
==== Front
Eur J Phys Rehabil Med
Eur J Phys Rehabil Med
EJPRM
European Journal of Physical and Rehabilitation Medicine
1973-9087
1973-9095
Edizioni Minerva Medica

38743389
8261
10.23736/S1973-9087.24.08261-3
Article
Effects of motor and cognitive complex training on obstacle walking and brain activity in people with Parkinson’s disease: a randomized controlled trial
WONG Pei-Ling 1
HUNG Chen-Wei 1
YANG Yea-Ru 1
YEH Nai-Chen 1
CHENG Shih-Jung 2
LIAO Ying-Yi 3
WANG Ray-Yau 1 *
1Department of Physical Therapy and Assistive Technology, National Yang Ming Chiao Tung University, Taipei, Taiwan (ROC); 2Department of Neurology, Mackay Memorial Hospital, Taipei, Taiwan (ROC); 3Department of Gerontological Health Care, National Taipei University of Nursing and Health Science, Taipei, Taiwan (ROC)
* Corresponding author: Ray-Yau Wang, Department of Physical Therapy and Assistive Technology, National Yang Ming Chiao Tung University, 155, Sec.2, Li-Nong St., Beitou District, Taipei, 112, Taiwan (ROC). E-mail: rywang@nycu.edu.tw
Authors’ contributions: Pei-Ling Wong, Ray-Yau Wang: conceived and designed the experiments. Pei-Ling Wong, Chen-Wei Hung, Nai-Chen Yeh: performed the experiments. Pei-Ling Wong, Chen-Wei Hung, Yea-Ru Yang, Ying-Yi Liao: analyzed the data. Shih-Jung Cheng: confirmed the medical diagnosis of subjects and recruited subjects. Pei-Ling Wong, Chen-Wei Hung, Ray-Yau Wang: interpreted the data and wrote the manuscript. All authors read and approved the final version of the manuscript.

28 8 2024
8 2024
60 4 611620
05 4 2024
01 2 2024
02 10 2023
2024 THE AUTHORS
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (CC BY-NC-ND) 4.0 License.
BACKGROUND

The difficulties in obstacle walking are significant in people with Parkinson’s disease (PD) leading to an increased fall risk. Effective interventions to improve obstacle walking with possible training-related neuroplasticity changes are needed. We developed two different exercise programs, complex walking training and motor-cognitive training, both challenging motor and cognitive function for people with PD to improve obstacle walking.

AIM

To investigate the effects of these two novel training programs on obstacle walking and brain activities in PD.

DESIGN

A single-center randomized, single-blind controlled study.

SETTING

University laboratory; outpatient.

POPULATION

Individuals with idiopathic PD.

METHODS

Thirty-two participants were randomly assigned to the complex walking training group (N.=11), motor-cognitive training group (N.=11) or control group (N.=10). Participants in training groups received exercises for 40 minutes/session, with a total of 12-session over 6 weeks. Control group did not receive additional training. Primary outcomes included obstacle walking, and brain activities (prefrontal cortex (PFC), premotor cortex (PMC), and supplementary motor area (SMA)) during obstacle walking by using functional near-infrared spectroscopy. Secondary outcomes included obstacle crossing, timed up and go test (TUG), cognitive function in different domains, and fall efficacy scale (FES-I).

RESULTS

The motor-cognitive training group demonstrated greater improvements in obstacle walking speed and stride length, SMA activity, obstacle crossing velocity and stride length, digit span test, and TUG than the control group. The complex walking training did not show significant improvement in obstacle walking or change in brain activation compared with control group. However, the complex walking training resulted in greater improvements in Rey-Osterrieth Complex Figure test, TUG and FES-I compared with the control group.

CONCLUSIONS

Our 12-session of the cognitive-motor training improved obstacle walking performance with increased SMA activities in people with PD. However, the complex walking training did not lead such beneficial effects as the cognitive-motor training.

CLINICAL REHABILITATION IMPACT

The cognitive-motor training is suggested as an effective rehabilitation program to improve obstacle walking ability in individuals with PD.

