# The Effect of Cognitive Resource Competition Due to Dual-Tasking on the Irregularity and Control of Postural Movement Components

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## Abstract

**:**

## 1. Introduction

_{k}) [36,37,38,39]. It has also been shown, that the respective time-series of these PM

_{k}, specifically principal positions PP

_{k}and principal accelerations PA

_{k}, can be used to compute COP time-series [34], hence they contain the information of COP-trajectories. Furthermore, it could be shown that the COP-irregularity correlates with the irregularity of specific PP

_{k}, showing that to some extent the dynamics of PP are preserved in the COP [33]. Hence, these results also suggest that PCA-based variables can be used to expand the concept of COP-irregularity and might be better suitable to explain aspects of motor control that require considering the minimal intervention principle. Furthermore, PCA-related variables computed on the PA time-series were sensitive enough to identify effects in aging [40] and leg dominance [41], by quantifying how often the control system intervenes (N) and how variable the timing of these interventions is (σ). Again, not the entire neuromuscular control system displayed the effects, but only the control of specific movement components, emphasizing the need to consider the minimal intervention principle.

_{k}and the PA

_{k}(${\mathrm{SaEn}}_{k}^{PP},\text{}{\mathrm{SaEn}}_{k}^{PA}$) should also display this n-shaped relationship. In addition, tighter movement control has been interpreted as more efficient postural control, i.e., a high number of interventions of the control system (N) and a lower timing variability (σ) were observed in young versus old [40] or in the dominant versus the non-dominant leg [41]. Hence, the N

_{k}should also display the n-shaped relationship, whereas σ

_{k}should display a u-shaped relationship.

## 2. Materials and Methods

#### 2.1. Participants and Measurement Procedures

_{n}) of increasing difficulty. The DT

_{n}were auditory n-back working memory tasks with n = 1–4, i.e., via headphones the participants listened to a sequence of 26 seemingly random numbers ranged between 1 and 9, and had to reply with “yes” when the current number was equal to the nth digit before. Furthermore, each sequence contained 6 n-back stimuli and the inter-stimulus interval time in the sequence was randomized to avoid subjects falling into a rhythmical routine. Full body kinematics were recorded at 240 frames per second by 8 synchronized video cameras (Motion Analysis Corp., Santa Rosa, CA, USA) using 37 reflective markers (standard 39 plug-in gait marker set without markers on the hands).

#### 2.2. Data Analysis

#### 2.2.1. Pre-Processing

#### 2.2.2. Kinematic Principal Component Analysis

_{k}that are oriented in the direction of the largest variance and define postural movement components or “principal movements” PM

_{k}[34]. Furthermore, the PCA yields trial-specific scores PP

_{k}(t) that represent the positions in posture space with respect to the PC

_{k}and eigenvalues EV

_{k}that quantify the overall contribution of a PM

_{k}to the overall variance. In a similar fashion to standard biomechanics the PP

_{k}can be used to compute principal velocities PV

_{k}(t) and principal accelerations PA

_{k}(t) [34].

#### 2.2.3. Measures of Postural Control

_{k}and the PA

_{k}contain the information about posture and postural accelerations over time. It has been shown that they can be used not only to compute the center of pressure COP [34], but to quantify similar aspects of postural control as COP-based irregularity, with the advantage of preserving the information about involved body segment movements [33]. Similarly, in the current study the sample entropy SaEn was computed on both the detrended PP

_{k}and the PA

_{k}of all volunteers (${\mathrm{SaEn}}_{k}^{PP}$ and ${\mathrm{SaEn}}_{k}^{PA}$), as measure of the irregularity of the movement strategies and the irregularity of the neuromuscular control of specific movement components, respectively. The PP

_{k}were detrended by subtracting the floating average taken over 501 data points (around 4 s). The floating averages of the first 250 and last 250 data points were considered equal to the nearest available value. The sample entropy calculation parameters were set to typical values: embedding dimension m = 2 and r = 0.2·STD [48] (with STD being the standard deviation of the time-series). The time-lag was set to $\tau =12\stackrel{\wedge}{=}\frac{12}{120}\mathrm{s}=100\mathrm{ms}$, which is a meaningful timeframe from a physiological point of view [49].

