Relationship between manual dexterity and the unified parkinson's disease rating scale-motor exam

Quantifying Changes in Dexterity as a Result of Piano Training in People with Parkinson's Disease

People with Parkinson's disease often show deficits in dexterity, which, in turn, can lead to limitations in performing activities of daily life. Previous studies have suggested that training in playing the piano may improve or prevent a decline in dexterity in this population. In this pilot study, we tested three participants on a six-week, custom, piano-based training protocol, and quantified dexterity before and after the intervention using a sensor-enabled version of the nine-hole peg test, the box and block test, a test of finger synergies using unidimensional force sensors, and the Quantitative Digitography test using a digital piano, as well as selected relevant items from the motor parts of the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and the Parkinson's Disease Questionnaire (PDQ-39) quality of life questionnaire. The participants showed improved dexterity following the training program in several of the measures used. This pilot study proposes measures that can track changes in dexterity as a result of practice in people with Parkinson's disease and describes a potential protocol that needs to be tested in a larger cohort.

Hand Function as Predictor of Motor Symptom Severity in Individuals with Parkinson's Disease

INTRODUCTION

Parkinson's disease (PD) leads to deficits in upper limb strength and manual dexterity and consequently resulting in functional impairment. Handgrip strength is correlated with the motor symptom severity of the disease, but there is a gap in the literature about the influence of freezing in PD patients.

OBJECTIVE

The objective is to study the correlation between handgrip strength and motor symptom severity considering the freezing phenomenon and to verify variables that can predict Unified Parkinson's Disease Rating Scale (UPDRS) III.

METHODS

This is a multicenter cross-sectional study in PD. 101 patients were divided into 2 groups: freezing of gait (FOG) (n = 51) and nonfreezing (nFOG) (n = 52). Freezing of Gait Questionnaire (FOGQ); UPDRS II and III sections; Hoehn and Yahr (HY) scale; handgrip dynamometry (HD); 9 Hole Peg Test (9-HPT) were assessed.

RESULTS

In both groups, HD was correlated to UPDRS III (nFOG: -0.308; FOG: -0.301), UPDRS total (nFOG: -0.379; FOG: -0.368), UPDRS item 23 (nFOG: -0.404; FOG: -0.605), and UPDRS item 24 (nFOG: -0.405; FOG: -0.515). For the correlation to UPDRS II (0.320) and 9-HPT (-0.323), only nFOG group presented significance. For the UPDRS 25 (-0.437), only FOG group presented statistical significance. The UPDRS III can be predicted by 9-HPT, age, and HY in nFOG patients (Adjusted R2 = 0.416). In FOG group, UPDRS III can be predicted by HD, 9-HPT, age, and HY (Adjusted R2 = 0.491).

CONCLUSION

Handgrip strength showed to be predictive of motor impairment only in the FOG group. Our results showed clinical profile differences of motor symptoms considering freezers and nonfreezers with PD.

Validity of a Fully-Immersive VR-Based Version of the Box and Blocks Test for Upper Limb Function Assessment in Parkinson's Disease

In recent decades, gaming technology has been accepted as a feasible method for complementing traditional clinical practice, especially in neurorehabilitation; however, the viability of using 3D Virtual Reality (VR) for the assessment of upper limb motor function has not been fully explored. For that purpose, we developed a VR-based version of the Box and Blocks Test (BBT), a clinical test for the assessment of manual dexterity, as an automated alternative to the classical procedure. Our VR-based BBT (VR-BBT) integrates the traditional BBT mechanics into gameplay using the Leap Motion Controller (LMC) to capture the user’s hand motion and the Oculus Rift headset to provide a fully immersive experience. This paper focuses on evaluating the validity of our VR-BBT to reliably measure the manual dexterity in a sample of patients with Parkinson’s Disease (PD). For this study, a group of twenty individuals in a mild to moderate stage of PD were recruited. Participants were asked to perform the physical BBT (once) and our proposed VR-BBT (twice) system, separately. Correlation analysis of collected data was carried out. Statistical analysis proved that the performance data collected by the VR-BBT significantly correlated with the conventional assessment of the BBT. The VR-BBT scores have shown a significant association with PD severity measured by the Hoehn and Yahr scale. This fact suggests that the VR-BBT could be used as a reliable indicator for health improvements in patients with PD. Finally, the VR-BBT system presented high usability and acceptability rated by clinicians and patients.

A comparison of the relationship between manual dexterity and postural control in young and older individuals with Parkinson's disease

The motor symptoms of Parkinson's disease (PD) cause deterioration in manual dexterity. This deterioration affects independence in activities of daily living negatively. The loss of postural control, which occurs more frequently with disease progression, restricts physical functions and reduces mobility in patients with PD. Impaired postural control may affect distal mobility of an individual. The aim of this study was to investigate postural control and manual dexterity in individuals ≤ 65 and >65 years with PD and analyze the relationship between these variables according to age. Sixty-six individuals with PD participated in the study. The participants were categorized according to age (n = 29 for 65 years of age or younger and n = 37 for older). Manual dexterity (Dominant and Non-dominant hand) was assessed by the Nine Hole Peg Test (NHPT). Postural control was evaluated by the Limit of Stability Test (LoS) using a computerized balance measuring instrument. There was no statistically significant difference between the age groups on the combined dependent variables after controlling for disability, gender, weight, and height; F(7, 54) = 0.804, p = 0.587. Only LoS-Maximum Excursion was higher in the individuals ≤ 65 years (p = 0.035). Significant correlations were found between NHPT-Dominant and LoS-Reaction Time, LoS-Maximum Excursion; NHPT-Non-dominant and LoS-Reaction Time, LoS-Endpoint Excursion, LoS-Maximum Excursion in the older group (p < 0.05). There was no difference manual dexterity and postural control according to age except for LoS-Maximum Excursion. LoS-Maximum Excursion was higher in the young group. The manual dexterity was associated with postural control in individuals over 65 years of age with PD; however, not associated in younger individuals.

Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier

Objective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson’s disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes’ displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user’s performance.