Aging Clin Exp Res DOI 10.1007/s40520-014-0216-0

ORIGINAL ARTICLE

Measures of static postural control moderate the association of strength and power with functional dynamic balance Roberta Forte • Colin A. G. Boreham • Giuseppe De Vito • Massimiliano Ditroilo Caterina Pesce

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Received: 16 December 2013 / Accepted: 6 March 2014 Ó Springer International Publishing Switzerland 2014

Abstract Background and aims Age-related reductions in strength and power are considered to negatively impact balance control, but the existence of a direct association is still an issue of debate. This is possibly due to the fact that balance assessment is complex, reflects different underlying physiologic mechanisms and involves quantitative measurements of postural sway or timing of performance during balance tasks. The present study evaluated the moderator effect of static postural control on the association of power and strength with dynamic balance tasks. Methods Fifty-seven healthy 65–75 year old individuals performed tests of dynamic functional balance (walking speed under different conditions) and of strength, power and static postural control. Results and conclusions Dynamic balance performance (walking speed) was associated with lower limb strength and power, as well as postural control under conditions requiring postural adjustments (narrow surface walking r2 = 0.31, p \ 0.001). An interaction effect between

R. Forte  C. A. G. Boreham  G. De Vito Institute for Sport and Health, University College Dublin, Newstead Building, Belfield, Dublin 4, Dublin, Ireland R. Forte (&)  C. Pesce Department of Human Movement and Sport, Universita` degli Studi di Roma, ‘‘Foro Italico’’, Piazza L. De Bosis 15, 00135 Rome, Italy e-mail: [email protected] M. Ditroilo Department of Sport, Health and Exercise Science, University of Hull, Hull, UK

strength and static postural control was found with narrow surface walking and talking while walking (change of b 0.980, p \ 0.001 in strength for 1 SD improvements in static postural control for narrow walking, and b -0.730, p \ 0.01 in talking while walking). These results indicate that good static postural control facilitates the utilisation of lower limb strength to better perform complex, dynamic functional balance tasks. Practical implications for assessment and training are discussed. Keywords Strength  Power  Static postural balance  Dynamic functional balance  Aging

Introduction Balance is a complex function which is essential for the successful performance of a variety of daily activities. Balance is achieved when the projection of the center of mass (COM) is maintained within the base of support in so called ‘‘static’’ or ‘‘dynamic’’ conditions such as sitting and standing, or when moving from one position to another, as in walking [1]. In static balance, the center of mass falls within the base of support (BOS); for dynamic balance, the COM sometimes falls outside the BOS and the center of pressure from the ground needs to shift between the lower extremities to accommodate this change. Balance results from the integration of information coming from the proprioceptive, vestibular and vision systems. With advancing age, the progressive deterioration of such systems causes impairment in balance control and increases the risk of falling [2]. Balance is commonly assessed by quantifying sway length and velocity while standing on a static or moving force platform [3]. In clinical or field settings, where more

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functionally relevant measures may be required, balance is indirectly assessed by timing static or dynamic tasks of different complexity (e.g., one leg standing, gait, chair rise, timed up and go, stair climbing; [4]). Generally, older individuals with higher muscular strength and power are at lower risk of falling, and better perform functional balance tasks when compared to individuals with poorer muscular strength and power [5–9]. Therefore, strength and power may be considered contributory physiologic attributes to balance preservation in aging. On the other hand, findings from cross-sectional studies and strength training interventions do not support a direct association between muscular performance indicators (strength and power) and static or reactive postural control [4, 10–12]. These discrepancies, though possibly imputable to the characteristics of the observed population (age, gender and level of frailty [5, 13, 14]), may also be due to the different physiological requirements of the assessed tasks. Postural control, as a general measure reflective of sensory integration and functioning, may not be as directly dependent on muscular capacities (strength and power) as dynamic functional balance measures. A way forward in understanding the complex relationship between strength, power, postural control and dynamic functional balance, could be to verify whether strength and power jointly operate with static postural control in determining dynamic functional performance. In complex conditions where inconsistent relations are present and when more than one variable may determine an outcome, relationships may not be explained in terms of individual direct associations (i.e., simple correlation or simple regression) or of additive predictors (i.e., multiple regression). Rather, these may necessitate analysis in terms of interaction, with one variable moderating the predictive role of another. Therefore, the present study sought to investigate the relationship between dynamic functional balance, and muscular strength and power, by exploring whether static postural control moderates the association of strength and power with dynamic balance tasks. The findings were expected to partially replicate previous evidence on the association between strength, power, postural control and dynamic balance performance [7, 12, 15]. In addition, the study attempts to identify factors independently associated with dynamic balance performance by testing whether the role of strength or power in dynamic balance tasks varies as a function of the individuals’ level of postural control. Moderation analysis allows verification of the potential role the moderator may have on a specific determinant and observation of the relationship between a predictor and a dependent in terms of levels of a moderator [16, 17]. Levels of moderation may be enhancing, buffering or

