Freezing of gait (FOG) in Parkinson’s disease (PD) causes severe patient burden despite pharmacological management. Exercise and training are therefore advocated as important adjunct therapies. In this meta-analysis, we assess the existing evidence for such interventions to reduce FOG, and further examine which type of training helps the restoration of gait function in particular. The primary meta-analysis across 41 studies and 1838 patients revealed a favorable moderate effect size (ES = −0.37) of various training modalities for reducing subjective FOG-severity (p < 0.00001), though several interventions were not directly aimed at FOG and some included non-freezers. However, exercise and training also proved beneficial in a secondary analysis on freezers only (ES = −0.32, p = 0.007). We further revealed that dedicated training aimed at reducing FOG episodes (ES = −0.24) or ameliorating the underlying correlates of FOG (ES = −0.40) was moderately effective (p < 0.01), while generic exercises were not (ES = −0.14, p = 0.12). Relevantly, no retention effects were seen after cessation of training (ES = −0.08, p = 0.36). This review thereby supports the implementation of targeted training as a treatment for FOG with the need for long-term engagement.

BackgroundBackground: Deficits in fine motor skills may impair device manipulation including touchscreens in people with Parkinson's disease (PD). Objectives Objectives: To investigate the impact of PD and anti-parkinsonian medication on the ability to use touchscreens. Methods Methods: Twelve PD patients (H&Y II-III), OFF and ON medication, and 12 healthy controls (HC) performed tapping, single and multi-direction sliding tasks on a touchscreen and a mobile phone task (MPT). Task performance was compared between patients (PD-OFF, PD-ON) and HC and between medication conditions. Results Results: Significant differences were found in touchscreen timing parameters, while accuracy was comparable between groups. PD-OFF needed more time than HC to perform single (P = 0.048) and multi-direction (P = 0.004) sliding tasks and to grab the dot before sliding (i.e., transition times) (P = 0.040; P = 0.004). For tapping, dopaminergic medication significantly increased performance times (P = 0.046) to comparable levels as those of HC. However, for the more complex multi-direction sliding, movement times remained slower in PD than HC irrespective of medication intake (P < 0.050 during ON and OFF). The transition times for the multidirection sliding task was also higher in PD-ON than HC (P = 0.048). Touchscreen parameters significantly correlated with MPT performance, supporting the ecological validity of the touchscreen tool. Conclusions Conclusions: PD patients show motor problems when manipulating touchscreens, even when optimally medicated. This hinders using mobile technology in daily life and has implications for developing adequate Ehealth applications for this group. Future work needs to establish whether touchscreen training is effective in PD.

Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand.Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals.Results: A total of 101 articles were included, of which 56 researched Parkinson’s Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups.Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson’s Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.

Tapping tasks have the potential to distinguish between ON–OFF fluctuations in Parkinson’s disease (PD) possibly aiding assessment of medication status in e-diaries and research. This proof of concept study aims to assess the feasibility and accuracy of a smartphone-based tapping task (developed as part of the cloudUPDRS-project) to discriminate between ON–OFF used in the home setting without supervision. 32 PD patients performed the task before their first medication intake, followed by two test sessions after 1 and 3 h. Testing was repeated for 7 days. Index finger tapping between two targets was performed as fast as possible with each hand. Self-reported ON–OFF status was also indicated. Reminders were sent for testing and medication intake. We studied task compliance, objective performance (frequency and inter-tap distance), classification accuracy and repeatability of tapping. Average compliance was 97.0% (± 3.3%), but 16 patients (50%) needed remote assistance. Self-reported ON–OFF scores and objective tapping were worse pre versus post medication intake (p < 0.0005). Repeated tests showed good to excellent test–retest reliability in ON (0.707 ≤ ICC ≤ 0.975). Although 7 days learning effects were apparent, ON–OFF differences remained. Discriminative accuracy for ON–OFF was particularly good for right-hand tapping (0.72 ≤ AUC ≤ 0.80). Medication dose was associated with ON–OFF tapping changes. Unsupervised tapping tests performed on a smartphone have the potential to classify ON–OFF fluctuations in the home setting, despite some learning and time effects. Replication of these results are needed in a wider sample of patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00702-023-02659-w.

Background: Studies aiming to objectively quantify upper limb movement disorders during functional tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to select the most sensitive sensor features for symptom detection and quantification and discuss application of the proposed methods in clinical practice. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: (1) participants were adults/children with a neurological disease, (2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during functional tasks, (3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. (4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinsons Disease. Wrist(s), hand and index finger were the most popular sensor locations. The most frequent tasks for assessment were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. The most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis and entropy of acceleration and/or angular velocity, in combination with dominant frequencies and power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers to select the most sensitive pathology-dependent sensor features and measurement methodologies for detection and quantification of upper limb movement disorders and for the objective evaluations of treatment effects. The insights from Parkinsons Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.