Key words:

Walking
Psychomotor performance
Parkinson’s disease
National Science and Technology CouncilMOST109-2314-B-010-035-MY3 National Health Research Institutes NHRI-EX-111-10913PI
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pmcParkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Several studies have shown that PD not only affects motor functions, but also cognitive functions, including executive function, attention and visuospatial abilities.1-3 Both motor and cognitive symptoms may increase the fall risk in individuals with PD.4 It is estimated that up to 70% of people with PD experience falls, with the highest proportion of tripping over obstacles during walking.5 Stegemöller et al. reported that people with PD demonstrated poor obstacle crossing performance compared with healthy control.6 Obstacle crossing while walking (obstacle walking) requires motor planning and motor execution according to the information from external environments.7 Therefore, apart from the challenges of motor performance, attention and executive function have been reported to influence the obstacle walking performance.8, 9 In addition, the visuospatial ability affects the obstacle crossing performance during walking.10 Therefore, once the cognitive resources cannot be effectively recruited during obstacle walking, the risk of fall will increase.8 To date, most studies mainly investigated the obstacle crossing or the obstacle walking while stepping over a single obstacle, which may not truly reflect the complexity of daily activities.6 Previous study has reported stepping over multiple obstacles requires more attention and increases the risk of tripping in both people with PD and healthy individuals.11 Therefore, a multi-obstacle walking assessment was designed as a test in this study to reflect the complexity and challenge of crossing obstacles during walking.

The neural control of obstacle walking seems to be mediated by various regions in the brain, including the frontal lobe. The prefrontal cortex (PFC) is involved in planning, execution, and adjustment to the environment of walking.12 Previous study using functional near-infrared spectroscopy (fNIRS) demonstrated greater PFC activity during obstacle walking in people with PD compared to older adults,13 suggesting that people with PD need more cognitive activations to maintain obstacle walking performance. On the other hand, the premotor cortex (PMC) and supplemental motor area (SMA) are involved in adapting walking speed and posture.14 Recent studies have reported increased PMC and SMA activity in people with PD during various walking tasks, including dual-task walking and obstacle walking.14, 15 However, if the DLPFC, SMA and PMC capacity failed to compensate the needs of the obstacle walking, people with PD may demonstrate a poor gait performance in consequence.15 Therefore, in this study, the PMC, SMA, and PFC activity were measured by using fNIRS to investigate movement planning and postural control of locomotion during obstacle walking after training to document the underlying brain changes.

Exercise interventions can modulate neuroplasticity in individuals with PD, and training combined with visual or attentional cuing could promote the engagement of cognitive circuits to improve performance on complex walking tasks.16 Obstacle walking involves both cognitive and motor functions in people with PD,7, 17 highlighting the importance that an effective training program should include both components. However, Maidan et al.’s study showed virtual reality (VR) combined treadmill training did not improve obstacle walking performance.18 Therefore, effective training programs are needed to improve such complex walking ability in individuals with PD. To optimize recruitment of motor and cognitive networks, we designed two novel training programs, complex walking training and cognitive-motor training, both including executive function, attention, visual and motor challenges. The complex walking training was innovated by trail-walking test.19 During such complex walking situations, people with PD need more motor and cognitive resources to change walking direction as compared with relatively simple straight walking.20 For the cognitive-motor training, the design was to integrate the Stroop test with exercises. Previous study demonstrated the Stroop test assessing the inhibitory control of executive function which was related to gait adaptability during obstacle walking in individuals with PD.21 Present study aimed to explore the effects of these two novel trainings on obstacle walking performance and cortical activities in individuals with PD. We hypothesized both would improve obstacle walking coupled with increasing brain activities in people with PD.

