Does instructing attentional focus direction affect static single leg balance performance?

Abstract

Balance and postural control exercises are generally included as a part of exercise programs, during which movement practitioners can provide instructions to facilitate the performance of motor skills. Instructions can be used as cues to direct attentional focus, which has been found to affect the performance of motor skills, including balance and postural control tasks. However, no known studies to date have investigated the effect of both internal and external attentional focus instructions on static single leg balance performance, and it seems unclear whether effects of such instructions are related specifically to the direction of attention. The purpose of this study was to investigate the effect of instructing the direction of attentional focus on single leg static balance performance as reflected by the complexity of the center of pressure (COP) profile. Participants (N = 46) between the ages of 19–28 years old were randomly assigned to one of three group conditions: internal focus (INT_n=15), external focus (EXT_n=16) and control (CON_n=15). Participants performed a thirty-five second static single leg balance task. Outcome measures were the scaling exponent determined from a detrended fluctuation analysis (DFA) to infer complexity of the COP profile in the anterior-posterior (AP) and medial-lateral (ML) directions, and root mean square error (RMSE) of the COP profile in AP and ML directions. A one-way analysis of variance (ANOVA) determined there were no statistically significant differences in the measured variables among groups. The results do not support the claim that manipulating the direction of attentional focus affects static single leg balance performance.

Keywords

Dynamical systems theory motor skill physical fitness postural control

Introduction

Among the many facets of physical fitness that are popularly trained in fitness programs are balance and postural control.^1,2 Balance and postural control are related in so far as balance is a multidimensional concept referring to the ability of a person not to fall, and postural control is the act of maintaining, achieving or restoring a state of balance during any posture or activity.³ When balance and postural control training are included in a multifaceted fitness program, it can provide injury-prevention benefits such as reduction in the occurrence of both ankle and knee injuries in athletes.⁴ Balance and postural control training is prevalent in sport and therapeutic settings^5,6 - environments in which movement practitioners can provide instructions to learners. An important aspect of instructions is the attentional focus that they facilitate.^7,8

Two types of attentional focus that have been well-researched have been labeled as “external” and “internal” attentional focus.⁸ External focus is typically defined as consciously attending to details outside of the body, often regarding performance outcome(s), whereas internal focus is defined as consciously attending to details within the body, often regarding the movement process.^7,8 Although performance is often greater under external focus compared to internal focus conditions in a variety of tasks,⁸ it does not seem to be the case that adopting an internal focus is always detrimental with respect to performance outcomes compared to adopting an external focus; the usefulness of adopting an internal focus might be specific to the skill level of the performer,⁹ the task¹⁰ and the nature of the internal focus.^11,12

