Digital Outcome Measures Research Articles

Validity of a Smartphone App to Objectively Monitor Performance Outcomes in Degenerative Cervical Myelopathy: Preliminary Findings From a Longitudinal Observational Study

Background Developing new clinical measures for degenerative cervical myelopathy (DCM) is an AO Spine RECODE-DCM research priority. Difficulties detecting DCM, and changes in DCM, cause diagnostic and treatment delays in clinical settings and heightened costs in clinical trials due to elevated recruitment targets. Digital outcome measures can tackle these challenges due to their ability to measure disease remotely, repeatedly, and more economically. Objective The study aims to assess the validity of MoveMed, a battery of performance outcome measures performed using a smartphone app, in the measurement of DCM. Methods A prospective observational study in decentralized secondary care was performed in England, United Kingdom. Validity and risk of bias were assessed using criteria from the COSMIN (Consensus-Based Standards for the Selection of Health Measurement Instruments) manual. Each MoveMed outcome was compared with 2 patient-reported comparators, with a priori hypotheses of convergence or divergence tested against consensus thresholds. The primary outcome was the correlation coefficient between the MoveMed outcome and the patient-reported comparators. The secondary outcome was the percentage of correlations that aligned with the a priori hypotheses. The comparators used were the patient-derived modified Japanese Orthopaedic Association score and the World Health Organization Quality of Life Brief Version questionnaire. Thresholds for convergence or divergence were set at ≥0.3 for convergence, <0.3 for divergence, and >0/<0 for directionality. Results A total of 27 adults aged 60 (SD 11) years who live with DCM and possess an approved smartphone were included in a preliminary analysis. As expected, MoveMed tests of neuromuscular function correlated most with questionnaires of neuromuscular function (≥0.3) and least with questionnaires of quality of life (<0.3). Furthermore, directly related constructs correlated positively to each other (>0), while inversely related constructs correlated negatively (<0). Overall, 74% (67/90) and 47% (8/17) of correlations (unidimensional and multidimensional, respectively) were in accordance with hypotheses. No risk-of-bias factors from the COSMIN Risk of Bias checklist were recorded. Overall, this was equivalent to “very good” quality evidence of sufficient construct validity in DCM. Conclusions MoveMed outcomes and patient-reported questionnaires converge and diverge in accordance with expectations. These findings support the validity of the MoveMed tests in an adult population living with DCM. Criteria from COSMIN provide “very good” quality evidence to support this.

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis

BackgroundDigital monitoring of people with multiple sclerosis (PwMS) using smartphone-based monitoring tools is a promising method to assess disease activity and progression.ObjectiveTo study cross-sectional and longitudinal associations between active and passive digital monitoring parameters and MRI volume measures in PwMS.MethodsIn this prospective study, 92 PwMS were included. Clinical tests [Expanded Disability Status Scale (EDSS), Timed 25 Foot Walk test (T25FW), 9-Hole Peg Test (NHPT), and Symbol Digit Modalities Test (SDMT)] and structural MRI scans were performed at baseline (M0) and 12-month follow-up (M12). Active monitoring included the smartphone-based Symbol Digit Modalities Test (sSDMT) and 2 Minute Walk Test (s2MWT), while passive monitoring was based on smartphone keystroke dynamics (KD). Linear regression analyses were used to determine cross-sectional and longitudinal relations between digital and clinical outcomes and brain volumes, with age, disease duration and sex as covariates.ResultsIn PwMS, both sSDMT and SDMT were associated with thalamic volumes and lesion volumes. KD were related to brain, ventricular, thalamic and lesion volumes. No relations were found between s2MWT and MRI volumes. NHPT scores were associated with lesion volumes only, while EDSS and T25FW were not related to MRI. No longitudinal associations were found for any of the outcome measures between M0 and M12.ConclusionOur results show clear cross-sectional correlations between digital biomarkers and brain volumes in PwMS, which were not all present for conventional clinical outcomes, supporting the potential added value of digital monitoring tools.

