrossRefPubMedGoogle Scholar
  49. 49.
    Judge JO, Davis RB, Ounpuu S. Step length reductions in advanced age: the role of ankle and hip kinetics. J Gerontol A Biol Sci Med Sci. 1996;51(6):M303–12.CrossRefPubMedGoogle Scholar
  50. 50.
    Ringsberg K, Gerdhem P, Johansson J, et al. Is there a relationship between balance, gait performance and muscular strength in 75-year-old women? Age Ageing. 1999;28(3):289–93.CrossRefPubMedGoogle Scholar
  51. 51.
    Burnfield JM, Josephson KR, Powers CM, et al. The influence of lower extremity joint torque on gait characteristics in elderly men. Arch Phys Med Rehabil. 2000;81(9):1153–7.CrossRefPubMedGoogle Scholar
  52. 52.
    Suzuki T, Bean JF, Fielding RA. Muscle power of the ankle flexors predicts functional performance in community-dwelling older women. J Am Geriatr Soc. 2001;49(9):1161–7.CrossRefPubMedGoogle Scholar
  53. 53.
    Menz HB, Morris ME, Lord SR. Foot and ankle characteristics associated with impaired balance and functional ability in older people. J Gerontol A Biol Sci Med Sci. 2005;60(12):1546–52.CrossRefPubMedGoogle Scholar
  54. 54.
    Melzer I, Benjuya N, Kaplanski J, et al. Association between ankle muscle strength and limit of stability in older adults. Age Ageing. 2009;38(1):119–23.PubMedCentralCrossRefPubMedGoogle Scholar
  55. 55.
    Bouchard DR, Heroux M, Janssen I. Association between muscle mass, leg strength, and fat mass with physical function in older adults: influence of age and sex. J Aging Health. 2011;23(2):313–28.CrossRefPubMedGoogle Scholar
  56. 56.
    Shimada H, Tiedemann A, Lord SR, et al. Physical factors underlying the association between lower walking performance and falls in older people: a structural equation model. Arch Gerontol Geriatr. 2011;53(2):131–4.CrossRefPubMedGoogle Scholar
  57. 57.
    Spink MJ, Fotoohabadi MR, Wee E, et al. Foot and ankle strength, range of motion, posture, and deformity are associated with balance and functional ability in older adults. Arch Phys Med Rehabil. 2011;92(1):68–75.CrossRefPubMedGoogle Scholar
  58. 58.
    Marcus RL, Addison O, Dibble LE, et al. Intramuscular adipose tissue, sarcopenia, and mobility function in older individuals. J Aging Res. 2012;2012:629637.PubMedCentralCrossRefPubMedGoogle Scholar
  59. 59.
    Muehlbauer T, Besemer C, Wehrle A, et al. Relationship between strength, power and balance performance in seniors. Gerontology. 2012;58(6):504–12.CrossRefPubMedGoogle Scholar
  60. 60.
    Shin S, Valentine RJ, Evans EM, et al. Lower extremity muscle quality and gait variability in older adults. Age Ageing. 2012;41(5):595–9.CrossRefPubMedGoogle Scholar
  61. 61.
    Miyazaki J, Murata S, Horie J, et al. Lumbar lordosis angle (LLA) and leg strength predict walking ability in elderly males. Arch Gerontol Geriatr. 2013;56(1):141–7.CrossRefPubMedGoogle Scholar
  62. 62.
    Jenkins ND, Buckner SL, Bergstrom HC, et al. Reliability and relationships among handgrip strength, leg extensor strength and power, and balance in older men. Exp Gerontol. 2014;58:47–50.CrossRefPubMedGoogle Scholar
  63. 63.
    Pisciottano MV, Pinto SS, Szejnfeld VL, et al. The relationship between lean mass, muscle strength and physical ability in independent healthy elderly women from the community. J Nutr Health Aging. 2014;18(5):554–8.CrossRefPubMedGoogle Scholar
  64. 64.
    Callisaya ML, Blizzard L, Schmidt MD, et al. A population-based study of sensorimotor factors affecting gait in older people. Age Ageing. 2009;38(3):290–5.CrossRefPubMedGoogle Scholar
  65. 65.
    Tsang WW, Hui-Chan CW. Comparison of muscle torque, balance, and confidence in older tai chi and healthy adults. Med Sci Sports Exerc. 2005;37(2):280–9.CrossRefPubMedGoogle Scholar
  66. 66.
    Morita H, Olivier E, Baumgarten J, et al. Differential changes in corticospinal and Ia input to tibialis anterior and soleus motor neurones during voluntary contraction in man. Acta Physiol Scand. 2000;170(1):65–76.CrossRefPubMedGoogle Scholar
  67. 67.
    Taube W, Schubert M, Gruber M, et al. Direct corticospinal pathways contribute to neuromuscular control of perturbed stance. J Appl Physiol (1985). 2006;101(2):420–9.Google Scholar
  68. 68.
    Sackett DL, Straus SE, Richardson WS, et al. Evidence-based medicine: how to practice and teach EBM. Edinburgh: Churchill Livingstone; 2000.Google Scholar
  69. 69.
    Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62(10):1006–12.CrossRefPubMedGoogle Scholar
  70. 70.
    Kovacs AJ, Muhlbauer T, Shea CH. The coding and effector transfer of movement sequences. J Exp Psychol Hum Percept Perform. 2009;35(2):390–407.CrossRefPubMedGoogle Scholar
  71. 71.
    Muehlbauer T, Panzer S, Shea CH. The transfer of movement sequences: effects of decreased and increased load. Q J Exp Psychol. 2007;60(6):770–8.CrossRefGoogle Scholar
  72. 72.
    Hikosaka O, Nakahara H, Rand MK, et al. Parallel neural networks for learning sequential procedures. Trends Neurosci. 1999;22(10):464–71.CrossRefPubMedGoogle Scholar
  73. 73.
    Kressig RW, Beauchet O. Guidelines for clinical applications of spatio-temporal gait analysis in older adults. Aging Clin Exp Res. 2006;18(2):174–6.CrossRefPubMedGoogle Scholar
  74. 74.
    Bean JF, Kiely DK, Herman S, et al. The relationship between leg power and physical performance in mobility-limited older people. J Am Geriatr Soc. 2002;50(3):461–7.CrossRefPubMedGoogle Scholar

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© The Author(s) 2015

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Thomas Muehlbauer
    • 1
    Email author
  • Albert Gollhofer
    • 2
  • Urs Granacher
    • 1
  1. 1.Division of Training and Movement Sciences, Research Focus Cognition SciencesUniversity of PotsdamPotsdamGermany
  2. 2.Albert-Ludwigs-University Freiburg, Institute of Sport and Sport ScienceFreiburgGermany

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