There seems to be a subtle ambiguity in how the terms internal attentional focus and external attentional focus are used. Nideffer (1976) originally characterized attentional focus with a two-dimensional classification system: width and direction. According to Nideffer (1976), an internal focus of attention involves directing attention to one's own body, actions and/or thoughts, and an external focus involves directing attention to information arising from the environment, typically related to the performance of some task. In much of the research investigating the effects of the direction of attentional focus on the performance of motor skills⁸ the definition of an external focus of attention emphasizes focusing on information pertaining to the outcome of an action, and the definition of an internal focus of attention emphasizes focusing on the movement process. This definition is not always reflected in the instructions used in attentional focus research; instructions sometimes instead reflect Nideffer's (1976) characterization of direction of attentional focus. For example, Wulf et al. (2007)³⁹ investigated the effects of the direction of attentional focus on jump-and-reach performance where the external focus group was instructed to focus on reaching for the rungs of the apparatus, and the internal focus group was instructed to focus on their fingertips. Wulf et al. (2001)⁴⁰ characterized internal focus during performance of the stabilometer test as focusing on one's feet, while an external focus was characterized as focusing on markers placed on the stabilometer platform. These two studies reflect Nideffer's (1976)⁴¹ characterization of attentional focus. However, Wulf et al. (1998) during performance of a ski simulator, had participants either focus on putting force through the outsides of the feet (internal focus) or focusing on the force put into the wheels of the apparatus (external focus) which more reflects the process and outcome definition of internal and external focus, respectively. A lack of a consistent, clear definition and application of these terms could result in ambiguous interpretations of the role of attentional focus instructions, which could make it difficult for evidence-based practitioners to use research findings to guide their instructional strategies. As long as task performance outcome-related information is located externally, the characterizations of external and internal focus as described by Wulf (2013) and by Nideffer (1976) do not conflict very much. However, when performance outcome related information is located internally (related to one's body), for example during taekwondo routines¹⁰ and swimming emphasizing movement form,¹² the proposition that an “internal” focus of attention (focusing on one's body) causes performance decrements does not seem to hold. During tasks such as that used in Komar and Chow (2013) where the outcome being measured is movement form, differentiating internal and external focus as process and outcome focused attention, respectively, is not straight forward; sometimes the outcome is the process. Similarly, static standing balance tasks with the goal of “standing still” implies a relationship between process and outcome. As such, it is not clear whether the direction of attentional focus as described by Nideffer (1976) in general affects performance of perceptual-motor skills, and it therefore seems reasonable to ask whether attention on one's body, compared to attention on one's environment, affects static balance performance. In this light, it is worth considering the comments by Yi Cheng Peh et al. (2011) that the labeling of attentional focus instructions as distinctly “internal” and “external” might hinder the development of a deeper understanding of the effects and usefulness of attentional focus instructions; as Davids et al. (2008)⁴² discuss from the dynamical systems perspective, perhaps a characterization of attentional focus as focusing on movement dynamics (form) and movement effects (outcome) would be useful. In the present study, the decision was made to operationalize the terms internal focus and external focus in line with Nideffer (1976) as described above to determine whether performance of static single leg balance is specifically affected by the direction of attentional focus.

In studies investigating the effects attentional focus on dynamic (balance tasks in which the feet or surface is moving) balance task performance, results seem to suggest that external focus instructions are superior to internal focus instructions.¹³ The effects attentional focus manipulations on static standing postural control tasks (standing on a solid surface with feet stationary) have been studied by imposing secondary task demands,^14–16 however few studies have examined the effects of internal and external attentional focus instructions on static postural control tasks and results tend to vary.¹³ The purpose of this study was to investigate the following research question: does instructing the direction of attentional focus affect static single leg balance performance as reflected by the complexity of the COP time series?

Methods

Participants

A power analysis was conducted using G*POWER 3.1 (Universitat Kiel, Germany) which determined that 45 participants divided into three groups of fifteen would yield a power of .80 for an effect size of 0.5 with α set at 0.05. Forty-nine volunteers participated in this study; however, data from 46 participants were analyzed. Three participants did not follow directions during testing therefore their data were excluded from analysis. All participants in the study provided written informed consent, and all procedures were approved by the university Institutional Review Board for research involving human participants.

Participants (26 female, 20 male) were age 19–28 years and self-reported no trouble with vision and no eyeglasses, no trouble with dizziness, no pain and/or painful movement limitations, no physical condition(s) that may affect their balance and/or posture, and were not participating in any other balance- or postural control-related research at the time of the study. Participants had a body mass index (BMI) score less than 30. Height and weight (used for calculation of BMI) were reported by the participant. BMI categories were determined according to guidelines from the American College of Sports Medicine.¹⁷

Protocol

Participants were randomly assigned to one of three groups - an internal focus group (INT), an external focus group (EXT), or a control group (CON). Participants were scheduled for a single test date and time in the evening, and were asked not to participate in any exercise that day. On each day of testing, at the beginning of the first test session and at the end of each subsequent test session, the force platform was sanitized.

Participants were informed about the test procedure and specific details about the task they were to complete via written directions and a picture example of the posture they were asked to assume (Figure 1, 2 & 3). The balance task was based on the task employed in the study by Kee et al. (2012) Participants read the instructions while sitting at a desk. They were asked to balance barefoot on their non-preferred leg for one trial of thirty-five seconds duration on a force platform. Consistent with established testing procedures detailed in existing literature,¹⁸ the preferred limb was defined as the leg indicated by the participant in response to the following question: “if you would kick a ball at a target, which leg would you use to kick the ball?” The participants were instructed to stand on the leg that they would not use to kick the ball. Participants stood on a visible line on the force plate (parallel to the y-axis of the force plate coordinate system, corresponding to the A-P direction) such that the line on the force plate bisected the calcaneus and passed under the base of the second metatarsal (Figure 1 and 2), as described by Promisri et al. (2018). Participants were asked to stand barefoot to eliminate the influence of footwear on postural control in consideration of previous findings that have found different insoles to affect postural control.¹⁹ The participants were standing on the force platform facing away from the experimenter.