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antagonistic. In our case it was hypothesized that effective postural control might either amplify the muscular fitnessdynamic balance relationship, or buffer low levels of muscular fitness, thus weakening this relationship.

Methods Recruitment and participants Following approval from the local Ethics Committee, 57 healthy men and women (24 men: age 70.0 ± 3.3 years; height 173 ± 0.5 cm; weight 81.2 ± 9.6 kg; BMI 27.1 ± 2.5 kg/m2; 33 women: age 69.0 ± 3.3 years; height 163 ± 0.6 cm; weight 67.05 ± 8.7 kg; BMI 25.6 ± 3.7 kg/m2) participated in the study. They were recruited from the local community via parish newsletter, general practitioners lists and a consumer’s list database. Inclusion criteria were: age (65–75 years), lack of structured physical exercise more than once per week, no history of falling in the previous 2 years, no musculoskeletal or neurological diseases or any other medical condition potentially affecting study outcomes (i.e., absence of severe arthritis, cardiac illness, history of cerebro-vascular disease, uncontrolled metabolic disease) verified through medical history questionnaire [18]. Testing procedure Following consent procedures, participants underwent tests of lower limb muscular strength and power, postural control, and dynamic balance. All tests were performed on the same day. Rest periods of 1–2 min between trials were given as previously described [19–21]. Muscle strength, power and postural control Muscle strength Knee extension torque (Nm) was measured on the dominant lower limb with four maximal voluntary contractions during consecutive knee extensions on an isokinetic dynamometer (Biodex System 3 Pro, Biodex Medica System Inc. NY) set at 60°s. Participants sat with stabilizing straps placed across the chest, over the hips and thigh, the knee aligned to the rotating axis of the dynamometer. Range of motion was set between 558 and 658 from the starting position (approximately 908 at the knee varying slightly depending on the level of extensibility of knee flexors and comfort of participant). Verbal encouragement was given throughout the test. Peak torque of the best extension was calculated and used for analysis [22].

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Muscle power

not included in the subsequent analysis to exclude potential excessive sway due to adaptation to the position.

Muscle power was measured through a countermovement jump on a force platform (AMTI’s BP400600-2000, Advanced Mechanical Technology, Inc. MA, USA). From an upright posture with feet shoulder width apart and hands on hips, participants performed a vertical jump by rapidly bending and extending their knees. They performed three jumps, with 1 min rest in between [19]. The force signals were collected at a rate of 1,000 Hz. The vertical component (z) of ground reaction forces applied to the top surface of the plate was measured by strain gauges attached to load cells placed near the four corners of the platform. The signal was amplified using a 6 channel strain gauges amplifier (AMTI Mini Amp MSA-6; Advanced Mechanical Technology, Inc. MA, USA) connected through a RS232 cable to a PC where movement data files were stored and subsequently retrieved and analyzed. Briefly, explosive power was calculated multiplying the vertical force (F) by the vertical velocity (v). The vertical force was expressed as: F = M (g ? a) where M is the mass of the participant, g the acceleration of gravity and a the acceleration imposed by the muscle contraction to the body center of gravity (a = (F/M) - g). The vertical velocity (v) of center of gravity was obtained by time integration as previously R0 described [19]: v = ðF=M  gÞ dt. Peak power (PP) t

normalized for body mass was calculated as: PPkg = W/kg. The jump with the best peak power was used for analysis.

