The DUALITY trial, a large-scale multicenter study on the effects of dual-task (DT) training in people with Parkinson disease (PD), recently showed that 2 different training programs had equally positive effects on DT motor and cognitive performance without increasing fall risk.1 In this trial, consecutive task training (CTT, training 2 tasks separately) was contrasted with integrated task training (IDT, practicing 2 tasks simultaneously). Both regimens were assumed to improve DT outcomes via different mechanisms: (1) by increasing single-task automaticity or fluidity (CTT) and 2) by improving actual task integration (IDT). It was also expected that in IDT a better retention of practice would be found at the expense of a higher fall risk, a hypothesis that was not confirmed. These very similar outcomes of training bring up a clinical dilemma, namely how clinicians can determine the best training approach for each individual. Therefore, this study aimed to inform clinical decision-making by increasing insight into DT training responses.
Thus far, a first exploratory analysis has been conducted to compare the training response of subgroups based on dichotomous scoring of the presence of falls, gait freezing, Hoehn and Yahr (H&Y) stage II or III, or having cognitive impairment (a cutoff score at the MoCA of 27). Results showed that no differences were apparent between these subgroups.1 Cross-sectional studies in participants with PD show that DT gait velocity, irrespective of training, is predicted by several motor, cognitive, and personal factors.2 Our own recent study identified the combination of single-task (ST) gait velocity and the score on the Alternating Intake Test, a test of executive function, as the key factors determining DT gait velocity in PD.3 In persons with stroke, improvement in gait velocity after training has been found to be associated with age and gait velocity at the beginning of the intervention.4 In older individuals, both ST and DT variabilities were important in predicting DT practice effects.5 Furthermore, executive function (inhibitory control, mental set shifting, and attentional flexibility) appeared to be an important factor determining changes in ST mobility after 12 months of intervention.6 To date, no studies have been performed in participants with PD to identify factors that determine the size of DT training effects.
Cognitive dysfunction has previously been found to relate to learning deficits in people with PD.7–9 Individuals with freezing of gait (FOG) were shown to suffer from more severe cognitive dysfunction10–12 and consequently to experience greater deficits in learning than nonfreezers.11 , 13 , 14 The impaired learning profile of persons with FOG as well as the fact that the frequency of FOG is known to increase under DT conditions15 predicts that IDT may be less effective in this subgroup of individuals. In addition, IDT may be contraindicated for individuals in the later disease stages and with a higher fall risk to guarantee safety during training.12 , 16–18
The main aim of this study was to identify which factors determine the change in DT gait velocity after DT training. Based on previous studies in other groups, gains in DT performance were hypothesized to be lower in those with initial slower gait velocity, more FOG, more advanced disease stage and age, and a higher prevalence of falling. The second aim of this study was to assess whether treatment benefits depended on the type of DT training strategy that was used. As attentional resources were challenged to a greater extent in IDT, it was expected that participants with cognitive problems, including those with FOG, and participants with an increased risk of falling would benefit less from this type of training.
This study presents results from 121 participants included in the DUALITY trial conducted in 2 clinical centers in Belgium and the Netherlands (registered on clinicaltrials.gov as NTC01375413).19 Inclusion criteria were (a) diagnosis of PD according to the UK Brain Bank criteria20; (b) H&Y stage 3 or less in the subjective best “on” phase of the medication cycle21; (c) being able to walk for 10 minutes continuously; (d) the presence of DT interference as established by a structured checklist19; (e) a score 24 or more on the MMSE22; (f) stable medication over the past 3 months or stable DBS settings over the past year; and (g) no hearing or visual problems that could cause interference with testing or training. The study was ethically approved in both centers (Belgium: CME KU Leuven, B322201213165/S53419, and the Netherlands: CMO Regio Arnhem-Nijmegen, NL39530.091.12) and participants signed an informed consent form before study participation.