Figure 1.

Test position.

Figure 2.

Position for standing on left foot.

Figure 3.

Position for standing on right foot.

All instructions were identical among groups with the exception of the attentional focus instructions. For the purpose of directing attentional focus internally and imposing a counting secondary task demand, the INT group was instructed to “stand as still as you can, pay attention to your heart beat and try to count the number of times your heart beats during the balance task.” For the purpose of directing attentional focus externally and imposing a counting secondary task demand, the EXT group watched a video of a cartoon²⁰ that was played during the balance trial and the participants were instructed to “stand as still as you can, watch the cartoon and count the number of times the cartoon switches scenes.” The CON group was instructed to “stand as still as you can.” These experimental group instructions were chosen to attempt to manipulate the direction of attentional focus without appreciable differences in how the instructions relate to the task constraints between the two groups.

The flat screen television (SANYO Manufacturing Corp., DP26640) on which the cartoon was displayed was placed on a television cart that was 1.37 meters in height as measured from the floor to the top of the surface of the cart. The television set-up as described above was present for all three conditions, but the television was turned off during the INT and CON conditions.

The first ten seconds of data was not used in data analysis, as Kee et al. (2012) noted that due to the challenging nature of this task, large initial amplitudes of body movement tend to occur in this initial ten-second period as participants attempt to establish balance. Therefore, the final twenty-five seconds of data were considered the most suitable for assessing the sustained efforts in postural control. Participants who broke form were omitted from the analysis. Breaking form was defined as either uncrossing the arms from the chest, or losing contact between the foot and the back of the knee. This was visually determined by the experimenter. Data were not used if participants lost their balance at any point during the trial, or if instructions were not followed properly regarding the form that participants were asked to use.

Forces and moments (F_x, F_y, F_z and M_x, M_y M_z, respectively) were recorded by a force plate (Bertec Corporation, K00606 Type 4060-10), which was calibrated two months prior to the beginning of data collection. The sampling frequency was 100 Hz, because findings from Giovanini et al. (2017)⁴³ who conducted analyses of the structure of fluctuations in COP time series, suggest a sampling frequency of 100 Hz to record COP trajectories. Ruhe et al. (2010)⁴⁴ also recommended a sampling frequency of 100Hz for COP data collection for analyses of postural control. A time series of the center of pressure (COP) in the anterior-posterior (A-P) and medial-lateral (M-L) directions was derived using built-in software (Contemplas professional motion analysis software, TEMPLO 2016.1.404).

Statistical analysis

The following calculations were carried out using Matlab software (MathWorks, Inc., 2018b). The initial ten seconds of the data was not used in the data analysis to allow for initial adjustments to the balance task, as in the study by Kee et al. (2012), who utilized the same balance task as the one used in this study. Therefore, twenty-five seconds of COP data was used for analysis.

The M-L and A-P COP time series data were separately filtered with a dual pass, 2^nd order, 10 Hz low pass Butterworth filter as used by Giovanni et al. (2017).

The amount of variability was determined from the root mean square error (RMSE), calculated for the M-L and A-P directions, as follows:

R M S E = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(x_{i} - \bar{x})}^{2}}

and

R M S E = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(y_{i} - \bar{y})}^{2}}

where N is the number of data points, x_i is i^th data point in the M-L component of the COP time series, y_i is the i^th data point in the A-P component, and

\bar{x}, \bar{y}

are the means of the M-L and A-P COP series, respectively.

Complexity of the COP dynamics in both the M-L and A-P directions was assessed using detrended fluctuation analysis (DFA). DFA determines the fractal dimension of a time series and is relatively robust to non-stationarities in the time series.²¹ The scaling exponent, α, calculated from the DFA reveals the correlation properties of the signal across different time scales, which reflects the complexity of the time series.

