European regulators' views on a wearable-derived performance measurement of ambulation for Duchenne muscular dystrophy regulatory trials

Unlocking the full potential of digital endpoints for decision making: a novel modular evidence concept enabling re-use and advancing collaboration

INTRODUCTION

Over the last decade increasing examples indicate opportunities to measure patient functioning and its relevance for clinical and regulatory decision making via endpoints collected through digital health technologies. More recently, we have seen such measures support primary study endpoints and enable smaller trials. The field is advancing fast: validation requirements have been proposed in the literature and regulators are releasing new guidances to review these endpoints. Pharmaceutical companies are embracing collaborations to develop them and working with academia and patient organizations in their development. However, the road to validation and regulatory acceptance is lengthy. The full value of digital endpoints cannot be unlocked until better collaboration and modular evidence frameworks are developed enabling re-use of evidence and repurposing of digital endpoints.

AREAS COVERED

This paper proposes a solution by presenting a novel modular evidence framework -the Digital Evidence Ecosystem and Protocols (DEEP)- enabling repurposing of measurement solutions, re-use of evidence, application of standards and also facilitates collaboration with health technology assessment bodies.

EXPERT OPINION

The integration of digital endpoints in healthcare, essential for personalized and remote care, requires harmonization and transparency. The proposed novel stack model offers a modular approach, fostering collaboration and expediting the adoption in patient care.

Stride Velocity 95th Centile Detects Decline in Ambulatory Function Over Shorter Intervals than the 6-Minute Walk Test or North Star Ambulatory Assessment in Duchenne Muscular Dystrophy

Background

Stride Velocity 95th Centile (SV95C) is the first wearable device-derived clinical outcome assessment (COA) to receive European Medicines Agency (EMA) qualification as a primary endpoint in ambulant patients with Duchenne muscular dystrophy (DMD) aged ≥4 years.

Objective

To compare SV95C-in its first-ever clinical trial application as a secondary endpoint-with established motor function COAs used in the trial (Four-Stair Climb [4SC] velocity, North Star Ambulatory Assessment [NSAA], and Six-Minute Walk Distance [6MWD]).

Methods

SV95C was a secondary endpoint in a subset (n = 47) of participants in the SPITFIRE/WN40227 trial of taldefgrobep alfa, which was discontinued due to lack of clinical benefit. Participants in the ≤48-week SV95C sub-study were 6-11 years old and received corticosteroids for ≥6 months pre-treatment. Pearson correlations were used to compare SV95C with the other COAs. Responsiveness and changes over time were respectively assessed via standardized response means (SRMs) based on absolute changes and mixed models for repeated measures.

Results

SV95C change at Week 24 was -0.07 m/s, with limited variability (standard deviation: 0.16, n = 27). The SRM for SV95C indicated moderate responsiveness to clinical change at the earliest timepoint (Week 12, n = 46), while those of the other COAs did not indicate moderate responsiveness until Week 36 (6MWD, n = 33) or Week 48 (4SC velocity, n = 20; NSAA total score, n = 20). Baseline correlations between SV95C and other COAs were strong (r = 0.611-0.695). Correlations between SV95C change from baseline to Week 48 and changes in other COAs were moderate to strong (r = 0.443-0.678).∥.

Conclusions

Overall, SV95C demonstrated sensitivity to ambulatory decline over short intervals, low variability, and correlation with established COAs. Although the negative trial precluded demonstration of SV95C's sensitivity to drug effect, these findings support the continued use of SV95C in DMD clinical trials.

Patient-centric assessment of rheumatoid arthritis using a smartwatch and bespoke mobile app in a clinical setting

Rheumatoid arthritis (RA) is a fluctuating progressive disease requiring frequent symptom assessment for appropriate management. Continuous tracking using digital technologies may provide greater insights of a patient's experience. This prospective study assessed the feasibility, reliability, and clinical utility of using novel digital technologies to remotely monitor participants with RA. Participants with moderate to severe RA and non-RA controls were monitored continuously for 14 days using an iPhone with an integrated bespoke application and an Apple Watch. Participants completed patient-reported outcome measures and objective guided tests designed to assess disease-related impact on physical function. The study was completed by 28 participants with RA, 28 matched controls, and 2 unmatched controls. Completion rates for all assessments were > 97% and were reproducible over time. Several guided tests distinguished between RA and control cohorts (e.g., mean lie-to-stand time [seconds]: RA: 4.77, control: 3.25; P < 0.001). Participants with RA reporting greater stiffness, pain, and fatigue had worse guided test performances (e.g., wrist movement [P < 0.001] and sit-to-stand transition time [P = 0.009]) compared with those reporting lower stiffness, pain, and fatigue. This study demonstrates that digital technologies can be used in a well-controlled, remote clinical setting to assess the daily impact of RA.

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.

Exploring the relationship between North Star Ambulatory Assessment and Health Utilities Index scores in Duchenne muscular dystrophy

BACKGROUND

The North Star Ambulatory Assessment (NSAA) documents motor performance in ambulatory individuals with Duchenne muscular dystrophy (DMD). Health Utilities Index (HUI) scores, reflecting preferences for health-related quality-of-life (HRQoL) implications of health states, are commonly estimated within trials. This study sought to characterize the relationship between the NSAA score and utility in DMD.

METHODS

Family members serving as proxy respondents for placebo-treated ambulatory individuals with DMD (NCT01254019; BioMarin Pharmaceuticals Inc) completed the HUI and the NSAA (score range, 0-34). Mean change over time on these measures was estimated, and the correlation between changes in NSAA score and a) HUI utility; b) HUI3 ambulation and HUI2 mobility attribute scores, over 48 weeks was calculated.