Digital outcome measures from smartwatch data relate to non-motor features of Parkinson’s disease

Monitoring of Parkinson’s disease (PD) has seen substantial improvement over recent years as digital sensors enable a passive and continuous collection of information in the home environment. However, the primary focus of this work has been motor symptoms, with little focus on the non-motor aspects of the disease. To address this, we combined longitudinal clinical non-motor assessment data and digital multi-sensor data from the Verily Study Watch for 149 participants from the Parkinson’s Progression Monitoring Initiative (PPMI) cohort with a diagnosis of PD. We show that digitally collected physical activity and sleep measures significantly relate to clinical non-motor assessments of cognitive, autonomic, and daily living impairment. However, the poor predictive performance we observed, highlights the need for better targeted digital outcome measures to enable monitoring of non-motor symptoms.

Reliability of a Smartphone App to Objectively Monitor Performance Outcomes in Degenerative Cervical Myelopathy: Observational Study.

Developing new clinical measures for degenerative cervical myelopathy (DCM) is an AO Spine RECODE-DCM Research, an international and multi-stakeholder partnership, priority. Difficulties in detecting DCM and its changes cause diagnostic and treatment delays in clinical settings and heightened costs in clinical trials due to elevated recruitment targets. Digital outcome measures can tackle these challenges due to their ability to measure disease remotely, repeatedly, and more economically. The aim of this study is to assess the reliability of the MoveMed battery of performance outcome measures. A prospective observational study in decentralized secondary care was performed in England, United Kingdom. The primary outcome was to determine the test-retest reliability of the MoveMed performance outcomes using the intraclass correlation (ICC) of agreement . The secondary outcome was to determine the measurement error of the MoveMed performance outcomes using both the SE of the mean (SEM) of agreement and the smallest detectable change (SDC) of agreement . Criteria from the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) manual were used to determine adequate reliability (ie, ICC of agreement ≥0.7) and risk of bias. Disease stability was controlled using 2 minimum clinically important difference (MCID) thresholds obtained from the literature on the patient-derived modified Japanese Orthopaedic Association (p-mJOA) score, namely, MCID ≤1 point and MCID ≤2 points. In total, 7 adults aged 59.5 (SD 12.4) years who live with DCM and possess an approved smartphone participated in the study. All tests demonstrated moderate to excellent test-retest coefficients and low measurement errors. In the MCID ≤1 group, ICC of agreement values were 0.84-0.94 in the fast tap test, 0.89-0.95 in the hold test, 0.95 in the typing test, and 0.98 in the stand and walk test. SEM of agreement values were ±1 tap, ±1%-3% stability score points, ±0.06 keys per second, and ±10 steps per minute, respectively. SDC of agreement values were ±3 taps, ±4%-7% stability score points, ±0.2 keys per second, and ±27 steps per minute, respectively. In the MCID ≤2 group, ICC of agreement values were 0.61-0.91, 0.75-0.77, 0.98, and 0.62, respectively; SEM of agreement values were ±1 tap, ±2%-4% stability score points, ±0.06 keys per second, and ±10 steps per minute, respectively; and SDC of agreement values were ±3-7 taps, ±7%-10% stability score points, ±0.2 keys per second, and ±27 steps per minute, respectively. Furthermore, the fast tap, hold, and typing tests obtained sufficient ratings (ICC of agreement ≥0.7) in both MCID ≤1 and MCID ≤2 groups. No risk of bias factors from the COSMIN Risk of Bias checklist were recorded. The criteria from COSMIN provide "very good" quality evidence of the reliability of the MoveMed tests in an adult population living with DCM.