Dynamic functional balance Walking speed Walking speed was measured indoors over a course of 10 m (Smartspeed, Fusion Sport, Coopers Plains, Australia). Times were recorded to the nearest millisecond through an IPAQ computer and later transferred to a PC and transformed into m/s. Habitual and maximal walking speeds were assessed by asking participants to walk respectively at ‘‘the speed at which you would walk to the shops’’ and ‘‘as fast as possible without running’’. Subsequently, only maximal walking pace was also measured, adapting from ShumwayCook et al. [21], under a series of conditions mimicking daily life: 15 cm walk The walking width was restricted to 15 cm and the participants were required to keep their feet within the marked lines. If they stepped out, the measurement was repeated. Talking The participants were asked to name as many animals as they could while walking beginning with either letter B or the letter C. Only a few seconds were allowed to think about names before starting.

Postural control Picking up Control was assessed on a force platform (AMTI’s BP400600-2000, Advanced Mechanical Technology, Inc. MA, USA) using two positions of the feet: Romberg and Tandem. The subjects stood barefoot with arms by their sides for 30 s, feet hip width apart in the Romberg and feet aligned heel to toe with dominant foot behind in the Tandem. The Romberg position was performed under open and closed eyes conditions, the Tandem position only with open eyes. During the open-eyes trials subjects focused on a target placed two meters in front of them. Centre of pressure (CoP) data was sampled at a rate of 100 Hz for 30 s and then low pass filtered at 10 Hz to reduce noise. To assess body sway, the most commonly used CoP parameters were subsequently calculated: sway length (mm) and sway velocity (mm/s) [20, 23]. The tests, administered in a randomized order, were repeated twice with 1 min rest in between. The best test, i.e. the one with the shortest sway length, was used for statistical analysis. The initial 10 s of the recording were

Two hand weights of 250 g each were placed at 2 and 4 m from the first timing gate at about 50 cm distance from the mid line of the track. Participants were asked to pick them up as they went. Carrying a package The participant was asked to walk holding an empty cardboard box of dimension 31 9 22 9 22 cm and of an approximate weight of 200 g with arms straight down, so that the box was in front of their thighs and covering their feet, blocking vision. Stepping over obstacles Participants were asked to step with their preferred foot over two plastic hurdles (SAQ international UK) of 45 cm width and respectively of 15 and 45 cm height placed in

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succession on the midline of the track at 2 and 4 m distance from the first timing gate. Each walk, except the talking one, was repeated twice. Habitual walking speed was averaged while the best time of the maximal walks was used for the subsequent analyses. Test retest reliability (4-week interval) yielded intraclass correlation values [ 0.80 for all walks except 15 cm ([0.70) and talking (.59).

the individual parameters. In case an interaction significantly predicted an outcome, post hoc analyses were performed using simple slope testing [17]. A level of significance of p \ 0.05 was used for all statistical computations performed using PASW version 18.

Statistical analysis

Descriptive statistics (mean and standard deviation) of dynamic functional balance, muscular strength, power, and postural control are reported in Table 1. Significant differences between genders were observed for KETor, PPkg, RCE, walking on 15 cm base, walking and picking up and walking negotiating hurdles. Bivariate correlation coefficients are shown in Table 2, while the results of multiple regression analyses and of moderated prediction are reported in Tables 3, 4 and Fig. 1 respectively. Regarding the association between measures of postural control, strength or power, a significant correlation was observed between sway velocity in the Romberg closed eyes condition and both strength and power measures (KETor vs. RCEvel = 0.335 p = 0.05; PPkg vs. RCEvel = 0.321 p = 0.05), indicating that the stronger and more powerful individuals were also those with higher sway velocity. No other associations could be observed.