The process is as follows:

First, the N-point time series {z_t, t = 1, …, N} is centered at zero mean and cumulatively summed to obtain the integrated time series, as follows:

Z (t) = \sum_{u = 1}^{t} (z_{u} - z)

where

z = \frac{1}{N} \sum_{t = 1}^{N} z_{t}

is the global mean.

This series is then divided into a number of non-overlapping windows with an equal number, w, of data points. Hence, there are N/w windows. The size of the windows will range from 10 data points to N/4 data points, as suggested by Peng et al. (1995)⁴⁴.

Within each window, the series Z(t) is detrended by a linear least square fit, ẑ(t). Then the detrended fluctuation parameter, F(w), is computed as

F (w) = \sqrt{\frac{1}{N} \sum_{t = 1}^{N} {[Z (t) - \hat{z} (t)]}^{2}}

where

\hat{z} (t)

is a piecewise continuous function composed of the local least-square fit lines in each window.

Because F(w) obeys a power-law function such that $F (w) \propto w^{a}$ , the scaling exponent α is obtained from the slope of the linear regression of a log-log plot of F(w) over w.

When α = 1.0, the series is considered 1/f noise (pink noise) where f is frequency (the spectral power of the signal is inversely proportional to the frequency) and is maximally complex, while white noise (α = 0.5) and Brownian noise (α = 1.5) have lower or no complexity.^21–23 Moreover, α can be interpreted as the “roughness” of the series, with larger α reflecting “smoother” series than lower α.²³

The range of window sizes on which the slope of the log-log plot was evaluated was determined according to the process developed by the Center for Research in Human Movement Variability at the University of Nebraska at Omaha and used in Taylor (2015)⁴⁵. In short, a range of window sizes was determined appropriate if it performs “reasonably well” when used in a DFA analysis of one hundred samples of pink noise (with a known alpha value of 1) with the same number of data points as the collected COP data. “Reasonably well” is defined as meeting the following requirement: the ninety-five percent confidence interval of the mean alpha value calculated from the one-hundred random trials must contain the known alpha value of 1 for pink noise. In other words, if a chosen window size did not perform reasonably well on a set of data with a known alpha value, it was not determined as suitable for the analysis of the collected data.

Delignieres et al. (2011)⁴⁶ found that the log-log plot of COP position data did not show signs of the “cross-over” phenomenon, whereas COP velocity data did. The data collected in this study was COP position data, and therefore the range of window sizes was not chosen on the basis of location on the log-log plot, but based on the results of the statistical test described above.

For the purpose of determining whether there was a confounding relationship between the height of the participants and the (fixed) height of the television in the EXT group, a correlation analysis (Pearson r coefficient) was conducted between participant height data and scaling exponent data (both M-L and A-P) for all three groups (INT, EXT and CON) as well as for the pooled data of all groups.

The statistical analysis was performed with SPSS software (IBM Corp, Armonk, NY, version 25.0) The mean values of the root-mean-square-error (RMSE) and the scaling exponents calculated from the detrended fluctuation analysis (DFA) were each compared among groups (INT, EXT and CON) using a one-way analysis of variance (ANOVA). Data were inspected for normality and outliers. Q-Q plots were used to inspect the data for deviations from normality. Outliers were defined as data three or more standard deviations away from the mean, and if present were omitted from analysis. Levene's test of equality of error variances was used to determine if the assumption of homogeneity of variance was violated. Post-hoc tests with a Bonferroni correction were used to determine significant differences. Significance level was set to 0.05. The null hypotheses was rejected if the test statistic p-value <0.05.

Results

Data from 46 participants were included for analysis (external focus group n = 16, internal group n = 15, control group n = 15). Data was excluded from analysis for 3 participants because they did not successfully accomplish the task as described in the written instructions.

Anova

No statistically significant differences were found among groups for any of the dependent measures. The mean values and 95% confidence intervals for all measures and groups are provided in Table 2.

Table 1.

Participant demographics.