_{k}that further characterize the neuromuscular control, namely, the number of PA zero crossings N

_{k}and the standard deviation of the time between zero-crossings σ

_{k}. These variables serve as a measure for the tightness of the neuromuscular control; specifically, a “tight” control is considered to have a high number of interventions N and a low timing variability of the interventions. Previous studies using these measures were able to identify age differences in ST tandem stances [40], as well as differences in the control of one-legged static balance [39,41] when comparing dominant to non-dominant leg.

#### 2.3. Validity Considerations and Cross-Validation

_{k}(t) was conducted, revealing that the highest power resided in frequencies up to 3 Hz. However, visible power was still found up to 7 Hz. The PP

_{k}(t) and the PA

_{k}(t) were therefore filtered with a 6th-order Butterworth filter using a cut-off frequency of 7 Hz before computing the variables. Unfortunately, SaEn

_{k}, N

_{k}and σ

_{k}are all susceptible to noise. However, previous studies show [33,40,41] that while the absolute values of these variables depend highly on the filtering frequency and the chosen entropy parameters, resultant statistical effects do not.

#### 2.4. Statistics

_{k}and σ

_{k}). Normality of the data was tested with both Kolmogorov–Smirnov and Shapiro–Wilk tests. The equality of variances was assessed computing Levene’s tests. If the sphericity criterion was not met (Mauchly’s test) a Greenhouse–Geisser or a Huynh–Feldt correction was performed for epsilon values smaller than 0.75, or greater or equal than 0.75, respectively. We report the p-value, partial eta squared ${\mathsf{\eta}}_{p}^{2}$, and the observed power $\mathsf{\pi}$ for all main dual-tasking effects. To simplify the main table, the corrected degrees of freedom and the respective F statistics were not included. For each statistically significant result ($p<0.05$) a post-hoc analysis was conducted using a Sidak correction. For these we report the increases in percent and the p-values.

## 3. Results

#### 3.1. PCA Results

#### 3.2. Dual-Tasking Effects (n-back)

_{5}and PP

_{6}(${\mathrm{SaEn}}_{5}^{PP}$: +12% (ST to DT

_{2}), p = 0.004; and ${\mathrm{SaEn}}_{6}^{PP}$: +11% (ST to DT

_{2}), p = 0.039). Furthermore, significant effects were found between 2-back and 4-back DT in the sample entropy of PP

_{6}(${\mathrm{SaEn}}_{6}^{PP}$: −9% (DT

_{2}to DT

_{4}), p = 0.012) and the first two PAs (${\mathrm{SaEn}}_{1}^{PA}$: −3% (DT

_{2}to DT

_{4}), p = 0.023; and ${\mathrm{SaEn}}_{2}^{PA}$: −2% (DT

_{2}to DT

_{4}), p = 0.020). The variables N

_{5}and σ

_{2}displayed differences between two DT-condition each (N

_{5}: −4% (DT

_{1}to DT

_{2}), p = 0.026 and σ

_{2}: −3% (ST to DT

_{4}), p = 0.021). No statistically significant effects were found in the post-hoc analysis of N

_{6}.

#### 3.3. Interaction Effects

_{(4,156)}= 3.14, p = 0.016, η

_{p}

^{2}= 0.08, π = 0.79; and ${\mathrm{SaEn}}_{5}^{PA}$: F

_{(3.45,134.00)}= 3.01, p = 0.026, η

_{p}

^{2}= 0.07, π = 0.74). In both cases only the older participants displayed significant dual-tasking effects and the expected n-shaped relationship (Figure 3), while the younger age group showed rather constant irregularity values throughout varying dual-task conditions. Specifically, the interaction effects in ${\mathrm{SaEn}}_{6}^{PP}$ showed that the main dual-tasking effects in the irregularity of PP

_{6}originated from the older participants.

#### 3.4. Age Effects between Subjects

_{2}: F

_{(1,39)}= 8.74, p = 0.005, η

_{p}

^{2}= 0.18, π = 0.82). In detail, the older participants displayed decreased activity of the control system in three conditions (ST: −12% (old vs. young): p = 0.002; DT

_{1}: −12% (old vs. young): p = 0.003; and DT

_{2}: −9% (old vs. young): p = 0.025). Furthermore, significant age effects were found in the movement component that resembles an upper body retraction (${\mathrm{SaEn}}_{3}^{PP}$: F

_{(1,39)}= 5.93, p = 0.020, η

_{p}

^{2}= 0.132, π = 0.66), originating from the higher sway irregularity of the older group in the two easiest dual-tasking conditions (DT