Improving measurement-based care implementation in youth mental health through organizational leadership and climate: a mechanistic analysis within a randomized trial

BackgroundTheory and correlational research indicate organizational leadership and climate are important for successful implementation of evidence-based practices (EBPs) in healthcare settings; however, experimental evidence is lacking. We addressed this gap using data from the WISDOM (Working to Implement and Sustain Digital Outcome Measures) hybrid type III effectiveness-implementation trial. Primary outcomes from WISDOM indicated the Leadership and Organizational Change for Implementation (LOCI) strategy improved fidelity to measurement-based care (MBC) in youth mental health services. In this study, we tested LOCI’s hypothesized mechanisms of change, namely: (1) LOCI will improve implementation and transformational leadership, which in turn will (2) mediate LOCI’s effect on implementation climate, which in turn will (3) mediate LOCI’s effect on MBC fidelity.MethodsTwenty-one outpatient mental health clinics serving youth were randomly assigned to LOCI plus MBC training and technical assistance or MBC training and technical assistance only. Clinicians rated their leaders’ implementation leadership, transformational leadership, and clinic implementation climate for MBC at five time points (baseline, 4-, 8-, 12-, and 18-months post-baseline). MBC fidelity was assessed using electronic metadata for youth outpatients who initiated treatment in the 12 months following MBC training. Hypotheses were tested using longitudinal mixed-effects models and multilevel mediation analyses.ResultsLOCI significantly improved implementation leadership and implementation climate from baseline to follow-up at 4-, 8-, 12-, and 18-month post-baseline (all ps < .01), producing large effects (range of ds = 0.76 to 1.34). LOCI’s effects on transformational leadership were small at 4 months (d = 0.31, p = .019) and nonsignificant thereafter (ps > .05). LOCI’s improvement of clinic implementation climate from baseline to 12 months was mediated by improvement in implementation leadership from baseline to 4 months (proportion mediated [pm] = 0.82, p = .004). Transformational leadership did not mediate LOCI’s effect on implementation climate (p = 0.136). Improvement in clinic implementation climate from baseline to 12 months mediated LOCI’s effect on MBC fidelity during the same period (pm = 0.71, p = .045).ConclusionsLOCI improved MBC fidelity in youth mental health services by improving clinic implementation climate, which was itself improved by increased implementation leadership. Fidelity to EBPs in healthcare settings can be improved by developing organizational leaders and strong implementation climates.Trial registrationClinicalTrials.gov identifier: NCT04096274. Registered September 18, 2019.

Randomized Trial of an Organizational Implementation Strategy to Improve Measurement-Based Care Fidelity and Youth Outcomes in Community Mental Health

Measurement-based care (MBC), which collects session-by-session symptom data from patients and provides clinicians with feedback on treatment response, is a highly generalizable evidence-based practice with significant potential to improve the outcomes of mental health treatment in youth when implemented with fidelity; however, it is rarely used in community settings. This study tested whether an implementation strategy targeting organizational leadership and organizational implementation climate could improve MBC fidelity and clinical outcomes for youth in outpatient mental health clinics. In a cluster randomized trial, 21 clinics were assigned to the Leadership and Organizational Change for Implementation strategy plus training and technical assistance in MBC (k= 11, n= 117) or training and technical assistance only (k= 10, n= 117). Primary outcomes of MBC fidelity (assessed via electronic metadata) and youth symptom improvement (assessed via caregiver-reported change on the Shortform Assessment for Children Total Problem Score) were collected for consecutively enrolled youths (ages 4-18 years) who initiated treatment in the 12 months following MBC training. Outcomes of each youth were assessed for 6 months following baseline. A total of 234 youths were enrolled and included in intent-to-treat analyses. At baseline, there were no significant differences by condition in clinic, clinician, or youth characteristics. Youths in clinics using the Leadership and Organizational Change for Implementation strategy experienced significantly higher MBC fidelity compared with youths in control clinics (23.1% vs 3.4%, p= .014), and exhibited significantly greater reductions in symptoms from baseline to 6 months (d= 0.31, 95% CI: 0.04-0.58, p= .023). Implementation strategies targeting organizational leadership and focused implementation climate can improve fidelity to evidence-based practices and clinical outcomes of youth mental health services. Working to Implement and Sustain Digital Outcome Measures (WISDOM); https://clinicaltrials.gov/; NCT04096274.

