A smartphone-based tapping task as a marker of medication response in Parkinson's disease: a proof of concept study

Dataset of Smartphone-Based Finger Tapping Test

The finger tapping test (FTT) is a neuropsychological test that measures motor speed and coordination. It involves tapping a designated surface with a specific finger as quickly as possible for a certain duration. Touchscreen of smartphones has been used as interface to record the tap, what enables to extract information about the taps. The present study represents an initiative of construction for a national Database of Smartphone-Based FTT, which includes data from 176 healthy male and female adults ranging in age from 18 to 74 years. Participants were asked to perform the FTT using one or both hands, tapping on a central area of a smartphone as many times as possible within a 30-second interval. Data were extracted using an Android application, encompassing details such as touch timing, spatial coordinates, sex, smartphone model, hand used, and age. A Python-developed web visualization tool for individual and averaged analysis. This database serves as informative foundation for a healthy adult sample and supports further exploration and international comparative analyses of FTT performance.

Patient Perspectives on Upper-Limb Daily Function in Parkinson's Disease

BACKGROUND

Dexterity impairments are common among people with Parkinson's disease (PWP), yet little is understood about the effect of upper-limb (UL) dysfunction on daily activity performance.

OBJECTIVES

The aims were to (1) map the dexterity activities most affected and meaningful to PWP; (2) explore the associations between perceived dexterity function and disease severity, cognitive and motor UL impairments, dexterity ability, self-reported activities of daily living (ADL) function, and quality of life (QOL); (3) investigate variables explaining perceived dexterity function; and (4) examine the differences in perceived dexterity function based on dominance affectedness.

METHODS

A total of 43 PWP (mean age = 70.00 years, standard deviation [SD] = 6.75) were assessed for perceived dexterity function (36-item Dexterity Questionnaire [DextQ-36]), dexterity ability (Coin Rotation Task), disease severity (modified Hoen and Yahr Scale), self-reported ADL function and motor UL impairments (Movement Disorder Society-Unified Parkinson's Disease Rating Scale), cognition (Montreal Cognitive Assessment), and QOL (Parkinson's Disease Questionnaire-39).

RESULTS

The leading dexterity activities participants reported as difficult and meaningful included using a touchscreen, pulling on socks, and dialing a phone. Perceived dexterity significantly correlated with self-reported ADL function (r = 0.716), QOL (r = 0.691), disease severity (r = 0.470), and dominant-hand dexterity (r = 0.432). Dexterity ability and disease severity explained 30% of perceived dexterity variance. No differences in perceived dexterity function based on dominance affectedness were found.

CONCLUSIONS

PWP encounter challenges in complex dexterity tasks that impact their independence. Before interventions focused on UL function are initiated, assessments of PWP should include inquiries about the meaningfulness of challenging dexterity activities.

Enhancing public health in developing nations through smartphone-based motor assessment

Several protocols for motor assessment have been validated for use on smartphones and could be employed by public healthcare systems to monitor motor functional losses in populations, particularly those with lower income levels. In addition to being cost-effective and widely distributed across populations of varying income levels, the use of smartphones in motor assessment offers a range of advantages that could be leveraged by governments, especially in developing and poorer countries. Some topics related to potential interventions should be considered by healthcare managers before initiating the implementation of such a digital intervention.

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.

Improving naturalistic neuroscience with patient engagement strategies

Introduction

The clinical implementation of chronic electrophysiology-driven adaptive deep brain stimulation (DBS) algorithms in movement disorders requires reliable representation of motor and non-motor symptoms in electrophysiological biomarkers, throughout normal life (naturalistic). To achieve this, there is the need for high-resolution and -quality chronic objective and subjective symptom monitoring in parallel to biomarker recordings. To realize these recordings, an active participation and engagement of the investigated patients is necessary. To date, there has been little research into patient engagement strategies for DBS patients or chronic electrophysiological recordings.

Concepts and results

We here present our concept and the first results of a patient engagement strategy for a chronic DBS study. After discussing the current state of literature, we present objectives, methodology and consequences of the patient engagement regarding study design, data acquisition, and study infrastructure. Nine patients with Parkinson's disease and their caregivers participated in the meeting, and their input led to changes to our study design. Especially, the patient input helped us designing study-set-up meetings and support structures.

Conclusion

We believe that patient engagement increases compliance and study motivation through scientific empowerment of patients. While considering patient opinion on sensors or questionnaire questions may lead to more precise and reliable data acquisition, there was also a high demand for study support and engagement structures. Hence, we recommend the implementation of patient engagement in planning of chronic studies with complex designs, long recording durations or high demand for individual active study participation.