_{1}: +18% (old vs. young): p = 0.009 and DT

_{2}: +19% (old vs. young): p = 0.002). In addition, age effects were found in the hip/knee strategy (${\mathrm{SaEn}}_{6}^{PP}$: F

_{(1,39)}= 7.09, p = 0.011, η

_{p}

^{2}= 0.15, π = 0.74) also originating from the higher sway irregularity of the older group in the two easiest dual-tasking conditions (DT

_{1}: +23% (old vs. young), p = 0.005; and DT

_{2}: +28% (old vs. young), p < 0.001). Figure 4 visualizes the descriptive statistics of the significant age effects. Complete descriptive statistics can be found in the Supplementary Materials (“Age_effects.png”).

## 4. Discussion

#### 4.1. Dual-Tasking Effects

_{1}). To our knowledge there is only one previous study comparing the same n-back DT conditions (n = 1, 2, 3) for tandem stances, which found comparable DT effects in the AP-COP displacement [10]. For the mediolateral (ML) ankle sway (PM

_{2}) both the irregularity of the control ${\mathrm{SaEn}}_{2}^{PA}$ and the timing variability of the interventions σ

_{2}displayed statistical significance. In agreement with previous literature [10,12,14,15,16,20,53] and H1 the effects in ${\mathrm{SaEn}}_{2}^{PA}$ suggest higher automaticity [16,22,24,26] of the control system for medium difficulties followed by a decrease for harder dual-tasks. However, while ${\mathrm{SaEn}}_{2}^{PA}$ shows the expected n-shape trend, supporting H1, σ

_{2}exhibits a steady increase with difficulty level. This steady increase of σ

_{2}shows that the timing of the control interventions of the mediolateral ankle sway becomes more variable with increasing task difficulty, which would suggest a steady decrease in the tightness of the control. Due to the smaller base of support in mediolateral direction while tandem standing, it seems obvious that PM

_{2}is one of the most critical movement strategies and is tightly controlled by an effective control system [40]. In contrast to the literature [10,12,14,15,16,20,53] and H1, this would suggest that although the automaticity displays the n-shape the efficacy of postural control is steadily reduced with increasing dual-task difficulty. The trends in ${\mathrm{SaEn}}_{2}^{PP}$ and N

_{2}(n-shaped automaticity and steady decrease in the number of interventions) support this assumption; however, these results were not significant.

_{2}, N

_{k}and σ

_{k}display their extrema in the DT

_{1}condition. The AP-COP sway in the other study [10] also displayed the expected u-form with the least sway in the DT

_{2}condition. This might suggest that in terms of sway and control irregularity, highest automatization is reached at the more difficult DT

_{2}condition, whereas the tightest postural control (high N, low σ) is typically found in the DT

_{1}condition while DT

_{2}already shows signs of a decreased control tightness. It is important to note that this interpretation is speculative, since the peak values were only significantly different compared to extreme values, e.g., when comparing the irregularity of DT

_{2}to the irregularity in ST or the most difficult DT

_{4}condition; or when comparing the timing variability σ

_{2}of ST to DT

_{4}condition. Hence, the data does not allow us to make definitive statements when comparing non-extreme conditions and therefore the conditions that display peak-values could not be determined in a conclusive manner.

_{1-2}, i.e., anteroposterior AP and mediolateral ML ankle sway, is affected in greater magnitude than the control irregularity of PM

_{3-6}. In addition, the ML ankle sway is the only movement component that shows significant DT effects in the timing variability of the neuromuscular system’s intervention. These results support H2, since PM

_{1}and PM

_{2}displayed the main dynamics and PM

_{2}is probably the most task-relevant movement to be controlled. However, the absence of significant dual-tasking effects in ${\mathrm{SaEn}}_{1\u20134}^{PP}$ and N

_{1-4}suggests that the sway regularity and the number of interventions are affected in greater magnitude in PM

_{5-6}, i.e., upper body rotation and hip/knee strategy, respectively. On the one hand, the minimal intervention principle suggests that the control system focuses on task relevant movements, thus we expect limitations in cognitive resources to affect these relevant components in greater magnitude. This would suggest that in terms of sway irregularity and the number of interventions of the control system, upper body rotations and the hip/knee strategy are very task-relevant. On the other hand, if PM

_{1-2}are, as we assume, the most task relevant, these results would suggest that the control system manages to channel sufficient attention to PM

_{1-2}to avoid significant changes in the sway irregularity and the number of control interventions. Thus, the cognitive resources available for higher order movement components would be limited resulting in the observed dual tasking effects in PM

_{5-6}.