RESULTS

Baseline mean (range) age was 8.0 years (5-16; n = 61) and mean (standard deviation [SD]) scores were 0.87 (0.13; HUI2), 0.82 (0.19; HUI3), and 21.0 (8.1; NSAA). Mean (SD) change over 48 weeks was -0.05 (0.14; HUI2), -0.06 (0.19; HUI3), and -2.9 (4.7; NSAA). Weak positive correlations were observed between baseline NSAA score and HUI utility (HUI2: r = 0.29; HUI3: r = 0.17) and for change over 48 weeks (HUI2: r = 0.16; HUI3: r = 0.15). Stronger correlations were observed between change in NSAA score and the HUI3 ambulation (r = 0.41) and HUI2 mobility (r = 0.41) attributes.

CONCLUSIONS

Among ambulatory individuals with DMD, NSAA score is weakly correlated with HUI utility, suggesting that motor performance alone does not fully explain HRQoL. Stronger relationships were observed between HUI ambulation and mobility attributes, and NSAA. Although unidimensional measures like the NSAA are informative for documenting disease-specific health impacts, they may not correlate well with measures of overall health status; requiring use in conjunction with other patient-reported and preference-based outcomes.

Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease

With the advent of continuous health monitoring with wearable devices, users now generate their unique streams of continuous data such as minute-level step counts or heartbeats. Summarizing these streams via scalar summaries often ignores the distributional nature of wearable data and almost unavoidably leads to the loss of critical information. We propose to capture the distributional nature of wearable data via user-specific quantile functions (QF) and use these QFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an alternative approach, we also propose to represent QFs via user-specific L-moments, robust rank-based analogs of traditional moments, and use L-moments as predictors in SOQFR (SOQFR-L). These two approaches provide two mutually consistent interpretations: in terms of quantile levels by SOQFR and in terms of L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal distributional data via Joint and Individual Variation Explained using L-moments. The proposed methods are illustrated in a study of association of digital gait biomarkers with cognitive function in Alzheimers disease. Our analysis shows that the proposed methods demonstrate higher predictive performance and attain much stronger associations with clinical cognitive scales compared to simple distributional summaries.

Digital health technology derived measures: Biomarkers or clinical outcome assessments?

Digital health technologies (DHTs) present unique opportunities for clinical evidence generation but pose certain challenges. These challenges stem, in part, from existing definitions of drug development tools, which were not created with DHT-derived measures in mind. DHT-derived measures can be leveraged as either clinical outcome assessments (COAs) or as biomarkers since they share properties with both categories of drug development tools. Examples from the literature indicate a variety of applications for DHT-derived data, including capturing disease physiology, symptom tracking, or response to therapies. The distinction between the categorization of DHT-derived measures as COAs or as biomarkers can be very fine, with terminology variability among regulatory authorities. This has significant implications for integration of DHT-derived measures in clinical trials, leading to confusion regarding the evidence required to support these tools' use in drug development. There is a need to amend definitions and create clear evidentiary requirements to support broad adoption of these new and innovative tools. The biopharma industry, the technology sector, consulting businesses, academic researchers, and regulators need a dialogue via multi-stakeholder collaborations to clarify questions around DHT-derived measures, to unify definitions, and to create the foundations for evidentiary package requirements, providing a path forward to predictable results.

From Meaningful Outcomes to Meaningful Change Thresholds: A Path to Progress for Establishing Digital Endpoints

This paper examines the use of digital endpoints (DEs) derived from digital health technologies (DHTs), focusing primarily on the specific considerations regarding the determination of meaningful change thresholds (MCT). Using DHTs in drug development is becoming more commonplace. There is general acceptance of the value of DHTs supporting patient-centric trial design, capturing data outside the traditional clinical trial setting, and generating DEs with the potential to be more sensitive to change than conventional assessments. However, the transition from exploratory endpoints to primary and secondary endpoints capable of supporting labeling claims requires these endpoints to be substantive with reproducible population-specific values. Meaningful change represents the amount of change in an endpoint measure perceived as important to patients and should be determined for each digital endpoint and given population under consideration. This paper examines existing approaches to determine meaningful change thresholds and explores examples of these methodologies and their use as part of DE development: emphasizing the importance of determining what aspects of health are important to patients and ensuring the DE captures these concepts of interest and aligns with the overarching endpoint strategy. Examples are drawn from published DE qualification documentation and responses to qualification submissions under review by the various regulatory authorities. It is the hope that these insights will inform and strengthen the development and validation of DEs as drug development tools, particularly for those new to the approaches to determine MCTs.

Evolving regulatory perspectives on digital health technologies for medicinal product development

Digital health technology tools (DHTTs) present real opportunities for accelerating innovation, improving patient care, reducing clinical trial duration and minimising risk in medicines development. This review is comprised of four case studies of DHTTs used throughout the lifecycle of medicinal products, starting from their development. These cases illustrate how the regulatory requirements of DHTTs used in medicines development are based on two European regulatory frameworks (medical device and the medicinal product regulations) and highlight the need for increased collaboration between various stakeholders, including regulators (medicines regulators and device bodies), pharmaceutical sponsors, manufacturers of devices and software, and academia. As illustrated in the examples, the complexity of the interactions is further increased by unique challenges related to DHTTs. These case studies are the main examples of DHTTs with a regulatory assessment thus far, providing an insight into the applicable current regulatory approach; they were selected by a group of authors, including regulatory specialists from pharmaceutical sponsors, technology experts, academic researchers and employees of the European Medicines Agency. For each case study, the challenges faced by sponsors and proposed potential solutions are discussed, and the benefit of a structured interaction among the different stakeholders is also highlighted.

Predicting hospitalization from real-world measures in patients with chronic kidney disease: A proof-of-principle study

Objective

To investigate if in-clinic measures of physical function and real-world measures of physical behavior and mobility effort are associated with one another and to determine if they predict future hospitalization in participants with chronic kidney disease (CKD).