Predictors and Outcome Measures
Testing was performed as described in the previously published protocol of the DUALITY trial.19 All independent variables were derived from the first baseline assessment and included disease descriptors, cognitive scores, and motor scores. In all participants, ST and DT gait velocity (cm/sec) at preferred speed was assessed before and after the intervention by means of the GAITRite Walkway System,23 whereby walking started and ended 1 m before/after the walkway. Two cognitive tasks (ie, a backward digit span task [digit] and an auditory Stroop task [Stroop]) and 1 functional task (mobile phone task, requiring typing in the date of the test on a mobile phone) were used to assess DT performance while walking,19 with the instruction of focusing equally on both tasks. The backward digit span task was trained during both types of dual-task training, whereas both other tasks were untrained to capture the transfer effects of training. Reaction times for these tasks were assessed both under ST and DT conditions. Descriptors included (i) age, (ii) disease duration, (iii) history of recurrent falls in the previous year (yes/no), (iv) levodopa-equivalent dose (LED),24 and (v) presence of a deep brain stimulator (DBS) (yes/no). Cognitive scores consisted of (i) Mini-Mental State Examination (MMSE),22 (ii) Montreal Cognitive Assessment (MoCA),25 (iii) Frontal Assessment Battery (FAB),26 (iv) Scales for Outcomes in Parkinson's Disease-Cognition (ScopaCog),27 (v) Alternating Names Test,28 (vi) Alternating Intakes Test,28 and (vii) ST and DT reaction times on the cognitive tasks at baseline. Motor scores included (i) H&Y stage,21 (ii) Movement Disorders Society Unified Parkinson's Disease Rating Scale—part 3 (MDS-UPDRS-III),29 (iii) New Freezing of Gait Questionnaire (NFOG-Q),30 (iv) Activities-specific Balance Confidence scale (ABC),31 and (v) ST and DT gait velocity at baseline.
Testing was repeated 4 times over a period of 6 months of follow-up and was performed in the gait laboratories of the Neuromotor Research Group at KU Leuven or of the Radboud University Medical Center. Baseline performance was assessed at 2 different times, separated by a control period of 6 weeks. After the second assessment, all participants underwent 6 weeks of DT intervention. Immediately afterward, the training effect was assessed. After 12 weeks of follow-up, a final assessment investigated potential retention effects. Change in gait velocity after training was calculated by subtracting the average DT gait performance during the 2 baseline tests from the DT gait performance during the first postintervention test.
As described in the DUALITY trial protocol,19 for all participants training consisted of 3 identical components: (1) gait practice, (2) auditory cognitive exercise, and (3) functional training. Gait practice involved at-home gait exercises, aimed to improve gait quality. Exercises depended on the clinical need of the individual and included, for example, exercises such as focusing on the taking big steps, focusing on heel strike, turning, and other key parameters of gait. Difficulty level was gradually increased over the training program. Cognitive exercises addressed verbal fluency, discrimination and decision-making, working memory, mental tracking, and reaction time. The intervention included four 30-minute practice sessions per week, of which 2 were performed in the presence of a physical therapist who was specifically trained and experienced in treating individuals with PD. Participants were randomly assigned to either CTT or IDT.1 During CTT, 15 minutes of gait exercises, and 15 minutes of cognitive training exercises, were provided while participants were seated on a chair. In contrast, IDT consisted of motor-cognitive tasks during the entire session. All training sessions were performed while participants where “on” medication.
Data analysis was performed with IBM SPSS, version 22 (IBM Corp, Armonk, New York).32 , 33 Missing data were due to sporadic dropout or technical issues and were not included in the analysis. A regression analysis was performed to examine factors that determine the effect of DT training on the change in gait velocity while performing (1) digit, (2) Stroop, and (3) mobile phone tasks. Following the main regression analysis, an additional subanalysis was performed to identify determinants in both types of training separately. Correlations between determinants and outcome variables were assessed using simple linear regression models.32 , 33 For the main analysis, as well as for the secondary subanalysis, factors were included into a stepwise forward multivariate linear regression model when they were correlated with the outcome variable, with a P value less than 0.20. Variables with a mutual correlation higher than r = 0.70 were not entered into the model to avoid multicollinearity.
Descriptive data are presented in Table 1. No differences were found between the 2 training groups concerning descriptive, cognitive, and motor predictors. As previously reported, the change in DT performance after training did not differ between the groups.1
Change in DT Gait Velocity—Digit in All Participants
Table 2 presents the factors that exhibit univariate correlations at P < 0.20 with the outcome variable, and were included into a stepwise forward multivariate linear regression model. The change in DT gait velocity during the digit span task after training was correlated with the MMSE, MoCA, FAB score, ScopaCog, ST and DT reaction time on the digit span task, MDS-UPDRS-III, NFOG-Q, and ST and DT gait velocity during the digit span task at baseline. Stepwise forward linear regression (Table 3) revealed that lower DT gait velocity during the digit span task at baseline (β = −0.45; P < 0.001) and higher performance on the ScopaCog, reflecting better cognitive functioning (β = 0.34; P < 0.001), related significantly to the change in DT performance after training (R 2 = 0.23; P < 0.001). The differences between the observed and predicted values based on the multiple linear regression analysis are shown in the Figure for the digit span (top), Stroop (center), and mobile phone tasks (bottom).