#### 4.2. Age-DT Interaction Effects

#### 4.3. Age Effects

_{2}). However, only the age differences in the two easiest conditions (ST and DT

_{1}) were significant. Furthermore, the older participants only displayed minor decreases of N

_{2}in this component that is critical for maintaining postural stability, while the younger participant’s number of control interventions decreased steadily. Hence, in contrast to H3, it is the younger age group that exhibits larger decreases in control interventions with increasing cognitive load. In addition, the trend in σ

_{2}suggested that the younger participants exhibited a greater increase in timing variability when increasing cognitive load. Nevertheless, although the control tightness of the mediolateral ankle sway of the younger participants decreased more with increased cognitive load, they exhibited tighter postural control in all conditions in this critical component.

_{3/6}was more automatized in the ST condition and then displayed the postulated increase in automaticity, followed by a decrease of sway irregularity due to resource competition. Here, the older participants followed the expected u-shaped pattern, while the younger participants seemed to maintain an unaltered focus of attention on trunk stability throughout the DT conditions. In accordance with previous findings [54,55], this could be interpreted as a sign of less variable and more effective control of trunk stability of the younger group.

#### 4.4. Limitations

_{k}(with k > 6) explains around 5% of the overall variance. Though it is possible that interesting aspects of the movement were neglected, we are confident that the main dynamics of the balancing task were captured. Furthermore, the PM

_{k}are linear movement components. Therefore, the interpretation of individual PM

_{k}must be done with caution, since they can only approximate the dynamics of real movements.