Methods

In this secondary analysis, novel real-world measures of physical behavior and mobility effort, including the best 6-minute step count (B6SC), were derived from passively collected data from a thigh worn actigraphy sensor and compared to traditional in-clinic measures of physical function (e.g. 6-minute walk test (6MWT). Hospitalization status during 2 years of follow-up was determined from electronic health records. Correlation analyses were used to compare measures and Cox Regression analysis was used to compare measures with hospitalization.

Results

One hundred and six participants were studied (69  ±  13 years, 43% women). Mean  ±  SD baseline measures for 6MWT was 386  ±  66 m and B6SC was 524  ±  125 steps. Forty-four hospitalization events over 224 years of total follow-up occurred. Good separation was achieved for tertiles of 6MWT, B6SC and steps/day for hospitalization events. This pattern persisted in models adjusted for demographics (6MWT: HR  =  0.63 95% CI 0.43-0.93, B6SC: HR  =  0.75, 95% CI 0.56-1.02 and steps/day: HR  =  0.75, 95% CI 0.50-1.13) and further adjusted for morbidities (6MWT: HR  =  0.54, 95% CI 0.35-0.84, B6SC: HR  =  0.70, 95% CI 0.49-1.00 and steps/day: HR  =  0.69, 95% CI 0.43-1.09).

Conclusion

Digital health technologies can be deployed remotely, passively, and continuously to collect real-world measures of physical behavior and mobility effort that differentiate risk of hospitalization in patients with CKD.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Background

Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.

Summary

In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.

Key Messages

In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy

Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy.

The Responsiveness of Gait and Balance Outcomes to Disease Progression in Friedreich Ataxia

To identify gait and balance measures that are responsive to change during the timeline of a clinical trial in Friedreich ataxia (FRDA), we administered a battery of potential measures three times over a 12-month period. Sixty-one ambulant individuals with FRDA underwent assessment of gait and balance at baseline, 6 months and 12 months. Outcomes included GAITRite® spatiotemporal gait parameters; Biodex Balance System Postural Stability Test (PST) and Limits of Stability; Berg Balance Scale (BBS); Timed 25-Foot Walk Test; Dynamic Gait Index (DGI); SenseWear MF Armband step and energy activity; and the Friedreich Ataxia Rating Scale Upright Stability Subscale (FARS USS). The standardised response mean (SRM) or correlation coefficients were reported as effect size indices for comparison of internal responsiveness. Internal responsiveness was also analysed in subgroups. SenseWear Armband daily step count had the largest effect size of all the variables over 6 months (SRM = -0.615), while the PST medial-lateral index had the largest effect size (SRM = 0.829) over 12 months. The FARS USS (SRM = 0.824) and BBS (SRM = -0.720) were the only outcomes able to detect change over 12 months in all subgroups. The DGI was the most responsive outcome in children, detecting a mean change of -2.59 (95% CI -3.52 to -1.66, p < 0.001, SRM = -1.429). In conclusion, the FARS USS and BBS are highly responsive and can detect change in a wide range of ambulant individuals with FRDA. However, therapeutic effects in children may be best measured by the DGI.

Biomarkers in Medicines Development-From Discovery to Regulatory Qualification and Beyond

Biomarkers are important tools in medicines development and clinical practice. Besides their use in clinical trials, such as for enrichment of patients, monitoring safety or response to treatment, biomarkers are a cornerstone of precision medicine. The European Medicines Agency (EMA) emphasised the importance of the discovery, qualification, and use of biomarkers in their Regulatory Science Strategy to 2025, which included the recommendation to enhance early engagement with biomarker developers to facilitate regulatory qualification. This study explores the journey of biomarkers through the EU regulatory system and beyond, based on a review of interactions between developers and the EMA from 2008 to 2020, as well as the use of qualified biomarkers in clinical trials. Of applicants that used early interaction platforms such as the Innovation Task Force, less than half engaged in fee-related follow-up procedures. Results showed that, as compared to companies, consortia were more likely to opt for the Qualification of Novel Methodologies procedure and engage in follow-up procedures. Our results highlight the importance of early engagement with regulators for achieving biomarker qualification, including pre-submission discussions in the context of the qualification procedure. A review of clinical trials showed that all qualified biomarkers are used in practice, although not always according to the endorsed context of use. Overall, this study highlights important aspects of biomarker qualification, including opportunities to improve the seamless support for developers by EMA. The use of qualified biomarkers in clinical trials underlines the importance of regulatory qualification, which will further enable precision medicine for the benefit of patients.

Can Innovative Trial Designs in Orphan Diseases Drive Advancement of Treatments for Common Neurological Diseases?

Global regulatory agencies have transformed their approach to approvals in their processes for formal review of the safety and efficacy of new drugs. Opportunities for innovation have expanded because of the COVID-19 pandemic. Several regulatory-led initiatives have progressed rapidly during the past year including patient-focused drug development, model-informed drug development, Real World Evidence, and complex innovative trial designs. Collectively, these initiatives have accelerated the rate of approvals. Despite demands to focus on urgent needs imposed by the COVID-19 pandemic, the number of new drug approvals over the past year, particularly for rare diseases, has outpaced expectations. Advancing therapeutics for nervous system disorders requires adaptive strategies that align with rapid developments in the field. Three relentlessly progressive diseases, Amyotrophic Lateral Sclerosis (ALS), Duchenne Muscular Dystrophy (DMD), and Parkinson's disease are in urgent need of new treatments. Herein, we propose new regulatory initiatives including innovative trial designs and patient-focused drug development that accelerate clinical trial conduct while meeting critical regulatory requirements for therapeutic approval.

A Smartphone Application as an Exploratory Endpoint in a Phase 3 Parkinson's Disease Clinical Trial: A Pilot Study

BACKGROUND

Smartphones can generate objective measures of Parkinson's disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited.