Mobilizing Patient and Public Involvement in the Development of Real-World Digital Technology Solutions: Tutorial.

Although the value of patient and public involvement and engagement (PPIE) activities in the development of new interventions and tools is well known, little guidance exists on how to perform these activities in a meaningful way. This is particularly true within large research consortia that target multiple objectives, include multiple patient groups, and work across many countries. Without clear guidance, there is a risk that PPIE may not capture patient opinions and needs correctly, thereby reducing the usefulness and effectiveness of new tools. Mobilise-D is an example of a large research consortium that aims to develop new digital outcome measures for real-world walking in 4 patient cohorts. Mobility is an important indicator of physical health. As such, there is potential clinical value in being able to accurately measure a person's mobility in their daily life environment to help researchers and clinicians better track changes and patterns in a person's daily life and activities. To achieve this, there is a need to create new ways of measuring walking. Recent advancements in digital technology help researchers meet this need. However, before any new measure can be used, researchers, health care professionals, and regulators need to know that the digital method is accurate and both accepted by and produces meaningful outcomes for patients and clinicians. Therefore, this paper outlines how PPIE structures were developed in the Mobilise-D consortium, providing details about the steps taken to implement PPIE, the experiences PPIE contributors had within this process, the lessons learned from the experiences, and recommendations for others who may want to do similar work in the future. The work outlined in this paper provided the Mobilise-D consortium with a foundation from which future PPIE tasks can be created and managed with clearly defined collaboration between researchers and patient representatives across Europe. This paper provides guidance on the work required to set up PPIE structures within a large consortium to promote and support the creation of meaningful and efficient PPIE related to the development of digital mobility outcomes.

P.222 Wearable inertial sensors for longitudinal follow-up of patients with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by the loss of motor neuron leading to weakness and death often resulting from failure of the respiratory muscles. There have been numerous recent discoveries in ALS research leading to potential therapies that will need testing in clinical trials. Due to outcome measures limitations, trials, especially for neurodegenerative disorders, require large sample sizes, long durations and have a high risk for failure. Digital biomarkers collected with wearable devices could provide powerful outcome measures through an objective assessment performed in real-life. Remote continuous monitoring is an innovative method for quantifying disease progression in ALS patients providing for motor function assessment through patients' loss of ambulation. This opportunity for shorter and less burdensome clinical trials should be confirmed by further data collection. We designed a longitudinal natural history study in ALS. Participants were assessed with standard evaluations including the amyotrophic lateral sclerosis functional rating scale, six-minute walk test, Medical Research Council scores, Ashworth score, hand dynamometer, pulmonary function and cognitive tests every 3 months for 1 year. After each visit, patients were asked to wear for one month one inertial sensor at the ankle and one at the wrist for continuous assessment of motor function in real-life. These data will be compared with already acquired normative data. At the present time, 11 patients with ALS (3 females, 8 males) aged from 44 to 72 years old were included. Four patients discontinued the study due to death or permanent ventilation. Initial results suggest that upper and lower limb variables are lower in patient with ALS than in controls and show a downward trend over time. We will present longitudinal data along with correlation between change in traditional and digital outcome measures. We will show comparison between patients and controls.

Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.

BackgroundMore sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests.ObjectiveThis study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials.MethodsThe Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies’ ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments.ResultsParticipant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic.ConclusionsThis study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies.International Registered Report Identifier (IRRID)DERR1-10.2196/35442

Identifying and characterising sources of variability in digital outcome measures in Parkinson’s disease

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson’s disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies.