From the strength, power, and postural control assessments, the following variables were used for statistical analysis: peak knee extension torque (KETor), lower limb peak power normalized for body weight (PPkg), body sway velocity and length in Romberg, both with open (ROEVel, ROELength) and closed eyes (RCEVel, RCELength), and Tandem (TandVel, TandLenght) positions. Gender differences were preliminarily investigated comparing data with independent Student’s t-tests. In addition, before performing main analyses, the relationships between measures of muscular strength, power and postural control and dynamic functional balance were verified using bivariate correlation coefficients (r). Then, multiple regression analyses with backward elimination were performed to predict dynamic functional balance measures using strength, power and postural control parameters as independent variables and gender as a control variable. As sway length and velocity parameters of postural control are highly correlated, to avoid collinearity only the sway velocity variables were used (ROEVel, RCEVel, TandVel). Moreover, sway length is reported to be affected by the duration of the trial and sway velocity displays a higher level of reliability [20]. Further regressions using strength/power, postural control and gender as component factors for moderation analysis, were run to verify whether postural control moderates the effect of muscular predictors taking gender into account. To this aim interaction variables were created multiplying strength (KETor) and power (PPkg) by gender and by the postural control variable (RCEVel) which had been shown to be an individual predictor of dynamic balance and sensitive to gender in the previous analyses. Then, the dynamic functional balance performances were submitted to separate three-way multivariate hierarchical regressions. Predictors were entered in a first block individually (strength/power, postural control, gender), as twoway interaction terms (e.g. KETor 9 RCE) in a second block and as three-way interaction terms (e.g. KETor 9 RCE 9 Gender) in a third block. The aim of using this hierarchical regression model was to evaluate the role of postural control and/or gender as moderators only after controlling for the individual prediction accrued by

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Results

Table 1 Mean ± standard deviation of muscular strength, power, postural control and dynamic balance performances of participants. (WS = walking speed; m/s) All (n = 57)

Men (n = 24)

Women (n = 33)

Peak power (W/kg)

23.83 ± 4.16

28.2 ± 2.9***

21.7 ± 2.99

Knee extensors torque (Nm)

94.71 ± 29.15

119.5 ± 31.6***

83.0 ± 20.2

Sway velocity Romberg open eyes (mm/s)

8.51 ± 2.34

Sway velocity Romberg closed eyes (mm/s)

11.09 ± 4.25

Sway velocity Tandem (mm/s)

30.52 ± 10.71

33.15 ± 11.2

29.7 ± 10.1

1.22 ± 0.25

1.33 ± 0.15

1.35 ± 0.19

Habitual WS

9.07 ± 2.74

8.3 ± 2.1

13.4 ± 5.3***

9.6 ± 2.4

Maximal WS

1.88 ± 0.23

1.91 ± 0.16

1.83 ± 0.24

15 cm WS

1.67 ± 0.26

1.77 ± 0.26*

1.60 ± 0.24

Picking up WS

1.29 ± 0.21

1.36 ± 0.20*

1.23 ± 0.20

Talking WS

1.63 ± 0.26

1.63 ± 0.30

1.63 ± 0.24

Box WS

1.94 ± 0.21

2.01 ± 0.20

1.89 ± 0.28

Hurdles WS

1.63 ± 0.22

1.74 ± 0.19*

1.55 ± 0.20

* Significant differences between men and women p \ 0.05 *** Significant differences between men and women p \ 0.001

Aging Clin Exp Res Table 2 Correlation (r) between dynamic balance measures (WS = walking speed) and muscular strength, power and sway velocity for postural control Habitual WS PPkg

Maximal WS

0.260*

0.275* 0.295**

15 cm WS

Picking up WS

0.371**

KETorque

0.255*

ROE velocity

0.033

-0.124

-0.200

RCE velocity

0.032

-0.101

Tand velocity

0.033

-0.042

0.418***

0.383**

Box WS

Hurdles WS

0.444***

0.469***

0.508***

0.383**

0.008

-0.068

-0.161

0.135

-0.074

-0.169

0.448***

Talking WS 0.100 0.204

0.063

-0.151

-0.021

0.155

-0.131

0.035

-0.099

-0.152

PPkg peak power normalised for body weight, KE knee extensors torque, ROE romberg open eyes position, RCE romberg closed eyes position, Tand tandem position * p \ 0.05 ** p \ 0.01 *** p \ 0.001