Age (years)	M = 20.6, SD = 1.68 years (males 20.7 ± 1.49 years; females 20.4 ± 1.85)
Height (meters)	M = 1.71, SD = 0.097 meters (males 1.79 ± 0.069; females 1.65 ± 0.066)
Mass	M = 74.0, SD = 11.79 kg (males 81.9 ± 8.98 kg; females 67.5 ± 9.70 kg)
BMI	M = 25.2, SD = 2.74 (males 25.6 ± 2.53; females 24.8 ± 2.53)

Table 2.

Descriptive statistics.

Dependent	EXT		INT		CON
Measure	Mean	95% CI	Mean	95% CI	Mean	95% CI
Scaling exponent M-L	1.1521	[1.0894,1.2147]	1.1726	[1.1078,1.2373]	1.1381	[1.0733, 1.2028]
Scaling exponent A-P	1.3149	[1.2314,1.3984]	1.3556	[1.2694,1.4419]	1.3411	[1.2549,1.4274]
RMSE M-L	0.0058	[0.0053,0.0062]	0.0053	[0.0048,0.0058]	0.0050	[0.0045,0.0055]
RMSE A-P	0.0069	[0.0059,0.0079]	0.0083	[0.0073,0.0093]	0.0070	[0.0060,0.0081]

Note. CI = confidence interval.

For the scaling exponent of the medial-lateral direction (M-L) component of the center of pressure (COP) time series data, Levene's test of equality of error variances was insignificant, F(2,43) = 0.359 (p = 0.7). The one-way ANOVA did not yield significant differences among groups, F(2,43) = 0.292 (p = .748). For the scaling exponent of the anterior-posterior direction (A-P) component of the COP time series data, Levene's test of equality of error variances was insignificant, F(2,43) = 2.289 (p = 0.114). The one-way ANOVA did not yield significant differences among groups, F(2,43) = 0.242 (p = .786).

For the root-mean-square-error (RMSE) of the M-L component of the COP time series data, Levene's test of equality of error variances was insignificant, F(2,43) = 1.601 (p = 0.214). The one-way ANOVA did not yield significant differences among groups, F(2,43) = 2.110 (p = .134). For the RMSE of the A-P component of the COP time series data, Levene's test of equality of error variances was insignificant, F(2,43) = 1.094 (p = 0.344). The one-way ANOVA did not yield significant differences among groups, F(2,43) = 2.296 (p = .113).

The results of the correlation analysis are presented in Table 3. The correlation coefficient between height and A-P scaling exponent data for the external focus group was statistically significant (r = −0.550, p = 0.026). All other correlation coefficients were not statistically significant.

Table 3.

Group	Pearson R (M-L scaling exponent and height)	Pearson r (A-P scaling exponent and height)
EXT	0.366 (p = 0.167)	−0.550 (p = 0.026)
INT	−0.191 (p = 0.503)	−0.353 (p = 0.201)
CON	0.289 (p = 0.303)	0.109 (p = 0.711)
Overall	0.111 (p = 0.472)	−0.256 (p = 0.0873)

Note. Pearson correlation coefficients of scaling exponent data and participant height data in the form “r (p value)”.

Discussion

The purpose of this study was to investigate the following research question: does instructing the direction of attentional focus affect single leg static balance performance as reflected by COP complexity? Statistically significant differences among groups were not found for any of the measured variables. These results are useful because they might suggest that characterizing instructions in terms of the direction of attentional focus that they facilitate- in line with Nideffer (1976)- is not itself sufficient to capture the usefulness and effectiveness of instructions. A possible explanation for the seemingly mixed results of the effect of internal and external attentional focus on static standing balance performance may be related to the nature, relevance and usefulness of the information attended to in relation to postural control demands. It is clear that postural dynamics are related to the perception of visual information^24–27 which is located externally, and proprioceptive information²⁸ which is located internally. Therefore, perhaps the effects of attentional focus instructions should be considered closely in terms of the content and relevance of the information attended to with respect to the constraints that define the context of the task being performed as opposed to specifically the direction of focus in and of itself. The dynamics of perception are self-organized patterns themselves,²⁹ and perception and action are circularly related to each other;²⁹ each influences and supports the other. Variability in the dynamics of perception and action support skilled behavior.³⁰ It is in this light that Yi-Ching Peh et al. (2011) argue that the usefulness of internal focus might be underemphasized, and that both might play an important role in the development and performance of perceptual-motor skills. As the philosophical perspective of complementarity³¹ would have it, perhaps internal and external focus are complementary, and insight might be gained by considering the interplay between the two. Careful consideration of how information attended to is related to the collective variable dynamics, rather than just the location of information, might help guide instructional strategies regarding where to direct attentional focus.