Change in DT Gait Velocity—Stroop in All Participants
Change in DT gait velocity during the Stroop task was correlated with the MMSE, MoCA, FAB score, ScopaCog, MDS-UPDRS-III, NFOG-Q, and ST and DT gait velocity during the Stroop task at baseline (Table 2). After forward linear regression (Table 3), lower DT gait velocity during the Stroop task at baseline (β = −0.52; P < 0.001), higher ScopaCog performance (β = 0.29; P = 0.002), and lower MDS-UPDRS-III scores, reflecting better motor function (β = −0.25; P = 0.005), remained significant determinants for change in DT gait velocity during the Stroop task after training (R 2 = 0.26; P < 0.001). The difference between the observed and predicted values based on the multiple linear regression analysis is shown in the Figure.
Change in DT Gait Velocity—Mobile Phone in All Participants
Univariate analysis (Table 2) showed that the LED, MoCA, ScopaCog, and DT gait velocity during the mobile phone task at baseline were correlated with change in DT gait velocity during the mobile phone task after training. Forward linear regression (Table 3) revealed that lower DT gait velocity during the mobile phone task at baseline (β = −0.40; P < 0.001), higher ScopaCog performance (β = 0.30; P = 0.002), and higher LED (β = 0.19; P = 0.03) significantly determined change in DT gait velocity during the mobile phone task (R 2 = 0.18; P < 0.001). The difference between the observed and predicted values based on the multiple linear regression analysis is shown in the Figure.
Consecutive Versus Integrated Task Training Group
Pearson correlation coefficients at P < 0.20 resulting from the univariate analysis in consecutive and integrated task training groups are shown in the Appendix. In the CTT group, significant determinants of change in DT gait velocity during the trained digit span task (Table 4) were lower baseline DT gait velocity during the digit span task (β = −0.37; P = 0.007) and higher performance on the ScopaCog (β = 0.31; P = 0.021) (R 2 = 0.14; P = 0.007). Results for gait velocity were similar in the IDT group (R 2 = 0.38; p < 0.001) (β = −0.51; P < 0.001). However, in this group higher MoCA scores (β = 0.45; P < 0.001) instead of performance on the ScopaCog determined better gains in DT gait velocity during the digit span task.
For the CTT group, change in DT gait velocity during the untrained Stroop task (Table 4) was solely related to a lower DT gait velocity during Stroop at baseline (β = −0.27; P = 0.04) (R 2 = 0.05; P = 0.04). In contrast, in the IDT group change in DT gait velocity was related to lower DT gait velocity during the Stroop at baseline (β = −0.62; P < 0.001) as well as to higher performance on the ScopaCog (β = 0.35; P = 0.004) and lower MDS-UPDRS-III score (β = −0.26; P = 0.03) (R 2 = 0.39; P < 0.001).
In the CTT group, only a higher LED (β = 0.28; P = 0.03) determined change in DT gait velocity during the mobile phone task significantly (R 2 = 0.06; P = 0.03). In the IDT group on the other hand, lower DT gait velocity during the mobile phone task at baseline (β = −0.37; P = 0.006) and higher MoCA score (β = 0.34; P = 0.01) were both significant determinants (R 2 = 0.17; P = 0.003) (Table 4). Overall, cognitive factors played an important part in predicting improvements in the IDT group. It is of note that a higher variance was explained by the prediction models in the IDT group (R 2 ranged between 0.17 and 0.39 compared with between 0.05 and 0.14 in the CCT group).
The main result of the current study was that both motor and cognitive baseline factors contributed to the prediction of DT training gains. More specifically, people with a lower DT gait velocity at baseline and better cognitive function were more likely to experience a greater benefit. In contrast to our hypothesis, this finding was irrespective of the type of training (ie, IDT vs CTT) and type of dual-task outcome.
The predictive effect of the motor and cognitive baseline factors is in line with our hypotheses and previous studies in older individuals and persons with stroke.4 , 6 As well, these results are supported by the relationship between cognition and DT gait velocity and between dual-task deficits and reduced movement automaticity in older individuals and in persons with PD.3 , 34–36 As a result, participants with PD showed a larger increase in frontostriatal networks than healthy controls.37 Participants with PD were previously shown not to be able to activate cerebellar areas involved in dual tasking to the same extent as their healthy counterparts.38 Participants with limited cognitive reserve, in particular, may no longer have the cognitive resources to optimally gain from DT training. As well, cognitive dysfunction has been linked to learning deficits in people with PD,7–9 particularly in participants with FOG.10–14 These individuals have specific alterations in functional connectivity in dual-task-related regions involving the precuneus and the striatum,39 and this may partly explain why cueing is of value for reducing FOG.40 In the current study, FOG severity correlated univariately with the outcome variables, but in contrast to our hypotheses, it was not maintained as a primary factor determining the gains found after DT training. Instead, cognitive function, irrespective of FOG, was a consistent and significant determinant. The fact that the cognitive load was different between the 3 tasks may explain why different factors did not contribute equally to changes in each of the tasks.