Distance to first symptoms measured by the 6-min walking test differentiates between treatment success and failure in patients with degenerative lumbar disorders

PurposeThe smartphone-based 6-min walking test (6WT) is an established digital outcome measure in patients undergoing surgery for degenerative lumbar disorders (DLD). In addition to the 6WTs primary outcome measure, the 6-min walking distance (6WD), the patient’s distance to first symptoms (DTFS) and time to first symptoms (TTFS) can be recorded. This is the first study to analyse the psychometric properties of the DTFS and TTFS.MethodsForty-nine consecutive patients (55 ± 15.8 years) completed the 6WT pre- and 6 weeks (W6) postoperative. DTFS and TTFS were assessed for reliability and content validity using disease-specific patient-reported outcome measures. The Zurich Claudication Questionnaire patient satisfaction subscale was used as external criterion for treatment success. Internal and external responsiveness for both measures at W6 was evaluated.ResultsThere was a significant improvement in DTFS and TTFS from baseline to W6 (p < 0.001). Both measures demonstrated a good test–retest reliability (β = 0.86, 95% CI 0.81–0.90 and β = 0.83, 95% CI 0.76–0.87, both p < 0.001). The DTFS exceeded the 6WD capability to differentiate between satisfied (82%) and unsatisfied patients (18%) with an AUC of 0.75 (95% CI 0.53–0.98) vs. 0.70 (95% CI 0.52–0.90). The TTFS did not demonstrate meaningful discriminative abilities.ConclusionChange in DTFS can differentiate between satisfied and unsatisfied patients after spine surgery. Digital outcome measures on the 6WT metric provide spine surgeons and researchers with a mean to assess their patient’s functional disability and response to surgical treatment in DLD.

The ARCA Registry: A Collaborative Global Platform for Advancing Trial Readiness in Autosomal Recessive Cerebellar Ataxias.

Autosomal recessive cerebellar ataxias (ARCAs) form an ultrarare yet expanding group of neurodegenerative multisystemic diseases affecting the cerebellum and other neurological or non-neurological systems. With the advent of targeted therapies for ARCAs, disease registries have become a precious source of real-world quantitative and qualitative data complementing knowledge from preclinical studies and clinical trials. Here, we review the ARCA Registry, a global collaborative multicenter platform (>15 countries, >30 sites) with the overarching goal to advance trial readiness in ARCAs. It presents a good clinical practice (GCP)- and general data protection regulation (GDPR)-compliant professional-reported registry for multicenter web-based capture of cross-center standardized longitudinal data. Modular electronic case report forms (eCRFs) with core, extended, and optional datasets allow data capture tailored to the participating site's variable interests and resources. The eCRFs cover all key data elements required by regulatory authorities [European Medicines Agency (EMA)] and the European Rare Disease (ERD) platform. They capture genotype, phenotype, and progression and include demographic data, biomarkers, comorbidity, medication, magnetic resonance imaging (MRI), and longitudinal clinician- or patient-reported ratings of ataxia severity, non-ataxia features, disease stage, activities of daily living, and (mental) health status. Moreover, they are aligned to major autosomal-dominant spinocerebellar ataxia (SCA) and sporadic ataxia (SPORTAX) registries in the field, thus allowing for joint and comparative analyses not only across ARCAs but also with SCAs and sporadic ataxias. The registry is at the core of a systematic multi-component ARCA database cluster with a linked biobank and an evolving study database for digital outcome measures. Currently, the registry contains more than 800 patients with almost 1,500 visits representing all ages and disease stages; 65% of patients with established genetic diagnoses capture all the main ARCA genes, and 35% with unsolved diagnoses are targets for advanced next-generation sequencing. The ARCA Registry serves as the backbone of many major European and transatlantic consortia, such as PREPARE, PROSPAX, and the Ataxia Global Initiative, with additional data input from SPORTAX. It has thus become the largest global trial-readiness registry in the ARCA field.

Deep Learning-Based Human Activity Recognition for Continuous Activity and Gesture Monitoring for Schizophrenia Patients With Negative Symptoms.