Table 3 Summary of results of the multiple regression models exploring the prediction of dynamic functional mobility individually accrued by muscular and static postural control factors. Total R2 explained by the model, ANOVA results and standardized b coefficients with level of significance are also reported. Only significant models are reported 2

Table 4 Results of hierarchical regression models testing moderated prediction of dynamic functional mobility. Total R2 explained by the final model, ANOVA results and standardized b coefficients with level of significance are also reported. Only significant models are reported 15 cm walking speed

R = 0.12 b

F1,55 = 7.67, p = 0.008 P

Model

Knee extensors torque

0.350

0.008

1

15 cm walking speed

2

Maximal walking speed

R = 0.31 b

F3,55 = 7.89, p \ 0.001 P

-0.371

0.004

Peak power

0.336

0.016

Knee extensors torque

0.333

0.015

2

F3,56 = 8.16, p \ 0.001 P

RCE sway velocity

Picking up walking speed

Knee extensors torque Peak power Tandem sway velocity

R = 0.32 b 0.390

0.005

0.229

0.086

-0.225

0.056

R2 = 0.31 b

F2,54 = 12.1, p \ 0.001 P

Peak power

0.379

0.005

Knee extensors torque

0.262

0.049

Hurdles walking speed

Box walking speed

R2 = 0.28 b

F3,56 = 6.8, p = 0.001 P

Peak power

0.396

Knee extensors torque

0.259

0.062

-0.230

0.074

RCE sway velocity

0.006

RCE romberg closed eyes position

With regard to the relationship between dynamic functional balance, postural control, strength and power (Table 2), all of the dynamic functional balance performances except

b Knee extensors torque RCE sway velocitys

2

Knee extensors torque

0.472

0.001

-0.303

0.020

0.595

0.001

-0.282

0.017

Knee extensors torque 9 RCE

-0.385

0.001

R2 = 0.20 b

Model Knee extensors torque RCE sway velocity 2

F3,56 = 10.39, p \ 0.001 P

RCE sway velocity

Talking while walking speed 1

R2 = 0.37

Knee extensors torque

F3,55 = 4.22, p = 0.010 P

0.226

0.114

-0.177

0.212

0.352

0.013

RCE

-0.157

0.236

Knee extensors torque 9 RCE

-0.396

0.004

RCE romberg closed eyes position

talking while walking, significantly correlated with both strength and power but with none of the postural control measures. Significant results of the multiple regression analyses performed on dynamic functional balance performances are reported in Table 3. Muscular power was significantly associated with walking on a narrow base, negotiating obstacles, carrying a box, all of which are complex conditions not requiring deceleration. In addition, postural control was independently associated with walking on a narrow base, picking up or carrying a box which are dynamic balance tasks requiring postural adjustments.

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between individuals with low or high knee extensors torque was observed.

15cm Walking speed

2 1.5 1 0.5 0 -0.5

Discussion Low KETor

Mean KETor High KETor

-1 -1.5 Low RCE Mean RCE High RCE

-2 -2.5

Talk Walking speed

1.5 1 0.5 0 -0.5 Low RCE Mean RCE

-1 -1.5 Low KETor

Mean KETor

High KETor

High RCE

Fig. 1 Results of simple slope tests showing the prediction of walking speed (std values) accrued by muscular strength, moderated by postural control. Solid lines represent the change in the slope of the muscular strength parameter for high vs low levels (1SD change) of postural control; (b) and its significance are reported. In both types of walking muscular strength is predictive in the case of good postural control (KETor = knee extensors torque; RCE = romberg closed eyes)