Materials and methods

Subjects

This study protocol was approved by the Institutional Review Board of National Yang Ming Chiao Tung University. This trial was registered at https://www.clinicaltrials.in.th/ (TCTR20210420003) and conformed to the CONSORT checklist. The inclusion criteria were: 1) ages between 50 and 80 years old; 2) diagnosed with idiopathic PD which was confirmed by the neurologist; 3) Hoehn and Yahr stage (H&Y) I-III; 4) taking stable PD medications for at least 3 months prior to the participation; 5) a score of ≥24 on the mini-mental state examination (MMSE). Exclusion criteria were: 1) any musculoskeletal, orthopedic, psychiatric disease or neurological diseases other than PD; 2) severe visual (e.g., color blindness) or other impairments that might interfere with participating the present study. All participants were informed about the study protocols and signed a written consent form.

Experimental design

This study was a single-blind (assessor blinded) randomized controlled trial with three parallel designs, and the blocked randomization was generated via sealed envelope selected by a person not involved in the study. Characteristic data including age, gender, height, weight, educational level, the more affected side, equivalent daily doses of levodopa (LEDD), MMSE score and MDS-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor score were obtained at baseline. All outcomes were measured before intervention (pre-test) and after completing the intervention (post-test) by the same assessor who was blinded for the group assignment. The interventions were conducted for 12 sessions (2 sessions per week for a 6 weeks) by the same physical therapist for each group. All assessments and interventions were conducted while participants were in the “on-phase” of medication.

Intervention

Complex walking group

The training program began with warm-up exercises for 5 minutes followed by 30-minute of complex walking training, and ended with cool-down exercises for 5 minutes. The complex walking training was developed according to Mitchell et al.’s steering task to challenge the balance and walking.22 In addition, we integrated the Chinese version of the trail making test into steering walking to challenge the cognition performance.23 The program consisted of three levels, detailed as follows: In level I, participants walked with the steering trajectory by the same color of cones (red or yellow). In level II, participants walked with the steering trajectory according to the number of cones consecutively (1-2-3...). In level III, participants walked with the steering trajectory according to the number of cones and Chinese zodiac signs in sequence (1-rat, 2-ox, 3-tiger...). Figure 1 illustrates the different levels of walking trajectory during complex walking training.

Figure 1 —Experimental setup of complex gait training. The solid arrow illustrates walking trajectory: A) level I; B) level II; C) level III.

Changing the placements of cones, reducing the distance between cones, and increasing the number and different colors of the cones were administered to increase the difficulties in each level. If participants could smoothly complete a level, they would progress to next level. During training, participants walked with their comfortable speed during training and a physical therapist followed behind for safety concerns.

Cognitive-motor group

We designed the motor-cognitive training program based on previous studies that revealed the importance of cognitive function, muscle strength and balance in obstacle walking.8, 9, 24 The training program consisted of 5-minute warm-up, 15-minute of motor training, 15-minute of motor combined cognitive training, and 5-minute cool-down. The motor training section consisted of resistance, balance and gait training (including walking forward, walking backward and tandem walking). The training difficulty was adjusted by weight of resistance, number of repetitions, base of support and using a foam during balance training, according to participant’s ability and performance. In the motor combined cognitive training section, we integrated Chinese version of the Stroop color and word test into the ankle exercises,23 which demands a high attention and inhibition to incongruent stimuli. The sitting position was adopted during the motor combined cognitive training since the Stroop effect was greater when sitting compared to a standing position.25 The participants sat on a chair and focused on a screen placed in front of them. The screen showed a set of footprints, as shown in Figure 2.

Figure 2 —Examples of motor combined cognitive training on the screen. The dashed arrow indicates the trajectory of footprints: A) for level I, participants performed ankle dorsiflexion when the footprints slid down and fitted into the contours; B) the level II and III were similar to level I, but participants performed ankle dorsiflexion only when the meaning of the word (RED) was as same as the color of the ink (red); otherwise, the ankle should remain at rest when the meaning of the word (RED) was printed in other colors (e.g. green or blue).