OBJECTIVE

This study aimed to determine the feasibility of introducing a smartphone research application into a PD clinical trial and to evaluate the resulting measures.

METHODS

A smartphone application was introduced part-way into a phase 3 randomized clinical trial of inosine. The application included finger tapping, gait, and cognition tests, and participants were asked to complete an assessment battery at home and in clinic alongside the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS).

RESULTS

Of 236 eligible participants in the parent study, 88 (37%) consented to participate, and 59 (27 randomized to inosine and 32 to placebo) completed a baseline smartphone assessment. These 59 participants collectively completed 1,292 batteries of assessments. The proportion of participants who completed at least one smartphone assessment was 61% at 3, 54% at 6, and 35% at 12 months. Finger tapping speed correlated weakly with the part III motor portion (r = -0.16, left hand; r = -0.04, right hand) and total (r = -0.14) MDS-UPDRS. Gait speed correlated better with the same measures (r = -0.25, part III motor; r = -0.34, total). Over 6 months, finger tapping speed, gait speed, and memory scores did not differ between those randomized to active drug or placebo.

CONCLUSIONS

Introducing a smartphone application midway into a phase 3 clinical trial was challenging. Measures of bradykinesia and gait speed correlated modestly with traditional outcomes and were consistent with the study's overall findings, which found no benefit of the active drug.

Technology-outcome measures in neuromuscular disorders: a systematic review

BACKGROUND

Portable and wearable devices can monitor a number of physical performances and have been lately applied to patients with neuromuscular disorders (NMD).

METHODS

We performed a systematic search of literature databases following PRISMA principles, including all studies reporting the use of technological devices for motor function assessment in NMDs from 2000 to 2021. We also summarized the evidence on measurement properties (validity, reliability, responsiveness) of the analyzed technological outcome measures.

RESULTS

One-hundred studies fulfilled the selection criteria, most of them published in the last ten years. We defined four categories that gathered similar technologies: gait analysis tools, for clinical assessment of pace and posture; continuous monitoring of physical activity with inertial sensors, that allow "unsupervised" activity assessment; upper limb evaluation tools, including Kinect-based outcome measures to assess the reachable workspace; and new muscle strength assessment tools, such as Myotools. Inertial sensors have the evident advantage of being applied in the "in-home" setting, which has become especially appealing with the Covid-19 pandemic, although poor evidence from psychometric property assessment and results of the analyzed studies may limit their research application. Both Kinect-based outcome measures and Myotools have been already validated in multicenter studies and different NMDs, showing excellent characteristics for application in clinical trials.

CONCLUSION

This overview is intended to raise awareness on the potential of the different TOMs in the neuromuscular field and be an informative source for the design of future clinical trials, particularly in the era of telemedicine.

Considerations to address missing data when deriving clinical trial endpoints from digital health technologies

Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspective in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.

Stride Velocity 95th Centile: Insights into Gaining Regulatory Qualification of the First Wearable-Derived Digital Endpoint for use in Duchenne Muscular Dystrophy Trials

In 2019, stride velocity 95th centile (SV95C) became the first wearable-derived digital clinical outcome assessment (COA) qualified by the European Medicines Agency (EMA) for use as a secondary endpoint in trials for Duchenne muscular dystrophy. SV95C was approved via the EMA’s qualification pathway for novel methodologies for medicine development, which is a voluntary procedure for assessing the regulatory acceptability of innovative methods used in pharmaceutical research and development. SV95C is an objective, real-world digital ambulation measure of peak performance, representing the speed of the fastest strides taken by the wearer over a recording period of 180 hours. SV95C is correlated with traditional clinic-based assessments of motor function and has greater sensitivity to clinical change over 6 months than other wearable-derived stride variables, for example, median stride length or velocity. SV95C overcomes many limitations of episodic, clinic-based motor function testing, allowing the assessment of ambulation ability between clinic visits and under free-living conditions. Here we highlight considerations and challenges in developing SV95C using evidence generated by a high-performance wearable sensor. We also provide a commentary of the device’s technical capabilities, which were a determining factor in the regulatory approval of SV95C. This article aims to provide insights into the methods employed, and the challenges faced, during the regulatory approval process for researchers developing new digital tools for patients with diseases that affect motor function.

First Regulatory Qualification of a Novel Digital Endpoint in Duchenne Muscular Dystrophy: A Multi-Stakeholder Perspective on the Impact for Patients and for Drug Development in Neuromuscular Diseases

Background

Functional outcome measures used to assess efficacy in clinical trials of investigational treatments for rare neuromuscular diseases like Duchenne muscular dystrophy (DMD) are performance-based tasks completed by the patient during hospital visits. These are prone to bias and may not reflect motor abilities in real-world settings. Digital tools, such as wearable devices and other remote sensors, provide the opportunity for continuous, objective, and sensitive measurements of functional ability during daily life. Maintaining ambulation is of key importance to individuals with DMD. Stride velocity 95th centile (SV95C) is the first wearable acquired digital endpoint to receive qualification from the European Medicines Agency (EMA) to quantify the ambulation ability of ambulant DMD patients aged ≥5 years in drug therapeutic studies; it is also currently under review for the US Food and Drug Administration (FDA) qualification.

Summary

Focusing on SV95C as a key example, we describe perspectives of multiple stakeholders on the promise of novel digital endpoints in neuromuscular disease drug development.