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Woollacott, M.; Shumway-Cook, A. Attention and the control of posture and gait: A review of an emerging area of research. Gait Posture
**2002**, 16, 1–14. [Google Scholar] [CrossRef] - Bronstein, A.M. Multisensory integration in balance control. Handb. Clin. Neurol.
**2016**, 137, 57–66. [Google Scholar] [PubMed] - Kaliuzhna, M.; Ferrè, E.R.; Herbelin, B.; Blanke, O.; Haggard, P. Multisensory effects on somatosensation: A trimodal visuo-vestibular-tactile interaction. Sci. Rep.
**2016**, 6, 26301. [Google Scholar] [CrossRef] [PubMed] - Bringoux, L.; Scotto Di Cesare, C.; Borel, L.; Macaluso, T.; Sarlegna, F.R. Do Visual and Vestibular Inputs Compensate for Somatosensory Loss in the Perception of Spatial Orientation? Insights from a Deafferented Patient. Front. Hum. Neurosci.
**2016**, 10, 181. [Google Scholar] [CrossRef] [PubMed] - Teasdale, N.; Simoneau, M. Attentional demands for postural control: The effects of aging and sensory reintegration. Gait Posture
**2001**, 14, 203–210. [Google Scholar] [CrossRef] - Redfern, M.S.; Yardley, L.; Bronstein, A.M. Visual influences on balance. J. Anxiety Disord.
**2001**, 15, 81–94. [Google Scholar] [CrossRef] - Woollacott, M.H. Systems contributing to balance disorders in older adults. J. Gerontol. Ser. A Biol. Sci. Med. Sci.
**2000**, 55, M424–M428. [Google Scholar] [CrossRef] - Pellecchia, G.L. Postural sway increases with attentional demands of concurrent cognitive task. Gait Posture
**2003**, 18, 29–34. [Google Scholar] [CrossRef] - Melzer, I.; Benjuya, N.; Kaplanski, J. Age-related changes of postural control: Effect of cognitive tasks. Gerontology
**2001**, 47, 189–194. [Google Scholar] [CrossRef] [PubMed] - Vander Velde, T.; Woollacott, M. Non-visual spatial tasks reveal increased interactions with stance postural control. Brain Res.
**2008**, 1208, 95–102. [Google Scholar] [CrossRef] [PubMed] - Bustillo-Casero, P.; Villarrasa-Sapiña, I.; García-Massó, X. Effects of dual task difficulty in motor and cognitive performance: Differences between adults and adolescents. Hum. Mov. Sci.
**2017**, 55, 8–17. [Google Scholar] [CrossRef] [PubMed] - Riley, M.A.; Baker, A.A.; Schmit, J.M.; Weaver, E. Effects of visual and auditory short-term memory tasks on the spatiotemporal dynamics and variability of postural sway. J. Mot. Behav.
**2005**, 37, 311–324. [Google Scholar] [CrossRef] [PubMed] - Stins, J.F.; Roerdink, M.; Beek, P.J. To freeze or not to freeze? Affective and cognitive perturbations have markedly different effects on postural control. Hum. Mov. Sci.
**2011**, 30, 190–202. [Google Scholar] [CrossRef] [PubMed] - Polskaia, N.; Richer, N.; Dionne, E.; Lajoie, Y. Continuous cognitive task promotes greater postural stability than an internal or external focus of attention. Gait Posture
**2015**, 41, 454–458. [Google Scholar] [CrossRef] [PubMed] - Huxhold, O.; Li, S.-C.; Schmiedek, F.; Lindenberger, U. Dual-tasking postural control: Aging and the effects of cognitive demand in conjunction with focus of attention. Brain Res. Bull.
**2006**, 69, 294–305. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Potvin-Desrochers, A.; Richer, N.; Lajoie, Y. Cognitive tasks promote automatization of postural control in young and older adults. Gait Posture
**2017**, 57, 40–45. [Google Scholar] [CrossRef] [PubMed] - Lacour, M.; Bernard-Demanze, L.; Dumitrescu, M. Posture control, aging, and attention resources: Models and posture-analysis methods. Neurophysiol. Clin. Clin. Neurophysiol.
**2008**, 38, 411–421. [Google Scholar] [CrossRef] - Vuillerme, N.; Nafati, G. How attentional focus on body sway affects postural control during quiet standing. Psychol. Res.
**2007**, 71, 192–200. [Google Scholar] [CrossRef] - Wulf, G.; Mercer, J.; McNevin, N.; Guadagnoli, M.A. Reciprocal influences of attentional focus on postural and suprapostural task performance. J. Mot. Behav.
**2004**, 36, 189–199. [Google Scholar] [CrossRef] - Donker, S.F.; Roerdink, M.; Greven, A.J.; Beek, P.J. Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control. Exp. Brain Res.
**2007**, 181, 1–11. [Google Scholar] [CrossRef] [Green Version] - Stins, J.F.; Michielsen, M.E.; Roerdink, M.; Beek, P.J. Sway regularity reflects attentional involvement in postural control: Effects of expertise, vision and cognition. Gait Posture
**2009**, 30, 106–109. [Google Scholar] [CrossRef] [PubMed] - Cavanaugh, J.T.; Guskiewicz, K.M.; Giuliani, C.; Marshall, S.; Mercer, V.S.; Stergiou, N. Recovery of postural control after cerebral concussion: New insights using approximate entropy. J. Athl. Train.
**2006**, 41, 305–313. [Google Scholar] [PubMed] - Schmit, J.M.; Regis, D.I.; Riley, M.A. Dynamic patterns of postural sway in ballet dancers and track athletes. Exp. Brain Res.
**2005**, 163, 370–378. [Google Scholar] [CrossRef] [PubMed] - Borg, F.G.; Laxåback, G. Entropy of balance—Some recent results. J. Neuroeng. Rehabil.
**2010**, 7, 38. [Google Scholar] [CrossRef] [PubMed] - Winter, D.A.; Prince, F.; Frank, J.S.; Powell, C.; Zabjek, K.F. Unified theory regarding A/P and M/L balance in quiet stance. J. Neurophysiol.
**1996**, 75, 2334–2343. [Google Scholar] [CrossRef] [PubMed] - Cavanaugh, J.T.; Mercer, V.S.; Stergiou, N. Approximate entropy detects the effect of a secondary cognitive task on postural control in healthy young adults: A methodological report. J. Neuroeng. Rehabil.
**2007**, 4, 42. [Google Scholar] [CrossRef] [PubMed] - Ladislao, L.; Rabini, R.A.; Ghetti, G.; Fioretti, S. Approximate entropy on posturographic data of diabetic subjects with peripheral neuropathy. Gait Posture
**2008**, 28, S6–S7. [Google Scholar] [CrossRef] - Santarcangelo, E.L.; Scattina, E.; Carli, G.; Balocchi, R.; Macerata, A.; Manzoni, D. Modulation of the postural effects of cognitive load by hypnotizability. Exp. Brain Res.