In level I, participants were instructed to do the ankle dorsiflexion, when the footprints fell and fitted into the contour on the screen. In level II, two colors words randomly appeared in the contours. When the meaning of the word was as same as the color of the ink (e.g., the word “Red” written in red), participants needed to do the ankle dorsiflexion once the footprints slid down and fitted into the contours. In level III, more than three colors words randomly appeared. Participants performed ankle dorsiflexion once the footprints slid down and fitted into the contours when the meaning of the word was as same as the color of the ink. During training, only when the footprints slid out of the screen can subjects relax the dorsiflexors. Based on the subjects’ performance, we adjusted the training difficulty, including the speed of the footprints and the number of colors. Once the participants completed the level with an 80% success rate, they would progress to the next level.

Control group

Participants in the control group did not receive additional training and were asked to maintain their daily activities. The researcher contacted participants weekly by phone calls during the intervention period.

Outcome measures

The primary outcomes were obstacle walking performance and brain activities during obstacle walking task. The secondary outcomes included obstacle crossing performance, cognitive function in different domains, functional mobility and fear of fall.

Obstacle walking performance

To assess the obstacles walking performance, we used wireless sensors (Physiolog®5 from GaitUp system, Lausanne, Switzerland) attached to participants’ shoes. Previous study has shown that the instrument has a good test-retest reliability (intra-class correlation coefficient, ICC= 0.87-0.979).26 Ten obstacles were placed on a 30 m corridor (Supplementary Digital Material 1: Supplementary Figure 1). The interval between each obstacle was 3 meters and the height of obstacles was 20% of the participant’s leg length to emulate the height of a stair or curb.24 The participants were asked to step over obstacles at their preferred speed back and forth for 1 minute with two physiotherapists stand by for safety. The obstacle walking test was repeated four times, and the average was used in the data analysis. The gait parameters of interest included gait speed, stride length and cadence.

Brain activity during obstacle walking

The present study used a fNIRS system (NIRSport2, NIRx Medical Technologies LLC, Glen Head, NY, USA) to monitor hemodynamic response, indicating the brain activity during obstacle walking. Brain activity was measured at the wavelengths of 760 nm and 805 nm and 8 LED sources and eight detectors were attached on the fNIRS cap wearing on participant’s head. The placement of optodes was based on the international 10-5 system with interoptode distance approximately 3.0 cm between every two adjacent positions. The sources and detectors were placed over PFC, PMC and SMA.14 Data were recorded with a sampling rate of 7.81 Hz. Before measurement, a calibration determined the amplification factor and quality of signal. To proceed the measurement, the quality of signal indicated by fNIRS software should be “excellent” or at least “acceptable”. During the experiment, we used a black overcap to avoid the interference of ambience light.

The fNIRS data were preprocessed using Homer 2 package (Matlab, version 2.8, R2013b, MathWorks) for analysis. The procedures of signal pre-processing were applied following our previous study.14 To check the data quality, the data were removed based on the coefficient of variation (CV) if CVchan >15% and CVtrail>10%.14 Then, to eliminate the physiological effects of heartbeat, respiration, and low frequency signal drifts for each wavelength, the signals were bandpass-filtered between 0.01 Hz and 0.15 Hz. Next, the motion artifacts were removed through wavelet filtering. The removal threshold of the wavelet coefficient was set to 0.1 in this study. The HbO concentrations were calculated using the modified Beer-Lambert law for each channel.27 To improve the signal quality, we applied the correlation-based signal improvement method to remove remaining artifacts. We used the HbO concentrations collected 5 seconds before each trial as a baseline14, 18 to calculate the HbO differences between resting and walking conditions (5-40 seconds after starting walking).28

Obstacle crossing performance

The obstacle crossing performance was measured using a GAITRite system (CIR system, Inc., Havertown, Pennsylvania). The participants required to walk and cross an obstacle at their comfortable speed for five times. An obstacle was placed in the middle of a 4.75 m long and 0.89 m wide GAITRite walkway and the height of the obstacle was 20% of subject’s leg length. The first leg that crossed over the obstacle was defined as the leading leg (LL) and the following leg as the trailing leg (TL).24 The outcome variables were crossing stride length, crossing stride velocity and step width for each leg.24 The average of the 5 obstacle crossing trials were used for data analysis.