Lessons Learned from Discontinued Clinical Developments in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is an X-linked condition caused by a deficiency of functional dystrophin protein. Patients experience progressive muscle weakness, cardiomyopathy and have a decreased life expectancy. Standards of care, including treatment with steroids, and multidisciplinary approaches have extended the life expectancy and improved the quality of life of patients. In the last 30 years, several compounds have been assessed in preclinical and clinical studies for their ability to restore functional dystrophin levels or to modify pathways involved in DMD pathophysiology. However, there is still an unmet need with regards to a disease-modifying treatment for DMD and the attrition rate between early-phase and late-phase clinical development remains high. Currently, there are 40 compounds in clinical development for DMD, including gene therapy and antisense oligonucleotides for exon skipping. Only five of them have received conditional approval in one jurisdiction subject to further proof of efficacy. In this review, we present data of another 16 compounds that failed to complete clinical development, despite positive results in early phases of development in some cases. We examine the reasons for the high attrition rate and we suggest solutions to avoid similar mistakes in the future.

Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

Home-based gait analysis as an exploratory endpoint during a multicenter phase 1 trial in limb girdle muscular dystrophy type R2 and facioscapulohumeral muscular dystrophy

INTRODUCTION/AIMS

Limb girdle muscular dystrophy type 2B (LGMDR2) and facioscapulohumeral muscular dystrophy (FSHD) are genetic muscular dystrophies with an increasing number of potential therapeutic approaches. The aim of this study is to report the data of exploratory digital outcomes extracted from wearable magneto-inertial sensors used in a non-controlled environment for ambulant patients with FSHD and LGMDR2 in a short-term, multicenter clinical study.

METHODS

Digital outcomes (stride length, stride speed, and walk parameters in a non-controlled environment) were used as exploratory outcomes in the open-label study ATYR1940-C-004 in ambulant patients during the 3 mo of ATYR1940 treatment and 1 mo of follow-up. Activity and gait variables were calculated from the data recorded in 30-day sub-periods using the sensors. For each sub-period, activity and gait parameters were compared between FSHD and LGMDR2 patients. Change from baseline over the 4-mo study period was assessed.

RESULTS

Ten patients (5 FSHD, 5 LGMDR2) were ambulant and compliant for analysis. Gait parameters, but not activity variables, were significantly lower in LGMDR2 compared to FSHD patients at baseline. Longitudinal analyses showed a slight but significant decrease in stride speed at month 4 for all subjects. Activity variables such as total number of strides per day were highly variable from month to month in individual patients, and no visit effects were found for this variable.

DISCUSSION

The present study suggests that home-recorded stride speed constitutes a precise and sensitive outcome in ambulant patients with FSHD and LGMDR2.

Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment

Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich contextual data to augment digital measures. Currently, the availability of devices is no longer the main factor limiting adoption but rather the ability to integrate fit-for-purpose BioMeTs reliably and safely into clinical care. We provide a critical review of the state of art for multimodal BioMeTs in clinical care and identify three unmet clinical needs: 1) expand the abilities of existing ambulatory unimodal BioMeTs; 2) adapt standardized clinical test protocols ("spot checks'') for use under free living conditions; and 3) develop novel applications to manage rehabilitation and chronic diseases. As the field is still in an early and quickly evolving state, we make practical recommendations: 1) to select appropriate BioMeTs; 2) to develop composite digital measures; and 3) to design interoperable software to ingest, process, delegate, and visualize the data when deploying novel clinical applications. Multimodal BioMeTs will drive the evolution from in-clinic assessments to at-home data collection with a focus on prevention, personalization, and long-term outcomes by empowering health care providers with knowledge, delivering convenience, and an improved standard of care to patients.

Digital endpoints for self-administered home-based functional assessment in pediatric Friedreich's ataxia

Background

Friedreich’s ataxia is an inherited, progressive, neurodegenerative disease that typically begins in childhood. Disease severity is commonly assessed with rating scales, such as the modified Friedreich’s Ataxia Rating Scale, which are usually administered in the clinic by a neurology specialist.

Objective

This study evaluated the utility of home‐based, self‐administered digital endpoints in children with Friedreich’s ataxia and unaffected controls and their relationship to standard clinical rating scales.

Methods

In a cross‐sectional study with 25 participants (13 with Friedreich’s ataxia and 12 unaffected controls, aged 6–15 years), home‐based digital endpoints that reflect activities of daily living were recorded over 1 week. Domains analyzed were hand motor function with a digitized drawing, automated analysis of speech with a recorded oral diadochokinesis test, and gait and balance with wearable sensors.

Results

Hand‐drawing and speech tests were easy to conduct and generated high‐quality data. The sensor‐based gait and balance tests suffered from technical limitations in this study setup. Several parameters discriminated between groups or correlated strongly with modified Friedreich’s Ataxia Rating Scale total score and activities of daily living total score in the Friedreich’s ataxia group. Hand‐drawing parameters also strongly correlated with standard 9‐hole peg test scores.

Interpretation

Deploying digital endpoints in home settings is feasible in this population, results in meaningful and robust data collection, and may allow for frequent sampling over longer periods of time to track disease progression. Care must be taken when training participants, and investigators should consider the complexity of the tasks and equipment used.

E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 1st eNMD Congress: Nice, France, March 22-23, 2019

By definition, neuromuscular diseases are rare and fluctuating in terms of symptoms; patients are often lately diagnosed, do not have enough information to understand their condition and be proactive in their management. Usually, insufficient resources or services are available, leading to patients' social burden. From a medical perspective, the rarity of such diseases leads to the unfamiliarity of the medical staff and caregiver and an absence of consensus in disease assessment, treatment, and management. Innovations have to be developed in response to patients' and physicians' unmet needs.It is vital to improve several aspects of patients' quality of life with a better comprehension of their disease, simplify their management and follow-up, help their caregiver, and reduce the social and economic burden for living with a rare debilitating disease. Database construction regrouping patients' data and symptoms according to specific country registration on data privacy will be critical in establishing a clear consensus on neuromuscular disease treatment.Clinicians also need technological innovations to help them recognize neuromuscular diseases, find the best therapeutic approach based on medical consensus, and tools to follow patients' states regularly. Diagnosis also has to be improved by implementing automated systems to analyze a considerable amount of data, representing a significant step forward to accelerate the diagnosis and the patients' follow up. Further, the development of new tools able to precisely measure specific outcomes reliably is of the matter of importance in clinical trials to assess the efficacy of a newly developed compound.In this context, creation of an expert community is essential to communicate and share ideas. To this end, 97 clinicians, healthcare professionals, researchers, and representatives of private companies from 9 different countries met to discuss the new perspective and challenges to develop and implement innovative tools in the field of neuromuscular diseases.

