Predicting Motor Learning of an Upper Limb Task Based on Behavioral and Disease-specific Characteristics in Patients With Parkinson's Disease
- Conditions
- Parkinson Disease
- Registration Number
- NCT06738290
- Lead Sponsor
- KU Leuven
- Brief Summary
Manual dexterity deficits and loss of motor automaticity are commonly seen in patients with Parkinson's disease (PD) (Jankovic 2008; Wu, Hallett, and Chan 2015). Amongst these problems, using a touchscreen is becoming increasingly burdensome. In addition, a variety of non-motor symptoms, including cognitive impairment have a significant impact on the quality of life of patients with PD (Jankovic 2008) which will also affect the use of mobile devices. As the degradation of dopaminergic neurons in the striatum results in an impaired capacity for motor learning and more specifically for consolidation in motor memory (Lehéricy et al. 2005; Wu, Liu, et al. 2015), the investigators want to unravel the remaining rehabilitation potential in this patient group. Recent work on the consolidation of an intensive writing training program from our group, indicated inconsistent and variable results on retention of learning gains, which strongly depended on the clinical profile of the patients involved (Heremans et al. 2016; Nackaerts et al. 2016). This raises the question whether it is possible to predict different training responses and better understand how this is determined by clinical characteristics, such as disease severity and the degree of cognitive impairment. Also, baseline task performance and early acquisition may determine long-term learning outcomes. Our main focus (primary dependent outcome) is to know which patients will be able to retain the learning gains after 4 weeks without training. For this aim, the investigators will use a home-based training program of a touchscreen task called the Swipe-Slide Pattern (SSP) task. Training of this SSP-task will be offered on a tablet under single and dual task conditions in a random fashion, not only to provide variation but also to increase the cognitive challenge, thereby stimulating consolidation (Pauwels et al. 2015; Sidaway et al. 2016). A crucial factor, which may affect the success of training, is compliance (Schootemeijer et al. 2020), which the investigators will measure objectively for the first time. As independent variables the investigators will measure several motor and cognitive functions as well as compliance, while including a broad sample of PD patients.
- Detailed Description
In order to achieve personalised rehabilitation in PD, it is imperative to understand which factors predict whether a patient will benefit from targeted training. To date, only few studies have looked into the predictors of gait and balance training and identified cognitive function at baseline and initial motor performance as the most important determinants (Löfgren et al. 2019; Strouwen et al. 2019). However, these studies focused on global cognitive function, while cognitive subdomains, such as executive function and memory, which are most affected in PD (Muslimović et al. 2005), likely impact more seriously on learning (Olson, Lockhart, and Lieberman 2019). In addition, patients with cognitive impairments were excluded in these studies. Hence, little is known about how non-motor symptoms, which may also include apathy and depression, interact with the capacity to retain learning in PD.
So far, the determinants of motor learning retention have only been examined as secondary analyses of effect studies in PD. Hence, this study aims to identify predictive factors, including motor and non-motor symptoms, for sustained motor learning in a wide and large PD cohort. The primary dependent outcome is to identify which patients are able to retain the learning gains after 4 weeks without training.
Given the impaired touchscreen skills and the espoused difficulties with retention (Nackaerts et al. n.d.; De Vleeschhauwer et al. 2021), training of touchscreen sliding motions will be delivered on a tablet enabling practice in the home setting. Both single (ST) and dual task (DT) conditions will be offered in a random order and feedback will be provided to enhance motivation and retention (Nackaerts et al. 2016; Nieuwboer et al. 2009). This project aims to identify the predictive factors for effect maintenance after receiving this two-week training program. The investigators will recruit a broad cohort of PD patients. Sample size was estimated, using a squared multiple correlation coefficient of 0.17 based on a prediction model from earlier work, which included DT-motor performance and cognitive function as determinants (Strouwen et al. 2019). Using an α = 0.05 and β = 0.20, sample size was calculated for a linear multiple regression: fixed model, R² deviation from 0. Although the number of predictors is dependent on possible multicollinearity, sample size was computed on the assumption of 10 predictors. Sample size was estimated to be 89 and after accounting for a dropout of 20%, 107 patients will have to be included. Importantly, this sample size is also supported by the rule of thumb to include 10 participants per predictor, suggested by Harrell et al. (1996) (Harrell, Lee, and Mark 1996). As the investigators expect to include 10 predictors in our multiple linear regression model (see below), a total number of 100 PD patients will be recruited. The investigators will enroll a wide range of cognitive profiles. However, all patients need to be able to follow instructions and engage in the motor learning. During the instructions of how to perform the touchscreen task, the investigators will assess eligibility pragmatically. The investigators will exclude PD-dementia using level I MDS-diagnostic criteria (Dubois et al. 2007). All patients will train the SSP-task for a duration of two weeks. On day 1 (T0), all participants will undergo an extensive screening session, including motor and non-motor tests performed at the subject's home or in a quiet room in our laboratory at KU Leuven, according to patients' preference.