Cognitive function

Measurements of cognitive function included global cognition, executive function, attention, and visuospatial ability. The global cognition was assessed using the Montreal cognitive assessment (MoCA). A higher score indicating better global cognitive function.29 The executive function was assessed with semantic verbal fluency task (VFT) with the fruit and houseware categories. The participants were asked to name a specific category as many as possible in 1 minute.30 We recorded the total number of items that participants named in these two categories and more named items indicate a better executive function. The attention was obtained from the digit span test (DST), including forward and backward DST.31 The score was based on total number of successful trials and a higher sore indicating a better attention performance. Rey-Osterrieth complex figure (ROCF) was used to assess the visuospatial ability. Participants were asked to copy a complex figure, consisted of 18 elements. Each element was scored 0.5, 1 or 2 points depending on its accuracy and location. Higher scores indicate a better visuospatial ability.32

Functional mobility

The timed up and go (TUG) test was used to evaluate the functional mobility. During the test, participants sat in a standard chair without arm rest, stood up, walked for 3 meters, turned around, returned to the chair, and sat down. The time needed to complete the task was recorded, and shorter time indicates a better functional mobility. A high reliability of TUG test has been reported in individuals with PD.33

Fear of fall

The falls efficacy scale international (FES-I), which test-retest reliability was high (ICC >0.9) in PD, was used to assess participant’s concern of falling.34 There are 16 items assessing functional tasks and social-related activities on a scale of 1 to 4, which had a total score ranged from 0 to 64, with higher scores indicating more concerns about falling.

Sample size

We calculated the sample size with a priori power analysis using G*power v3.1.9.7. A repeated measure with between factors ANOVA was used for the statistical test. Since there was no available data for parameter estimation of obstacle walking test, we then chose the obstacle crossing performance to estimate the effect size for sample size calculation.35 A total of 27 participants was suggested to be enough to detect a significant difference in obstacle crossing performance, with effect size f of 0.564, power of 0.80 and a two-tailed alpha level of 0.05. Allowing for an estimated 15% attrition rate, we recruited a total of 32 participants.

Statistical analysis

All analyses were performed using SPSS 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean and standard deviation, number, or median [interquartile range]) were calculated for all variables. The Shapiro-Wilk Test was used to assess normal distributions. The inter-group differences in baseline characteristics were analyzed using one-way ANOVA for continuous variables or χ2 Test for nominal scales. Intention-to-treat analysis was used for missing data in the post-test. Because of the results of the Shapiro-Wilk Test showed that most of the outcomes were not normally distributed. We used the generalized estimating equation (GEE) model for repeated measures to analyze the effect of time, group, and time × group interaction. The GEE model was used to evaluate the differential changes in each outcome across the time points between the three groups. Differential changes in each outcome were assessed using the regression coefficient (B) of the group × time interaction terms in the model. In addition, we performed pairwise comparisons of the GEE analyses with post-hoc Bonferroni correction to estimate the adjusted P value for within-group changes. The effect size was calculated using the formula of Cohen’s d formula, to evaluate the magnitude of the differences in changing values.36 A Cohen’s d value of 0.2 indicates a small effect size, 0.5 indicates, moderate effect size, and 0.8 indicates a large effect size.37 Statistical significance was set at P<0.05.

Results

Thirty-four subjects were screened for eligibility, and 32 participants were included and randomly assigned to the complex walking group (N.=11), cognitive-motor group (N.=11), or control groups (N.=10) (Supplementary Digital Material 2: Supplementary Figure 2). No significant differences between groups were found in baseline demographic characteristics (Table I) or in the outcome measures at the pre-intervention assessment.

Table I —Demographic characteristics of participants (N.=32).

