Developing an Instrumented Measure of Upper Limb Function in Friedreich Ataxia

Multifeature quantitative motor assessment of upper limb ataxia including drawing and reaching

OBJECTIVE

Voluntary upper limb movements are an ecologically important yet insufficiently explored digital-motor outcome domain for trials in degenerative ataxia. We extended and validated the trial-ready quantitative motor assessment battery "Q-Motor" for upper limb movements with clinician-reported, patient-focused, and performance outcomes of ataxia.

METHODS

Exploratory single-center cross-sectional assessment in 94 subjects (46 cross-genotype ataxia patients; 48 matched controls), comprising five tasks measured by force transducer and/or position field: Finger Tapping, diadochokinesia, grip-lift, and-as novel implementations-Spiral Drawing, and Target Reaching. Digital-motor measures were selected if they discriminated from controls (AUC >0.7) and correlated-with at least one strong correlation (rho ≥0.6)-to the Scale for the Assessment and Rating of Ataxia (SARA), activities of daily living (FARS-ADL), and the Nine-Hole Peg Test (9HPT).

RESULTS

Six movement features with 69 measures met selection criteria, including speed and variability in all tasks, stability in grip-lift, and efficiency in Target Reaching. The novel drawing/reaching tasks best captured impairment in dexterity (|rho9HPT| ≤0.81) and FARS-ADL upper limb items (|rhoADLul| ≤0.64), particularly by kinematic analysis of smoothness (SPARC). Target hit rate, a composite of speed and endpoint precision, almost perfectly discriminated ataxia and controls (AUC: 0.97). Selected measures in all tasks discriminated between mild, moderate, and severe impairment (SARA upper limb composite: 0-2/>2-4/>4-6) and correlated with severity in the trial-relevant mild ataxia stage (SARA ≤10, n = 20).

INTERPRETATION

Q-Motor assessment captures multiple features of impaired upper limb movements in degenerative ataxia. Validation with key clinical outcome domains provides the basis for evaluation in longitudinal studies and clinical trial settings.

Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers.

The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review

Developing drugs for rare diseases is challenging, and the precision and objectivity of outcome measures is critical to this process. In recent years, a number of technologies have increasingly been used for remote monitoring of patient health. We report a systematic literature review that aims to summarize the current state of progress with regard to the use of digital outcome measures for real-life motor function assessment of patients with rare neurological diseases. Our search of published literature identified 3826 records, of which 139 were included across 27 different diseases. This review shows that use of digital outcome measures for motor function outside a clinical setting is feasible and employed in a broad range of diseases, although we found few outcome measures that have been robustly validated and adopted as endpoints in clinical trials. Future research should focus on validation of devices, variables, and algorithms to allow for regulatory qualification and widespread adoption.

A Study of Upper-Limb Motion using Kinematic Measures for Clinical Assessment of Cerebellar Ataxia

Cerebellar Ataxia (CA) is a group of diseases affecting the cerebellum, which is responsible for movement coordination. It causes uncoordinated movements and can also impact balance, speech, and eye movements. There are no approved disease-modifying medications for CA, so clinical studies to assess potential treatments are crucial. These studies require robust, objective measurements of CA severity to reflect changes in the progression of the disease due to medication. In recent years, studies have used kinematic measures to evaluate CA severity, but the current method relies on subjective clinical observations and is insufficient for telehealth. There is a need for a non-intrusive system that can monitor people with CA regularly to better understand the disease and develop an automated assessment system. In this study, we analyzed kinematic measures of upper-limb movements during a ballistic tracking test, which primarily involves movements at the shoulder joint. We aimed to understand the challenges of identifying CA and evaluating its severity when measuring such movements. Statistical features of the kinematic signals were used to develop machine learning models for classification and regression. The Gradient Boosting Classifier model had a maximum accuracy of 74%, but the models had low specificity and performed poorly in regression, suggesting that kinematic measures from shoulder-dominated movements during ballistic tracking are not as viable for CA assessment as other measures.