Development of a Clinical Global Impression of Change (CGI-C) and a Caregiver Global Impression of Change (CaGI-C) measure for ambulant individuals with Duchenne muscular dystrophy

Background

In clinical trials for rare diseases, such as Duchenne muscular dystrophy, clinical outcome assessments (COA) used to assess treatment benefit are often generic and may not be sensitive enough to detect change in specific patient populations. Thus, there is a need for disease specific COAs that track meaningful change among individuals. When developing such measures, input from clinicians, caregivers and patients is critical for assessing clinically relevant concepts and ensuring validity of the measure.

Method

The aim of this study was to develop two Duchenne-specific global impression items for use in clinical trials. The development of the Duchenne Clinical Global Impression of Change (CGI-C) and Caregiver Global Impression of Change (CaGI-C) was informed by findings from concept elicitation (CE) interviews with clinicians, caregivers and individuals with Duchenne. Through cognitive debriefing (CD) interviews, clinicians and caregivers evaluated draft CGI-C and CaGI-C items to ensure relevance and understanding of the items and instructions. Suggestions made during the CD interviews were incorporated into the finalized CGI-C and CaGI-C measures.

Results

The symptoms most frequently reported by clinicians, caregivers and individuals with Duchenne were muscle weakness, fatigue, cardiac difficulties and pain. Regarding physical functioning, all three populations noted that small changes in functional ability were meaningful, particularly when independence was impacted. Caregivers and clinicians reported that changes in speed, endurance and quality of movement were important, as was improvement in the ability of individuals to keep up with their peers. A change in the ability to complete everyday activities was also significant to families. These results were used to create two global impression of change items and instruction documents for use by clinicians (CGI-C) and caregivers (CaGI-C). Overall, both items were well understood by participants. The descriptions and examples developed from the CE interviews were reported to be relevant and appropriate for illustrating different levels of meaningful change in patients with Duchenne. Modifications were made based on caregiver and clinician CD feedback .

Conclusions

As part of a holistic measurement strategy, such COA can be incorporated into the clinical trial setting to assess global changes in relevant symptoms and functional impacts associated with Duchenne.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-021-01813-w.

Normative data on spontaneous stride velocity, stride length, and walking activity in a non-controlled environment

Background

Normative data are necessary for validation of new outcome measures. Recently, the 95th centile of stride speed was qualified by the European Medicines Agency as a valid secondary outcome for clinical trials in subjects with Duchenne muscular dystrophy. This study aims to obtain normative data on spontaneous stride velocity and length in a non-controlled environment and their evolution after 12 months.

Method

Ninety-one healthy volunteers (50 females, 41 males), with a mean age of 16 years and 2 months, were recruited and assessed at baseline and 12 months later. The 4-stair climb, 6-min walk test, 10-m walk test and rise from floor assessments were performed. Stride length, stride velocity, and the distance walked per hour were studied in an everyday setting for one month after each evaluation.

Results

Of the 91 subjects assessed, 82 provided more than 50 h of recordings at baseline; and 73 subjects provided the same at the end of the year. We observed significant positive correlations of the stride length with age and height of participants, and a significant increase of the median stride length in children after the period. In this group, the 95th centile stride velocity was not correlated with age and was stable after one year. All measures but the 10MWT were stable in adults after a one-year period.

Conclusion

This study provides with data on the influence of age, height, and gender on stride velocity and length as well as accounting for natural changes after one year in controls.

Wearable Technologies for Children with Chronic Illnesses: An Exploratory Approach

OBJECTIVE

To determine the utility of wearable technologies in physical activity assessment in three paediatric diseases, namely, Niemann-Pick C (NP-C), Juvenile Idiopathic Arthritis (JIA) and Duchenne Muscular Dystrophy (DMD).

DESIGN

Exploratory study SETTING AND PATIENTS: Thirty children were recruited across three UK hospitals (Royal Manchester's Children Hospital, Great Ormond Street Children's Hospital, and the Great North Children's Hospital). Ten were diagnosed with NP-C, eight with DMD and twelve with JIA.

INTERVENTION

All participants completed the 6-min walk test (6MWT) at enrolment. Patients were provided with disease-specific smartphone apps paired with a wearable device via Bluetooth. Ambulation was recorded in 30-min epochs measuring average daily maximum (ADM), average daily steps (ADS) and average daily steps per 30-min epoch (ASE).

RESULTS

Median 6MWT results were 450 m, 325 m and 434.5 m for the NP-C, DMD and JIA cohorts, respectively. Wearable data capture was feasible in all three disease cohorts, although complete data capture was not sustained. A statistically significant between-cohort difference was identified for ADM, ADS and ASE. Statistically significant differences were found between DMD/JIA for ADM; NP-C/DMD for ADS and DMD/JIA for ASE.

DISCUSSION

Wearable sensor technologies have the potential to provide additional information for our understanding of ambulation in chronic paediatric disease. The wearable devices were easy to use and popular with patients although key features need to be addressed to appropriately meet study objectives. As the technology continues to evolve at a rapid pace, opportunities to implement child friendly solutions are already available.