For habitual walking speed and talking while walking the regression model did not return any predictor and results are not reported. VIF computation revealed no collinearity associated with all individual predictors (VIF values range 1.2–2.8). Regarding the results of the moderated prediction, none of the two-way or three-way interactions with gender were significant. Only the strength 9 postural control interaction (KETor 9 RCE) was a significant predictor of speed in the 15 cm walk and talking while walking task (Table 4) and was therefore further analysed with simple slope tests. This required regressing the dependent variable (dynamic functional balance) on the independent predictor (strength, power) and its interaction with the moderator (postural control) with two separate computations respectively at -1 SD and at ?1 SD of the moderator. This allowed the testing of whether the relationship between strength/power and dynamic balance differed for values of postural control below and above the mean. As shown in Fig. 1a, b, results indicated that knee extensor torque is a significant predictor of walking speed only in individuals with good postural control (low RCE sway velocity). With poor postural control (high RCE sway velocity), no significant difference in walking speed

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The present study investigated whether and how relevant muscular and motor functions are related to activities involving functional mobility, as defined by the ICF [24]. Specifically, the association between static postural control, muscular strength and power and measures of dynamic functional balance in healthy, older individuals was investigated. The evidence regarding such relationships is currently controversial [25], possibly due to the complexity of balance control and the variety of tasks assessed. On the one hand, weaker, frailer individuals usually show poorer performances in dynamic balance tests and are at greater risk of falling, suggesting a link between balance control and muscular capacities [6]. However, when balance is measured as postural sway, no relationship with strength and power emerges [12]. According to the present findings, such divergences may be reconciled by considering the demands of the dynamic functional balance tasks and the type of analysis performed to study the relationship, moving toward a moderated prediction model. When relating strength and power with static or dynamic postural control, these appear to be independent from each other [12, 26]. The correlational analysis performed in the present study generally confirmed the association of strength/power with dynamic functional balance, and the independence of muscular functions from several measures of static postural control and of static postural control from dynamic functional balance. This could be explained by the different demands on muscular capacities and sensorymotor integration in static postural and dynamic functional balance. One unexpected positive association was observed between measures of strength/power and sway length in the closed eyes condition, implying that the stronger and more powerful the individual, the worse the postural control and the greater the sway (Table 2). Some authors postulate that in older individuals, more swaying may actually be a compensatory mechanism to obtain more information about body orientation from the sensory system [27]. This might particularly hold true for men, whose more pronounced impairment in peripheral sensation in aging has been suggested to explain their higher swaying [28] also found in the present study (Table 1). This finding contradicts the general assumption that better balance control, at least in relation to postural control, is associated with higher levels of strength and power. While its interpretation is difficult, attention should be given not to relating quantitative sway measures with muscle strength and

Aging Clin Exp Res

power, but rather to considering quantitative sway measures as determinants of functional mobility performances along with strength and power. There is considerable evidence that where dynamic functional balance is concerned (i.e., stair climbing, timed up and go), strength and power do play a role along with other physical functions depending on the complexity of task demands [7, 13, 29–31]. In line with these data, in the present study, correlational and multiple regression analyses showed that muscular strength and power are positively associated with all walking measures (Table 2) and seem to be determinants of most of the locomotor dynamic balance performances (Table 3). In particular, muscular power was associated with complex dynamic functional balance in actions requiring maintained speed (e.g. walking on narrow base, negotiating hurdles and carrying a box). Conversely, strength seemed a stronger determinant than power for simple maximal walking speed and when the requested action incorporated deceleration, as in the walking while picking up task. The main contribution of this study, however, is the information provided by the interaction between muscular and postural control in determining dynamic functional balance performances. When physical environment requirements or cognitive challenges are added to the basic walking task (i.e., walking on a narrow path or performing a walk-talk dual task (Fig. 1 a, b), older individuals seem to be able to capitalize on their muscular strength for fast walking under reduced-space or dual task conditions only if they have a sufficiently high postural control (i.e. low sway in the Romberg closed eyes position). Therefore, postural control, particularly that deriving from vestibular and proprioceptive information processing, appears to have an amplifying effect for the translation of strength prerequisites into dynamic balance performance. Previous research has in fact shown that proprioception is a fundamental source of sensory feedback particularly for the successful performance of dynamic tasks such as walking, chair standing and stair climbing in the elderly [32]. The present results additionally indicate that muscular strength and postural control are interactive determinants of dynamic functional balance in older individuals, particularly when locomotor tasks are rendered more difficult by reduced width of the surface or dual tasking. Movement control adaptations and dual tasking strategies in aging may help explain this finding. When both speed and accuracy are needed to walk at maximal speed on a narrow surface (15 cm walking task) the elderly slowdown to ensure accuracy (e.g., Fitts’ law, speed-accuracy trade-off). To accomplish control of movement in such conditions and properly regulate the amount of strength required to perform the task, older individuals seem to rely more on the capacity to process ‘‘online’’ information