BackgroundWe aimed to develop a Human Activity Recognition (HAR) model using a wrist-worn device to assess patient activity in relation to negative symptoms of schizophrenia.MethodsData were analyzed in a randomized, three-way cross-over, proof-of-mechanism study (ClinicalTrials.gov: NCT02824055) comparing two doses of RG7203 with placebo, given as adjunct to stable antipsychotic treatment in patients with chronic schizophrenia and moderate levels of negative symptoms. Baseline negative symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) and Brief Negative Symptom Scale (BNSS). Patients were given a GeneActiv™ wrist-worn actigraphy device to wear over a 15-week period. For this analysis, actigraphy data and behavioral and clinical assessments obtained during placebo treatment were used. Motivated behavior was evaluated with a computerized effort-choice task. A trained HAR model was used to classify activity and an activity–time ratio was derived. Gesture events and features were inferred from the HAR-detected activities and the acceleration signal.ResultsThirty-three patients were enrolled: mean (±SD) age 36.6 ± 7 years; mean (±SD) baseline PANSS negative symptom factor score 23.0 ± 3.5; and mean (±SD) baseline BNSS total score 36.0 ± 11.5. Activity data were collected for 31 patients with a median monitoring time of 1,859 h per patient, equating to ~11 weeks or 74% monitoring ratio. The trained HAR model demonstrated >95% accuracy in separating ambulatory and stationary activities. A positive correlation was seen between the activity–time ratio and the percent of high-effort choices (Spearman r = 0.58; P = 0.002) in the effort-choice task. Median daily gesture counts correlated negatively with the BNSS total score (Spearman r = −0.44; P = 0.03), specifically with the diminished expression sub-score (Spearman r = −0.42; P = 0.03). Gesture features also correlated negatively with the BNSS total score and diminished expression sub-scores. Activity measures showed similar correlations with PANSS negative symptom factor but did not reach significance.ConclusionOur findings support the use of wrist-worn devices to derive activity and gesture-based digital outcome measures for patients with schizophrenia with negative symptoms in a clinical trial setting.

Validation of low-cost system for gait assessment in children with ataxia

BackgroundAtaxic syndromes include several rare, inherited and acquired conditions. One of the main issues is the absence of specific, and sensitive automatic evaluation tools and digital outcome measures to obtain a continuous monitoring of subjects' motor ability. ObjectivesThis study aims to test the usability of the Kinect system for assessing ataxia severity, exploring the potentiality of clustering algorithms and validating this system with a standard motion capture system. MethodsGait evaluation was performed by standardized gait analysis and by Kinect v2 during the same day in a cohort of young patient (mean age of 13.8±7.2). We analyzed the gait spatio-temporal parameters and we looked at the differences between the two systems through correlation and agreement tests. As well, we tested for possible correlations with the SARA scale as well. Finally, standard classification algorithm and principal components analysis were used to discern disease severity and groups. ResultsWe found biases and linear relationships between all the parameters. Significant correlations emerged between the SARA and the Speed, the Stride Length and the Step Length. PCA results, highlighting that a machine learning approach combined with Kinect-based evaluation shows great potential to automatically assess disease severity and diagnosis. ConclusionsThe spatio-temporal parameters measured by Kinect cannot be used interchangeably with those parameters acquired with standard motion capture system in clinical practice but can still provide fundamental information. Specifically, these results might bring to the development of a novel system to perform easy and quick evaluation of gait in young patients with ataxia, useful for patients stratification in terms of clinical severity and diagnosis.

Digital Biomarkers of Mobility in Parkinson's Disease During Daily Living.

Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD). To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring. We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a 'best subsets selection strategy' was used to find combinations of measures that discriminated mobility in PD from HC. Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC > 0.80. Turn angle (AUC = 0.89, 95% CI: 0.79-0.97) and swing time variability (AUC = 0.87, 95% CI: 0.75-0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures. Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.

A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies.

Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.