When information is considered as relevant, it is considered as such with respect to a particular task goal.^29,32–34 When relevant information with respect to the task goal is located internally, internal focus might be appropriate as supported by research on attentional focus instructions using tasks such as taekwondo routines¹⁰ and swimming emphasizing movement form.¹² If the nature of attentional focus instructions used in research conflict with the goal of the performance, the effectiveness and usefulness of both internal and external attentional focus might not be exposed.³⁵ In this light, to learn more about how internal and external attentional focus impacts static standing postural control, comparing how different attentional focus instructions qualitatively affect variables related to the movement process might be useful.

In the present study it was decided to operationalize the definitions of the directions of attentional focus - internal attentional focus and external attentional focus - in line with Nideffer's (1976) characterization of the direction of attentional focus. Both experimental conditions in the present study involved attempted manipulations of the direction of the attentional focus (counting heart beats and counting cartoon scene changes) without appreciably changing the relevance of the focus (with respect to the balance task), and the results did not yield statistically significant differences among groups. The results therefore do not support that manipulating the direction of attentional focus alone is sufficient to affect static single leg balance performance, supporting the above discussion on the importance of considering the nature and relevance of the information attended to.

Interestingly, neither experimental group significantly differed in performance from the control group in which the participants only received instructions to stand as still as possible. These results do not support the claim that the addition of a secondary counting task to a static standing balance task yields superior balance performance. Some insight might be gained by considering the relationship between secondary task constraints and static balance tasks used in the current study and in previous research. In a study by Cluff et al. (2010), non-linear analyses on COP trajectories indicated that balance performance improved (increased complexity) with the addition of stick balancing as a secondary task compared to the single-task control condition. Donker et al. (2007) found statistically significant differences in the complexity of COP trajectories between single-task and secondary task conditions during bilateral standing balance. The secondary task used by Donker et al. (2007) was speaking names backwards that were spoken to them by the researcher. Uiga et al. (2018) found that silently counting tones as a secondary task during static standing balance did not yield statistically significant differences in the complexity of COP trajectories between single and secondary task conditions. Interestingly, Cluff et al. (2010) also used a silent arithmetic dual-task in their study (the participants did not speak their answer during the balance task), which did not yield differences in balance performance as reflected by the complexity of the COP time series. A possible explanation for the lack of significant effect of the secondary tasks on balance performance in the present study, tone counting in Uiga et al. (2018), and the silent arithmetic task in Cluff et al. (2010), compared to the effect of stick balancing in Cluff et al. (2010) and speaking names backwards in Donker et al. (2007), might be related to the nature of the secondary tasks. The secondary tasks in the present study, Uiga et al. (2018) and Cluff et al. (2010) (silent arithmetic) did not require motor responses, unlike stick balancing and speaking names backwards. Since posture is typically performed to support suprapostural tasks (Smart et al. 2004; Stroffregen et al. 1999), it is reasonable to suspect that the motor responses required during stick balancing and speech are related to the significant differences in COP complexity between groups in Cluff et al. (2010) and Donker et al. (2007). Cluff et al. (2010) noted changes in the timescale of postural corrections during the stick balancing in the form of a “drift and correct” mechanism; the postural dynamics reflected the task demands of stick balancing. Although the motor component of the secondary task used in Donker et al. (2007) only required speaking, it is worth noting that even uttering simple syllables such as “pa” uses as many as seventy muscles which control respiratory, velar, facial, pharyngeal, laryngeal, lingual and masticatory movements (Abbs & Connor, 1989)⁴⁷. Lagier et al. (2010)⁴⁸ provided evidence that vocal effort and posture do seem to be functionally coordinated together. As Lagier et al. (2010) note, vocal effort involves the whole body. Therefore, the speech component of the secondary task used in Donker et al. (2007) might be related to the increased complexity of the COP dynamics in the secondary task condition. In Cluff et al. (2010) and Donker et al. (2007), the focus on the secondary task might be considered as “relevant” with respect to the postural control task if the postural control system was functioning in a subservient way to the secondary task demands. In other words, the focus might be considered as directed to the outcome of the movement process if the postural control system acted as a component of the processes functioning to serve the motor performance of the secondary tasks. A consistent finding in attentional focus research is that when focus is on the outcome of the movement process (typically described as external focus) as opposed to the movement process itself (typically described as internal focus), performance is superior.⁸ In this light, research on the effects of secondary motor tasks on postural control performance are consistent with most of the research on the effects of attentional focus on motor performance.