Beyond cognition, DT training benefits were also determined by motor factors, including DT gait velocity at baseline and MDS-UPDRS-III scores. Contrary to our hypothesis, people with PD with a low initial gait velocity showed the largest improvements after training. This can be explained by their larger potential to improve compared with people who started at higher levels and by the fact that the training level was adapted specifically to the participants' gait velocity, which was the primary outcome measure of the DUALITY trial. It is also in line with previous work by Strouwen et al3 showing that single-task gait velocity had the highest association with dual-task gait velocity, suggesting that the loss of motor function is the most important explanatory factor of dual-task impairment in PD. In contrast, the higher MDS-UPDRS-III score correlated negatively with the DT training benefits, indicating that people with a greater disease severity were less likely to improve, particularly when performing the auditory Stroop task. This is in line with previous trials investigating the effect of exercise on other outcomes, such as balance and upper limb movements, which indicate that learning effects may be hampered in subgroups with greater disease severity.41 , 42 One potential explanation for this finding could be that individuals with greater disease severity have a higher fall risk, and may as such deliberately walk slower and/or focus less on the cognitive task as they may prioritize safety over improvement under dual-task conditions. Future interventions, including subgroup analyses comparing the effects of interventions on participants with lesser versus greater disease severity, would be extremely valuable in the field of PD rehabilitation. Other factors, such as disease stage, age, and prevalence of falling, did not contribute to the effect size. As for the mobile phone task, LED was an additionally important factor in determining training benefit, as higher medication intake was associated with larger improvements in the CTT group. Both the Stroop and mobile phone tasks were untrained tasks and thus training benefits could be considered as transfer effects. As generalization of learning is specifically impaired in some subgroups of PD,14 this may explain why a higher MDS-UPDRS-III score was found to be related with less DT gains in the untrained Stroop task. Dopamine replacement therapy has been previously demonstrated to reinforce learning in persons with PD,43 , 44 which was confirmed by the current results on the mobile phone task.
The second aim of the current study was to investigate whether the factors determining DT training gains depended on the type of practice. We hypothesized that IDT would demand higher attentional load than CTT.45 Therefore, it was expected that participants with cognitive problems, falling, more advanced disease stage, and FOG would benefit less from integrated compared with consecutive dual-task training. This hypothesis was only partially confirmed. We indeed found that cognitive outcomes were related to training improvements more frequently after CTT than IDT Participants with a higher cognitive status, measured by either MoCA or ScopaCog, showed larger effects after IDT in both trained and untrained tasks compared with participants with lower cognitive scores. In contrast, falling, disease stage, and FOG were not significant in determining benefits of IDT or CTT.
A limitation of the current study is that the explained variance was low, and predicted outcomes corresponded only moderately to the observed outcomes. Importantly, the prediction of the explained variance was higher in the IDT group compared with the CTT group. This finding indicates that DT training gains are also explained by other factors that were not included in this study, particularly in the CTT group. In the DUALITY trial by Strouwen et al,1 it was found that the total number of falls was higher in the CTT than in the IDT group. As such, it is likely that fear of falling could have been a contributing factor explaining the differences in explained variance between groups. Furthermore, a previous study found that depression was important in predicting DT gait interference in PD.2 In addition, other factors such as motivation, activity level, training frequency, and training intensity should be taken into account in future studies predicting DT training effects.
Despite these limitations, the current study offers valuable information regarding the implementation of dual-task training in the rehabilitation of individuals with PD. Lower dual-task gait velocity at baseline and higher cognitive reserve were found to be the most important predictors of the training effect for both IDT and CTT. As factors such as FOG, falling, and disease duration did not contribute significantly to improvements in gait velocity after training, dual-task training should be offered to individuals with various clinical profiles, including those with greatest disease severity. Disease severity measured by the MDS-UPDRS-III did not predict improvements on the trained task after either CTT or IDT and on both trained and untrained tasks after CTT. It had a significant effect only on the untrained Stroop task after IDT. Transfer to untrained tasks was compromised after IDT in patients with lower cognitive functioning, reflecting the inherent greater cognitive challenge related to IDT in comparison to CTT.
DT gait velocity at baseline and cognitive function, as assessed by the MoCA and the ScopaCog scale for global cognitive function, are the most important determinants of the effect of DT training on DT gait velocity. Whereas individuals with PD seem to benefit from consecutive dual-task training regardless of disease severity, their cognitive capacity and UPDRS-III scores are important factors to determine whether they would be eligible for integrated DT training.
We would like to thank all patients who participated in the study.
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