Group	Complex walking group (N.=11)	Motor-cognitive group (N.=11)	Control group (N.=10)	P value	
Age (years) a	65.86±5.60	67.63±6.92	64.94±4.98	0.575*	
Gender (M/F) b	6/5	7/4	6/4	0.909†	
Education level b				0.663†	
6-12 years	6	4	4		
>12 years	5	7	6		
H&Y stage b				0.957†	
1-1.5	5	7	5		
2-2.5	5	3	4		
3	1	1	1		
Disease duration (years) c	5.0 (1.6; 9.0)	3.0 (1.0; 7.5)	5.4 (2.8; 7.0)	0.641‡	
More affected side (L/R) b	6/5	7/4	8/2	0.464†	
MDS-UPDRS-III a	33.82±13.34	31.45±11.99	25.40±11.63	0.294*	
LEDD (mg/day) a	669.45±255.46	443.09±246.43	678.00±405.8	0.166*	
MMSE c	29 (27; 30)	28 (26; 30)	28 (26; 29)	0.638‡	
H&Y: Hoehn-Yahr; L: left; R: right; MDS-UPDRS-III: MDS-unified Parkinson’s disease rating scale part III; LEDD: levodopa equivalent daily dosage; MMSE: Mini-Mental State Examination. a Values are the mean±SD; b values are number; c values are the median (Q1;Q3); *analyzed using one-way ANOVA; † analyzed using Chi-square Test; ‡ analyzed using Kruskal-Wallis one-way analysis of variance by ranks.

Obstacle walking performance

The GEE model demonstrated a significant interaction effects between time and group on obstacle walking speed (Wald χ2(2)=8.300, P=0.016) and stride length (Wald χ2(2)=7.494, P=0.024) (Supplementary Digital Material 3: Supplementary Table I). The parameter estimates of the model revealed that the cognitive-motor group showed significant improvements in obstacle walking speed (β=11.784, P=0.017), and stride length (β=8.033, P=0.026) compared with the control group. Compared with the complex walking group, the obstacle walking speed improved more in the cognitive-motor group (β=6.473, P=0.027). For the pre- and post- differences, the pairwise comparisons from the GEE models revealed that only the cognitive-motor group had significant improvements in the obstacle walking speed (P<0.001) and stride length (P<0.001) after 12 training sessions (Supplementary Digital Material 4: Supplementary Table II).

Brain activity during obstacle walking

The GEE model demonstrated a significant interaction effects between time and group in SMA activation (Wald χ2(2)=9.971, P=0.007) (Supplementary Table I). The interaction effect indicated that the cognitive-motor group showed significantly greater increase in activation of the SMA than the complex walking group (β=0.083, P=0.031) and control group (β=0.135, P=0.002). However, only the cognitive-motor group showed a significant increased activation in SMA (P=0.038) after training (Supplementary Digital Material 5: Supplementary Table III) according to pairwise pre- and post-comparison.

Obstacle crossing performance

GEE statistical analysis showed a significant time-group interaction on velocity of the LL (Wald χ2(2)=6.241, P=0.044) and the TL (Wald χ2(2)=6.796, P=0.033), and stride length of the TL (Wald χ2(2)=7.887, P=0.019) (Supplementary Digital Material 6: Supplementary Table IV). The parameter estimates of the model showed that the cognitive-motor group showed a significantly greater increase in velocity (β=11.762, P=0.013) and stride length (β=11.561, P=0.006) of the LL and velocity of the TL (β=13.079, P=0.010) compared with the control group. However, there were no significant differences between the complex walking group and control groups. Pairwise pre- and post-comparisons from the GEE revealed only the cognitive-motor group showed significant improvements in speed (P<0.01) and stride length (P<0.01) of both the LL and TL after training (Supplementary Digital Material 7: Supplementary Table V).