**2009**, 194, 323–328. [Google Scholar] [CrossRef] [Green Version] - Haran, F.J.; Keshner, E.A. Sensory Reweighting as a Method of Balance Training for Labyrinthine Loss. J. Neurol. Phys. Ther.
**2008**, 32, 186. [Google Scholar] [CrossRef] - De Beaumont, L.; Mongeon, D.; Tremblay, S.; Messier, J.; Prince, F.; Leclerc, S.; Lassonde, M.; Théoret, H. Persistent motor system abnormalities in formerly concussed athletes. J. Athl. Train.
**2011**, 46, 234–240. [Google Scholar] [CrossRef] - Gao, J.; Hu, J.; Buckley, T.; White, K.; Hass, C. Shannon and Renyi entropies to classify effects of Mild Traumatic Brain Injury on postural sway. PLoS ONE
**2011**, 6, e24446. [Google Scholar] [CrossRef] [PubMed] - Sosnoff, J.J.; Broglio, S.P.; Shin, S.; Ferrara, M.S. Previous Mild Traumatic Brain Injury and Postural-Control Dynamics. J. Athl. Train.
**2011**, 46, 85–91. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Haid, T.; Federolf, P. Human Postural Control: Assessment of Two Alternative Interpretations of Center of Pressure Sample Entropy through a Principal Component Factorization of Whole-Body Kinematics. Entropy
**2018**, 20, 30. [Google Scholar] [CrossRef] - Federolf, P.A. A novel approach to study human posture control: “Principal movements” obtained from a principal component analysis of kinematic marker data. J. Biomech.
**2016**, 49, 364–370. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Todorov, E.; Jordan, M.I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci.
**2002**, 5, 1226–1235. [Google Scholar] [CrossRef] [PubMed] - Zago, M.; Pacifici, I.; Lovecchio, N.; Galli, M.; Federolf, P.A.; Sforza, C. Multi-segmental movement patterns reflect juggling complexity and skill level. Hum. Mov. Sci.
**2017**, 54, 144–153. [Google Scholar] [CrossRef] [PubMed] - Daffertshofer, A.; Lamoth, C.J.C.; Meijer, O.G.; Beek, P.J. PCA in studying coordination and variability: A tutorial. Clin. Biomech.
**2004**, 19, 415–428. [Google Scholar] [CrossRef] - Troje, N.F. Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. J. Vis.
**2002**, 2, 371–387. [Google Scholar] [CrossRef] - Promsri, A.; Haid, T.; Werner, I.; Federolf, P. Influence of lower-limb dominance on coordinative movement structures observed during single-leg balancing on a multiaxial unstable surface. Gait Posture
**2018**. [Google Scholar] [CrossRef] - Haid, T.H.; Doix, A.-C.M.; Nigg, B.M.; Federolf, P.A. Age Effects in Postural Control Analyzed via a Principal Component Analysis of Kinematic Data and Interpreted in Relation to Predictions of the Optimal Feedback Control Theory. Front. Aging Neurosci.
**2018**, 10, 22. [Google Scholar] [CrossRef] - Promsri, A.; Haid, T.; Federolf, P. How does lower limb dominance influence postural control movements during single leg stance? Hum. Mov. Sci.
**2018**, 58, 165–174. [Google Scholar] [CrossRef] [PubMed] - Gløersen, Ø.; Federolf, P. Predicting Missing Marker Trajectories in Human Motion Data Using Marker Intercorrelations. PLoS ONE
**2016**, 11, e0152616. [Google Scholar] [CrossRef] [PubMed] - Federolf, P.A. A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data. PLoS ONE
**2013**, 8, e78689. [Google Scholar] [CrossRef] [PubMed] - Rine, R.M.; Schubert, M.C.; Whitney, S.L.; Roberts, D.; Redfern, M.S.; Musolino, M.C.; Roche, J.L.; Steed, D.P.; Corbin, B.; Lin, C.-C.; et al. Vestibular function assessment using the NIH Toolbox. Neurology
**2013**, 80, S25–S31. [Google Scholar] [CrossRef] [PubMed] - Coquart, J.B.J.; Garcin, M. Knowledge of the endpoint: Effect on perceptual values. Int. J. Sports Med.
**2008**, 29, 976–979. [Google Scholar] [CrossRef] [PubMed] - Gløersen, Ø.; Myklebust, H.; Hallén, J.; Federolf, P. Technique analysis in elite athletes using principal component analysis. J. Sports Sci.
**2017**, 36, 229–237. [Google Scholar] [CrossRef] [PubMed] - Defense Technical Information Center. DTIC ADA304353: Anthropometry and Mass Distribution for Human Analogues, Volume I: Military Male Aviators; Naval Biodynamics Laboratory: New Orleans, LA, USA, 1988. [Google Scholar]
- Estrada, L.; Torres, A.; Sarlabous, L.; Jané, R. Influence of Parameter Selection in Fixed Sample Entropy of Surface Diaphragm Electromyography for Estimating Respiratory Activity. Entropy
**2017**, 19, 460. [Google Scholar] [CrossRef] - Kanekar, N.; Lee, Y.-J.; Aruin, A.S. Frequency analysis approach to study balance control in individuals with multiple sclerosis. J. Neurosci. Methods
**2014**, 222, 91–96. [Google Scholar] [CrossRef] [PubMed] - Bro, R.; Kjeldahl, K.; Smilde, A.K.; Kiers, H.A.L. Cross-validation of component models: A critical look at current methods. Anal. Bioanal. Chem.
**2008**, 390, 1241–1251. [Google Scholar] [CrossRef] [PubMed] - Camacho, J.; Ferrer, A. Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Theoretical aspects. J. Chemom.
**2012**, 26, 361–373. [Google Scholar] [CrossRef] - Diana, G.; Tommasi, C. Cross-validation methods in principal component analysis: A comparison. Stat. Methods Appl.
**2002**, 11, 71–82. [Google Scholar] [CrossRef] - Deviterne, D.; Gauchard, G.C.; Jamet, M.; Vançon, G.; Perrin, P.P. Added cognitive load through rotary auditory stimulation can improve the quality of postural control in the elderly. Brain Res. Bull.
**2005**, 64, 487–492. [Google Scholar] [CrossRef] [PubMed] - Gill, J.; Allum, J.H.J.; Carpenter, M.G.; Held-Ziolkowska, M.; Adkin, A.L.; Honegger, F.; Pierchala, K. Trunk Sway Measures of Postural Stability During Clinical Balance Tests: Effects of Age. J. Gerontol. Ser. A
**2001**, 56, M438–M447. [Google Scholar] [CrossRef] - Allum, J.H.J.; Adkin, A.L.; Carpenter, M.G.; Held-Ziolkowska, M.; Honegger, F.; Pierchala, K. Trunk sway measures of postural stability during clinical balance tests: Effects of a unilateral vestibular deficit. Gait Posture
**2001**, 14, 227–237. [Google Scholar] [CrossRef]