coming from the sensory systems than from central processing [33]. The present results suggest that individuals with a better capacity to process somatosensory and vestibular information can make better use of their strength and walk faster. In relation to dual task situations such as walking and talking, it is known that the two tasks compete for the same limited central processing resources, as demonstrated by deterioration in performance of one or both tasks (capacity sharing theory see [34]). In aging, worsening of walking performance under dual task conditions, as demonstrated in the present study (1.63 vs. 1.88 m/s walking speed in the dual and single task conditions, respectively, Table 1), supports the notion that the control of walking is no longer automatic, but requires attentional resources to compensate for age-related deteriorations. It has been recently demonstrated that tasks needing adaptations to environmental challenges while walking require the interplay of physiological and cognitive resources more than walking under less difficult conditions [21, 35]. In particular growing evidence suggests that cognitive executive functions play a role in dynamic balance performance measures such as gait speed [34, 36]. Though the main aim of the present study was not to explore the role of cognitive functioning for gait performance, results of the present interaction add to this notion, suggesting that in walk-talk dual tasks, having better postural control may represent a mechanism to limit cognitive resource allocation to the functional balance task, allowing a better sharing of limited-capacity resources between tasks and a useful utilization of strength for walking. Although gender differences were observed in both dynamic functional balance performances and their muscular and postural control determinants (Table 1), they did not influence the relationship between the investigated functions and activities. Both men and women could capitalize on strength to efficiently perform dynamic balance activities only if they had a sufficient level of postural control. Clearly, the well-known higher absolute amount of muscle in men with respect to women, is associated with higher strength and power capacities [37] and the latter, in turn, with faster walking. This was presently confirmed by the association between muscle strength/power and performances on complex dynamic functional balance tasks such as walking on a narrow surface, or picking up or negotiating hurdles. On the other hand, the absence of gender differences in motor cognitive dual-tasking (walking while talking) can be speculatively interpreted as an indicator of the lower relevance for this task of muscular strength, possibly in favour of cognitive factors. Limitations should be addressed. The present findings were observed in a healthy population of a relatively narrow age range, which may explain the lack of correlations

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and the moderation of strength effects by control in the more basic performances. These conditions were possibly not sufficiently challenging for the present population and a more relevant role of postural control might be found in future research with weaker/frailer populations. It is not therefore possible to generalize results to the wider older population. Moreover, the present findings may be limited by the lack of dynamic postural measures which may have been more appropriate for studying the relationship with dynamic balance tests. In conclusion, the present findings have practical implications for dynamic balance assessment and training in healthy older individuals. They suggest that dynamic balance at an older age should be considered as a multifaceted ability, the prerequisites of which vary as a function of task complexity. Muscular strength and power are overall determinants of erect standing and walking. However, postural control mechanisms seem to play a relevant role, not in ‘basic’ walking tasks simply requiring speed of movement, but in more complex locomotor tasks also requiring acceleration/deceleration, control of modified postures and allocation of resources on concurrent mental tasks. The moderating influence of static postural control on the muscular determinants of dynamic functional balance, suggests that the latter is an activity that is influenced by different types of static and dynamic postural impairments. Thus, it appears important to emphasise exercises for strength and power along with exercises which activate balance control mechanisms of proprioceptive and vestibular origin. Acknowledgments This research was carried out with the support of IRCSET ‘‘Irish Research Council for Science, Engineering and Technology’’. The authors wish to thank Sheena Murphy and Josianne Rodriguez Krause for their help during testing sessions. On behalf of all authors the corresponding author declares the absence of any financial relationship with the organization that sponsored the research. She also declares the authors have full control of all primary data and allow the journal to review our data if requested. Conflict of interest

None.

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