Cognitive function

There were significant time-group interactions in DST (Wald χ2(2)=10.544, P=0.005) and ROCF (Wald χ2(2)=11.035, P=0.004) (Supplementary Digital Material 8: Supplementary Table VI). The cognitive-motor group demonstrated a significantly greater improvement in DST than the control group (β=1.991, P=0.001) and complex walking group (β=1.273, P=0.032). The complex walking group showed a significantly greater improvement in ROCF than the control group (β=3.718, P=0.001) and cognitive-motor group (β=-3.466, P=0.014). The cognitive-motor group showed significant improvements in DST (P=0.001) and VST (P=0.022) after training according to pre- and post- comparisons (Supplementary Digital Material 9: Supplementary Table VII). Furthermore, a significant improvement in ROCF (P=0.002) was observed in the complex walking group after training.

Functional mobility and fear of fall

GEE statistical analysis showed a significant time-group interaction in TUG (Wald χ2(2)=8.764, P=0.012) and FES-I (Wald χ2(2)=10.229, P=0.006) (Supplementary Table VI). The cognitive-motor group showed a significantly greater increase in TUG (β=-2.235, P=0.003) compared with the control group. The complex walking group had a significantly greater improvement in TUG (β=-1.863, P=0.017) and FES-I (β=-6.582, P=0.002) than the control group. Pairwise comparisons from the GEE revealed that both the complex walking group and cognitive-motor group showed significant improvements in TUG (Pcomplex walking group=0.046; Pcognitive-motor group<0.001) after 12 training sessions (Supplementary Table VII).

Discussion

In this single-blind randomized control trial, we demonstrated that 12-session of cognitive-motor training was effective in people with PD in improving obstacle walking performance coupled with increased activations in SMA compared to control group. Furthermore, we noted the cognitive-motor training led to a significant improvement in attention, while complex walking training led to a significant improvement in visuospatial ability. However, the complex walking training did not improve the obstacle walking performance significantly.

Improving obstacle walking is important for individuals with PD to walk safely and independently in the community. Our results showed that cognitive-motor training had large-sized improvements compared to control group in improving obstacle walking performance in people with PD. Mirelman et al. demonstrated that VR obstacles training on treadmill led to significant improvements of obstacle walking speed and stride length in people with PD.38 Interestingly, our participants did not practice obstacle walking, but walked faster with larger stride length during obstacle walking after cognitive-motor training. According to a recent meta-analysis, motor-cognitive training can improve dual-task walking performance in individuals with PD.39 Taken together, the positive effects of motor-cognitive training seem to be able to transfer to the untrained, challenging walk tasks (e.g., obstacle walking or dual task walking). In addition, a recent exercise guideline for gait performance has recommended multidimensional physical exercise (including balance and gait training), as well as lower extremities resistance training to improve gait speed and stride length.40 Thus, the improvements in obstacle walking performance in present study might be also attributed to the motor training component of cognitive-motor training. Furthermore, a previous research suggested that the tibialis anterior (TA) should be integrated in exercise programs aimed to improve obstacle walking in older adults.41 Although our cognitive training section includes both cognitive training and ankle dorsiflexion training, we did not assess the improvements of control ability or muscle strength of the TA. Thus, future studies are recommended to determine the contribution of TA to obstacle walking performance.

Regarding brain activity changes, we found the increase in SMA activities after the cognitive-motor training. The SMA is a key structure for the planning and execution in complex walking tasks.42 A recent electroencephalography study showed that people with PD required more SMA integration to overcome obstacle walking compared to usual walking.43 Based on our results, the improvement of obstacle walking performance after motor-cognitive training may at least be explained by increased recruitment of the SMA. Therefore, mild to moderate PD individuals as our participants, could respond to the specific designed motor-cognitive training by cortical plastic changes in the motor-related areas. On the other hand, the PFC activity did not change significantly after motor-cognitive training during obstacle walking. However, the attention ability improved after such training. We do not know whether the cognitive-motor training enhanced the PFC efficiency44 or the additional cognitive resources were not required during obstacle walking due to improved attention ability.

In addition to obstacle walking, the cognitive-motor training improved the obstacle crossing performance. Liao et al.’s study showed that lower extremity muscle strength and balance control influenced obstacle crossing performance in individuals with PD.24 In this study, we further demonstrated incorporating 15-min of resistance training and balance training for 12 sessions could improve the obstacle crossing ability in people with PD. The improvement in TUG should also be noted after such training program.