**Figure 1.**Visualization of the first six principal movements (PM

_{1}–PM

_{6}) of the tandem stance with respective amplification factors (AmpFac). For each PM the minimal and maximal deviation from the mean posture are displayed.

**Figure 2.**Post-hoc analysis and descriptive statistics of the variables that displayed dual-tasking effects. ${\mathrm{SaEn}}_{k}^{PP/PA}$ stands for sample entropy of the principal position PP or principal acceleration PA of the kth principal movement. N

_{k}and σ

_{k}stand for the number of control interventions and the timing variability of the interventions of the kth component, respectively. Significant post-hoc results are symbolized with asterisks. ST = single task; DT

_{n}= dual task with n-back auditory working task.

**Figure 3.**Descriptive statistics of the variables that displayed dual-tasking age interaction effects. ${\mathrm{SaEn}}_{k}^{PP/PA}$ stands for sample entropy of the principal position PP or principal acceleration PA of the kth principal movement. Significant post-hoc results are symbolized with asterisks (dual-tasking effects were found only in the older group). ST = single task; DT

_{n}= dual task with n-back auditory working task.

**Figure 4.**Descriptive statistics of the variables that displayed age effects. ${\mathrm{SaEn}}^{PP/PA}$ stands for sample entropy of the principal position PP or principal acceleration PA and PM

_{k}for the kth principal movement. N

_{k}stands for the number of control interventions in the kth component. Significant post-hoc results are symbolized with asterisks. ST = single task; DT

_{n}= dual task with n-back auditory working task.