It should be acknowledged that although the present study did not detect significant differences among groups, it cannot be ruled out that a larger sample size is needed to detect statistically significant differences with a smaller effect size. Furthermore, it cannot be ruled out that other outcome measures might have yielded significant differences. It is also important to note that the anterior-posterior positioning of the participants on the force platform was not prescribed nor measured, and the height of the video monitor was not matched for the height of each participant. Since these factors relate to the orientation of the participant with the monitor they were looking at, these confounding variables might have introduced error. Indeed, the correlation analysis of scaling exponent data and participant height data revealed a statistically significant Pearson correlation coefficient of −0.55 (p = 0.0257) for the external focus group (EXT) in the A-P direction, suggesting greater balance performance with increasing height. The correlation analysis did not reveal statistically significant results for the other groups, nor was the coefficient significant for the pooled data (Table 3). Therefore, it cannot be ruled out that the lack of significant difference of the external focus (EXT) group's balance performance compared with the internal focus (INT) and control (CON) groups is related to the inconsistency of the relationship between the television height and the height of the participants. This proposition would not be inconsistent with previous literature. Wang, Ko, Challis and Newell (2014) demonstrated that motion of the neck is coordinated with the ankle, knee and hip during the control of human standing posture; since the height of the television monitor constrained the direction of the participants’ gaze in the external group, it seems reasonable to suspect that this might have influenced head position and cervical motion in ways that were particular to the height of the participant.

Moreover, the results of the present study should be interpreted within its limitations. Although written instructions were provided to participants intended to manipulate the direction of their attentional focus, it is not possible to control for the intentions of the participants. No follow-up questionnaires were used to assess the participants’ adherence to the written instructions, nor were participants asked to report their count of total number of heart beats or cartoon scene changes. Therefore, one cannot state with confidence that these results reflect the effect of the direction of attentional focus, because attentional focus was not measured; these results reflect the effects of the particular written instructions provided to the participants to the extent that participants read, remembered and attempted to follow the written directions provided to them prior to the performance of the balance task. It is also noteworthy that standing on a force platform may not represent normal standing balance, as balance is typically not performed for its own sake, and task constraints can influence emergent balance strategies.^26,27 Therefore, the results of this study should not be generalized to other balance tasks. Finally, these results might not generalize to age ranges outside of the age range of the volunteers who participated in the present study, since task-specific, bi-directional changes in complexity have been found in older individuals compared to younger individuals,^36,37 and postural control performance in children (9–18 years) has been shown to improve with increasing age.^37,38 Participants in this study were age 19–28 years (20.6 ± 1.68), and therefore results should not be generalized to populations outside of this age range.

Conclusion

The purpose of this study was to investigate whether the direction of attentional focus affects static single leg balance performance. The results of the present study do not support the proposition that the direction of attentional focus alone is sufficient to static single leg balance performance as reflected by the scaling exponent and RMSE of the COP time series. Based on these results, it is probably a good idea that instructors consider more than just the direction of the attentional focus of the performer if the goal is to facilitate balance performance. Future research might address whether instructions should be provided in the context of balance itself or whether balance is best to be practiced and instructed in the context of secondary or suprapostural tasks. Additionally, future research could address whether the effect of performing secondary tasks on static balance performance is different for secondary tasks requiring a motor response compared to secondary tasks that do not require a motor response. Overall, it is recommended that practitioners consider more than the direction of the attentional focus that their instructions facilitate, but also the nature and content of the information attended to in relation to the task goal.

Footnotes

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

ORCID iD

Cory J Monahan

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