To our surprise, the complex walking training did not exert positive effects on obstacle walking performance. Two possible reasons may explain such finding. First, adding the cognitive challenges during steering walking in our complex walking training task led participants to walk slowly. Such slow walking speed may not provide enough motor challenges to improve the obstacle walking performance.45 Second, Yamada et al. reported 24 sessions of the obstacle negotiation exercise can effectively reduce the incidence of falls in the community elderly,46 and more training sessions resulted in better effect on obstacle negotiation in the elderly according to a systematic review.47 Although the optimal training sessions were not known for people with PD, the proposed complex walking training seems to be insufficient to improve the obstacle walking performance.

It also draws our attention that the cognitive-motor training and complex training exerted positive effects on attention and visuospatial ability in people with PD, respectively. The different effects could be attributed to the various features of training programs. The cognitive-motor training required participants to focus on the screen and performed the correct movement accordingly, and leading to a significant improvement of attention. Previous study reported that attention capacity is essential for older adults to detect environmental changes and cross obstacles successfully.48 We thus speculate that improvements in attention after cognitive-motor training play an important role in improving obstacle walking and obstacle crossing performance in present study. In the complex walking training group, participants had to identify the position of cones, adapt their walking path and plan the traveled routes according to the given information. Such training process may directly increase visuospatial function. Previous study revealed deficits in visuospatial function were associated with gait dysfunction in PD, especially in postural instability and gait disorder (PIGD) phenotype.49 The improvements in visuospatial function and TUG after complex walking training may illustrate the importance of visuospatial function in functional mobility.

Although the FES-I score did not change significantly after both training programs, we noted 1 out of 3 (complex walking training group), 3 out of 4 (cognitive-motor training group), and 0 out of 2 (control group) were no more identified as high risk fallers according to the TUG test.50 Therefore, the beneficial effects of both training programs on fall risks in people with PD warrant further studies.

Limitations of the study

There are some limitations in the present study. First, the effects were only measured after training. Further research is suggested to evaluate the long-term or lasting effects of the cognitive-motor training. Second, the nature of passive control group may eliminate further interpreting the effects of our novel training programs. Third, the present findings may be limited to the PD population with H&Y stage I to III with the ability to complete the obstacle walking assessment. Finally, it might have the potential bias in the study due to the inability to blind the participants to the group assignment.

Conclusions

This randomized, single-blind controlled trial demonstrated that a 12-session of the cognitive-motor training improved obstacle walking performance with increased SMA activities in people with PD. However, the complex walking training did not lead such beneficial effects as the cognitive-motor training. The cognitive-motor training is suggested as an effective rehabilitation program to improve obstacle walking ability in individuals with PD.

Supplementary Digital Material 1

Supplementary Figure 1

Setup for testing obstacle walking performance.

Supplementary Digital Material 2

Supplementary Figure 2

Flowchart of the study procedures.

Supplementary Digital Material 3

Supplementary Table I

GEE models for comparison of obstacle walking performance and HbO2 level between groups across time.

Supplementary Digital Material 4

Supplementary Table II

Obstacle walking performance before and after different training.

Supplementary Digital Material 5

Supplementary Table III

HbO2 level during obstacle walking before and after different training.

Supplementary Digital Material 6

Supplementary Table IV

GEE models for comparison of obstacle crossing performance between groups across time.

Supplementary Digital Material 7

Supplementary Table V

Obstacle crossing performance before and after different training.

Supplementary Digital Material 8

Supplementary Table VI

GEE models for comparison of other secondary outcomes between groups across time.

Supplementary Digital Material 9

Supplementary Table VII

Other secondary outcome measurements before and after different training.

Acknowledgements

The authors would like to thank the participation of all subjects.

Conflicts of interest: The authors certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

Funding: This work was supported by grants from the National Science and Technology Council (MOST109-2314-B-010-035-MY3 ) and National Health Research Institutes (NHRI-EX-111-10913PI ).
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