**Table 1.**Description of the first six principal movements PM

_{k}that cumulatively describe over 95% of the overall variance. Significant effects in ${\mathrm{SaEn}}_{k}^{PP}$, ${\mathrm{SaEn}}_{k}^{PP}$, N

_{k}or σ

_{k}are symbolized by PP, PA, N or σ, respectively.

k | EV [%] | Effects | Main Strategy (Directions) | Specifications/Additional Features |
---|---|---|---|---|

1 | 51.1 | PA | Ankle (anterior/posterior) | No visible motions in the rest of the body. |

2 | 26.5 | PA, σ | Ankle (medial/lateral) | No visible motions in the rest of the body. |

3 | 9.7 | Upper body (retraction) | Upper body leans back. Front knee (flexion/extension). | |

4 | 3.9 | Weight shift (anterior/posterior) | Upper body shifted from over one foot to over the other. | |

5 | 2.6 | PP, N | Upper body rotation | No visible motions in the rest of the body. |

6 | 1.9 | PP, N | Hip/Knee strategy | Flexion/extension in both hip and knee. |

**Table 2.**Statistics describing dual-tasking effects of the variables ${\mathrm{SaEn}}_{k}^{PP},{\mathrm{SaEn}}_{k}^{PA}$, N

_{k}, and σ

_{k}. Significant effects are highlighted with bold font.

${\mathrm{SaEn}}_{\mathit{k}}^{\mathit{P}\mathit{P}}$ | ${\mathrm{SaEn}}_{\mathit{k}}^{\mathit{P}\mathit{A}}$ | N_{k} | σ_{k} | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

p | ${\mathit{\eta}}_{\mathit{p}}^{2}$ | $\mathit{\pi}$ | p | ${\mathit{\eta}}_{\mathit{p}}^{2}$ | $\mathit{\pi}$ | p | ${\mathit{\eta}}_{\mathit{p}}^{2}$ | $\mathit{\pi}$ | p | ${\mathit{\eta}}_{\mathit{p}}^{2}$ | $\mathit{\pi}$ | |

PM_{1} | 0.062 ^{1} | 0.06 | 0.64 | 0.016 *^{,2} | 0.09 | 0.76 | 0.147 ^{1} | 0.04 | 0.50 | 0.190 ^{2} | 0.04 | 0.37 |

PM_{2} | 0.228 | 0.04 | 0.45 | 0.028 * | 0.07 | 0.76 | 0.082 | 0.05 | 0.62 | 0.024 * | 0.07 | 0.77 |

PM_{3} | 0.054 | 0.06 | 0.68 | 0.101 ^{2} | 0.05 | 0.53 | 0.363 ^{1} | 0.03 | 0.33 | 0.403 | 0.03 | 0.03 |

PM_{4} | 0.128 ^{1} | 0.05 | 0.53 | 0.082 | 0.05 | 0.62 | 0.147 | 0.04 | 0.52 | 0.116 | 0.05 | 0.56 |

PM_{5} | 0.003 ** | 0.10 | 0.92 | 0.303 ^{1} | 0.03 | 0.34 | 0.011 *^{,1} | 0.09 | 0.83 | 0.216 ^{1} | 0.04 | 0.42 |

PM_{6} | 0.017 * | 0.07 | 0.80 | 0.099 ^{2} | 0.05 | 0.52 | 0.030 * | 0.07 | 0.75 | 0.065 ^{1} | 0.06 | 0.64 |

^{1}Huynh–Feldt corrected;

^{2}Greenhouse–Geisser corrected. * p < 0.05. ** p < 0.01.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Haid, T.; Federolf, P.
The Effect of Cognitive Resource Competition Due to Dual-Tasking on the Irregularity and Control of Postural Movement Components. *Entropy* **2019**, *21*, 70.
https://doi.org/10.3390/e21010070

**AMA Style**

Haid T, Federolf P.
The Effect of Cognitive Resource Competition Due to Dual-Tasking on the Irregularity and Control of Postural Movement Components. *Entropy*. 2019; 21(1):70.
https://doi.org/10.3390/e21010070

**Chicago/Turabian Style**

Haid, Thomas, and Peter Federolf.
2019. "The Effect of Cognitive Resource Competition Due to Dual-Tasking on the Irregularity and Control of Postural Movement Components" *Entropy* 21, no. 1: 70.
https://doi.org/10.3390/e21010070