The global cognitive screening will consist of the Montreal Cognitive Assessment (Nasreddine et al. 2005). Moreover, 2 specific tests will assess each cognitive subdomain. Attention and working memory are captured by the digit and visual span forward and backward test (Blackburn and Benton 1957). The trail making and alternating names tests will be used for executive function (Hyde and Fritsch 2011; Llinàs-Reglà et al. 2017). Visuospatial function will be examined using the short form of the Benton's judgement of line orientation and the Rey Osterrieth Complex figure (Gullett et al. 2013; Poreh and Shye 1998; Winegarden et al. 1998). The 30-min recall of the latter test will also be used to assess memory, together with the Rey Auditory Verbal Learning test (Poreh and Shye 1998; Schmidt 1996). The Boston naming test and the Animal fluency test of the Controlled Oral Word Association test will be used to assess language (Kaplan, Goodglass, and Weintraub 1983; Tombaugh, Kozak, and Rees 1999). Other non-motor features, such as anxiety, depression, and sleep quality, will be tested using validated questionnaires for PD (Almeida and Almeida 1999; Buysse et al. 1988; Leentjens et al. 2014). Touchscreen skills will be assessed using the SSP-test in ST and DT condition, the mobile phone task (MPT, typing a predefined telephone number on a smartphone) and specific questionnaires. Following the baseline session, all patients will receive 10 training sessions of the SSP-task randomly in ST and DT condition (2 weeks, 5 days/week, 10 min/session) over a period of two weeks. The training is home-based and unsupervised. Both immediately after training (T1) and after a four-week retention period (T2), touchscreen skills will be assessed at home. To account for performance bias due to other training, other rehabilitation content will be recorded.
For model selection, the investigators will follow the recommendation of the PROBAST tool (Wolff et al. 2019) and reduce the number of cognitive tests accordingly. Other independent variables may include age, gender, disease duration, the New Freezing of Gait Questionnaire, and LEDD. The investigators will also calculate a measure of early acquisition. After exploratory univariate analysis, the investigators expect to include 10 independent variables, taking multicollinearity into account.
Additionally, the investigators will study the determinants of compliance and its association with retention, as compliance with unsupervised home-based training is likely to be associated with specific non-motor characteristics (Allen et al. 2015). Apathy and the anticipation of reward have been shown to determine the level of effort generated for exercise in PD unlike in HC (Colón-Semenza, Fulford, and Ellis 2021). Also, one previous study investigated the relationship between the success of a balance training program and compliance in PD, establishing compliance to be one of the two most important determinants (Joseph, Leavy, and Franzén 2020). Other work revealed that disease duration was found to be related to the degree of motor learning of the SSP-task (Nackaerts et al. 2020) and to exercise adherence altogether (Allen et al. 2015). Hence, compliance is likely associated with training outcomes in PD (long- and short-term) and may be modulated by (multiple) clinical characteristics. Compliance will be automatically logged by the digitized system and stored in a secured data cloud. Training compliance will be expressed as a percentage of the desired number of sessions and personalized time, with a maximum of 100%. The investigators will analyze the relation between compliance and clinical characteristics first with a multiple linear regression model. Next, a multiple mediation model will be constructed to examine the expected mediating influence of the clinical profiles on this association. Different models will be applied for retention of learning and immediate training effects, as well as for the different outcome measures.
Recruitment & Eligibility
- Status
- ACTIVE_NOT_RECRUITING
- Sex
- All
- Target Recruitment
- 100
- Diagnosis of idiopathic Parkinson's disease based on the criteria of the Movement Disorders Society
- Right-handed, or right-handed use of touchscreen devices.
- Parkinson's disease dementia (PD-D), determined by the level I diagnostics according to Dubois et al. (2007)
- Comorbidities of the upper limb that could interfere with the study and are not caused by Parkinson's disease
- Other neurological disorders besides Parkinson's disease
- Color blindness as determined by the Ishihara test for color deficiency
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Overall learning effects of training on the average slide duration in ST condition Baseline and retention (4 weeks) Using the behavioral data gathered at the different time points (described in Time frame), the changes in average slide duration in ST condition will be determined and used in a multiple linear regression model as dependent variable.
- Secondary Outcome Measures
Name Time Method Immediate training effects and retention of training on performance accuracy in ST condition Baseline, post (2 weeks) and retention (4 weeks) Using the behavioral data gathered at the different time points (described in Time frame), the immediate changes and overall changes in performance accuracy (in %) will be determined and used in a multiple linear regression model as dependent variable.
Immediate training effects and retention of training of the average slide duration in ST condition Baseline, post (2 weeks) and retention (4 weeks) Using the behavioral data gathered at the different time points (described in Time frame), the changes in average slide duration in ST condition will be determined and used in a multiple linear regression model as dependent variable.
Immediate and retained training effects and overall learning of the average slide duration in DT condition Baseline, post (2 weeks) and retention (4 weeks) Using the behavioral data gathered at the different time points (described in Time frame), the immediate changes and overall changes in average slide duration in DT condition will be determined and used in a multiple linear regression model as dependent variable.
Immediate and retained training effects and overall learning on performance accuracy in DT condition Baseline, post (2 weeks) and retention (4 weeks) Using the behavioral data gathered at the different time points (described in Time frame), the immediate changes and overall changes in performance accuracy (in %) will be determined and used in a multiple linear regression model as dependent variable.
Transfer of learning towards an untrained Mobile Phone Task Baseline, post (2 weeks) and retention (4 weeks) Using the behavioral data gathered at the different time points (described in Time frame), changes in the performance on an untrained mobile phone task will be determined, based on the time necessary to type in telephone numbers (in sec) and used in a multiple linear regression model as dependent variable.
Compliance rate to training (in %) Baseline, post (2 weeks) and retention (4 weeks) The automatically logged compliance rate will be expressed as the percentage of the desired number of sessions and personalized time (max. 100%). Compliance rate will be used as dependent variable in a multiple linear regression model and in a secondary mediation model.
Trial Locations
- Locations (1)
Department of Rehabilitation Sciences KU Leuven
🇧🇪Leuven, Belgium