Evaluation, Acceptance, and Qualification of Digital Measures: From Proof of Concept to Endpoint

To support the successful adoption of digital measures into internal decision making and evidence generation for medical product development, we present a unified lexicon to aid communication throughout this process, and highlight key concepts including the critical role of participant engagement in development of digital measures. We detail the steps of bringing a successful proof of concept to scale, focusing on key decisions in the development of a new digital measure: asking the right question, optimized approaches to evaluating new measures, and whether and how to pursue qualification or acceptance. Building on the V3 framework for establishing verification and analytical and clinical validation, we discuss strategic and practical considerations for collecting this evidence, illustrated with concrete examples of trailblazing digital measures in the field.

Digitally Enabled, Patient-Centric Clinical Trials: Shifting the Drug Development Paradigm

The rapidly advancing field of digital health technologies provides a great opportunity to radically transform the way clinical trials are conducted and to shift the clinical trial paradigm from a site-centric to a patient-centric model. Merck's (Kenilworth, NJ) digitally enabled clinical trial initiative is focused on introduction of digital technologies into the clinical trial paradigm to reduce patient burden, improve drug adherence, provide a means of more closely engaging with the patient, and enable higher quality, faster, and more frequent data collection. This paper will describe the following four key areas of focus from Merck's digitally enabled clinical trials initiative, along with corresponding enabling technologies: (i) use of technologies that can monitor and improve drug adherence (smart dosing), (ii) collection of pharmacokinetic (PK), pharmacodynamic (PD), and biomarker samples in an outpatient setting (patient-centric sampling), (iii) use of digital devices to collect and measure physiological and behavioral data (digital biomarkers), and (iv) use of data platforms that integrate digital data streams, visualize data in real-time, and provide a means of greater patient engagement during the trial (digital platform). Furthermore, this paper will discuss the synergistic power in implementation of these approaches jointly within a trial to enable better understanding of adherence, safety, efficacy, PK, PD, and corresponding exposure-response relationships of investigational therapies as well as reduced patient burden for clinical trial participation. Obstacle and challenges to adoption and full realization of the vision of patient-centric, digitally enabled trials will also be discussed.

Development of Novel, Value-Based, Digital Endpoints for Clinical Trials: A Structured Approach Toward Fit-for-Purpose Validation

Novel digital endpoints gathered via wearables, small devices, or algorithms hold great promise for clinical trials. However, implementation has been slow because of a lack of guidelines regarding the validation process of these new measurements. In this paper, we propose a pragmatic approach toward selection and fit-for-purpose validation of digital endpoints. Measurements should be value-based, meaning the measurements should directly measure or be associated with meaningful outcomes for patients. Devices should be assessed regarding technological validity. Most importantly, a rigorous clinical validation process should appraise the tolerability, difference between patients and controls, repeatability, detection of clinical events, and correlation with traditional endpoints. When technically and clinically fit-for-purpose, case building in interventional clinical trials starts to generate evidence regarding the response to new or existing health-care interventions. This process may lead to the digital endpoint replacing traditional endpoints, such as clinical rating scales or questionnaires in clinical trials. We recommend initiating more data-sharing collaborations to prevent unnecessary duplication of research and integration of value-based measurements in clinical care to enhance acceptance by health-care professionals. Finally, we invite researchers and regulators to adopt this approach to ensure a timely implementation of digital measurements and value-based thinking in clinical trial design and health care. SIGNIFICANCE STATEMENT: Novel digital endpoints are often cited as promising for the clinical trial of the future. However, clear validation guidelines are lacking in the literature. This paper contains pragmatic criteria for the selection, technical validation, and clinical validation of novel digital endpoints and provides recommendations for future work and collaboration.

Natural history of Type 2 and 3 spinal muscular atrophy: 2-year NatHis-SMA study

Objective

To characterize the natural history of spinal muscular atrophy (SMA) over 24 months using innovative measures such as wearable devices, and to provide evidence for the sensitivity of these measures to determine their suitability as endpoints in clinical trials.

Methods

Patients with Type 2 and 3 SMA (N = 81) with varied functional abilities (sitters, nonsitters, nonambulant, and ambulant) who were not receiving disease‐modifying treatment were assessed over 24 months: motor function (Motor Function Measure [MFM]), upper limb strength (MyoGrip, MyoPinch), upper limb activity (ActiMyo^®), quantitative magnetic resonance imaging (fat fraction [FF_T2] mapping and contractile cross‐sectional area [C‐CSA]), pulmonary function (forced vital capacity [FVC], peak cough flow, maximum expiratory pressure, maximum inspiratory pressure, and sniff nasal inspiratory pressure), and survival of motor neuron (SMN) protein levels.

Results

MFM32 scores declined significantly over 24 months, but not 12 months. Changes in upper limb activity could be detected over 6 months and continued to decrease significantly over 12 months, but not 24 months. Upper limb strength decreased significantly over 12 and 24 months. FVC declined significantly over 12 months, but not 24 months. FF_T2 increased over 12 and 24 months, although not with statistical significance. A significant increase in C‐CSA was observed at 12 but not 24 months. Blood SMN protein levels were stable over 12 and 24 months.

Interpretation

These data demonstrate that the MFM32, MyoGrip, MyoPinch, and ActiMyo^® enable the detection of a significant decline in patients with Type 2 and 3 SMA over 12 or 24 months.

The Path Forward for Digital Measures: Suppressing the Desire to Compare Apples and Pineapples

Digital measures are becoming more prevalent in clinical development. Methods for robust evaluation are increasingly well defined, yet the primary barrier for digital measures to transition beyond exploratory usage often relies on a comparison to the existing standards. This article focuses on how researchers should approach the complex issue of comparing across assessment modalities. We discuss comparisons of subjective versus objective assessments, or performance-based versus behavioral measures, and we pay particular attention to the situation where the expected association may be poor or nonlinear. We propose that, rather than seeking to replace the standard, research should focus on a structured understanding of how the new measure augments established assessments, with the ultimate goal of developing a more complete understanding of what is meaningful to patients.

Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

Predicting Subjective Recovery from Lower Limb Surgery Using Consumer Wearables

Introduction

A major challenge in the monitoring of rehabilitation is the lack of long-term individual baseline data which would enable accurate and objective assessment of functional recovery. Consumer-grade wearable devices enable the tracking of individual everyday functioning prior to illness or other medical events which necessitate the monitoring of recovery trajectories.

Methods

For 1,324 individuals who underwent surgery on a lower limb, we collected their Fitbit device data of steps, heart rate, and sleep from 26 weeks before to 26 weeks after the self-reported surgery date. We identified subgroups of individuals who self-reported surgeries for bone fracture repair (n = 355), tendon or ligament repair/reconstruction (n = 773), and knee or hip joint replacement (n = 196). We used linear mixed models to estimate the average effect of time relative to surgery on daily activity measurements while adjusting for gender, age, and the participant-specific activity baseline. We used a sub-cohort of 127 individuals with dense wearable data who underwent tendon/ligament surgery and employed XGBoost to predict the self-reported recovery time.

Results

The 1,324 study individuals were all US residents, predominantly female (84%), white or Caucasian (85%), and young to middle-aged (mean age 36.2 years). We showed that 12 weeks pre- and 26 weeks post-surgery trajectories of daily behavioral measurements (steps sum, heart rate, sleep efficiency score) can capture activity changes relative to an individual's baseline. We demonstrated that the trajectories differ across surgery types, recapitulate the documented effect of age on functional recovery, and highlight differences in relative activity change across self-reported recovery time groups. Finally, using a sub-cohort of 127 individuals, we showed that long-term recovery can be accurately predicted, on an individual level, only 1 month after surgery (AUROC 0.734, AUPRC 0.8). Furthermore, we showed that predictions are most accurate when long-term, individual baseline data are available.

Discussion

Leveraging long-term, passively collected wearable data promises to enable relative assessment of individual recovery and is a first step towards data-driven intervention for individuals.

Gait Characteristics Harvested During a Smartphone-Based Self-Administered 2-Minute Walk Test in People with Multiple Sclerosis: Test-Retest Reliability and Minimum Detectable Change

The measurement of gait characteristics during a self-administered 2-minute walk test (2MWT), in persons with multiple sclerosis (PwMS), using a single body-worn device, has the potential to provide high-density longitudinal information on disease progression, beyond what is currently measured in the clinician-administered 2MWT. The purpose of this study is to determine the test-retest reliability, standard error of measurement (SEM) and minimum detectable change (MDC) of features calculated on gait characteristics, harvested during a self-administered 2MWT in a home environment, in 51 PwMS and 11 healthy control (HC) subjects over 24 weeks, using a single waist-worn inertial sensor-based smartphone. Excellent, or good to excellent test-retest reliability were observed in 58 of the 92 temporal, spatial and spatiotemporal gait features in PwMS. However, these were less reliable for HCs. Low SEM% and MDC% values were observed for most of the distribution measures for all gait characteristics for PwMS and HCs. This study demonstrates the inter-session test-retest reliability and provides an indication of clinically important change estimates, for interpreting the outcomes of gait characteristics measured using a body-worn smartphone, during a self-administered 2MWT. This system thus provides a reliable measure of gait characteristics in PwMS, supporting its application for the longitudinal assessment of gait deficits in this population.

Remote Digital Monitoring for Medical Product Development

The use of digital health products has gained considerable interest as a new way to improve therapeutic research and development. Although these products are being adopted by various industries and stakeholders, their incorporation in clinical trials has been slow due to a disconnect between the promises of digital products and potential risks in using these new technologies in the absence of regulatory support. The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium hosted a public workshop to address challenges and opportunities in this field. Important characteristics of tool development were addressed in a series of presentations, case studies, and open panel sessions. The workshop participants endorsed the usefulness of an evidentiary criteria framework, highlighted the importance of early patient engagement, and emphasized the potential impact of digital monitoring tools and precompetitive collaborations. Concerns were expressed about the lack of real‐life validation examples and the limitations of legacy standards used as a benchmark for novel tool development and validation. Participants recognized the need for novel analytical and statistical approaches to accommodate analyses of these novel data types. Future directions are to harmonize definitions to build common methodologies and foster multidisciplinary collaborations; to develop approaches toward integrating digital monitoring data with the totality of the data in clinical trials, and to continue an open dialog in the community. There was a consensus that all these efforts combined may create a paradigm shift of how clinical trials are planned, conducted, and results brought to regulatory reviews.

Assessing Physical Activity Using Accelerometers in Youth with Duchenne Muscular Dystrophy

BACKGROUND

Physical activity, assessed by accelerometers, has been proposed as a quantitative outcome measure for patients with DMD, but research is limitedObjective:To assess the total amount and patterns of physical activity in patients with DMD using accelerometers.

METHODS

Physical activity was assessed in patients with DMD (n = 49, 13.6±4.0-year-old) and age- and sex-matched healthy controls (n = 15, 14.0±2.3-year-old) using wrist- and ankle-worn accelerometers. To assess the amount of activity, accelerometer recordings were converted into acceleration estimates (counts/min). Patterns of activity were assessed as the time that participants spent in sedentary, low-intensity, and moderate-to-vigorous physical activity categories. The sedentary category was divided into three (sedentary -1, -2, and -3) and the low-intensity into two (low-intensity-1, and -2) subcategories.

RESULTS

Physical activity across intensity categories differed between study groups (p < 0.001). Patients with DMD spent on average 98.8% of their daytime in the sedentary and low-intensity categories. Compared to non-ambulatory, ambulatory patients spent more time in sedentary-3 and low-intensity-2 subcategories (p < 0.001). Amount of activity was lower in all patients than controls (p < 0.05) and in non-ambulatory than ambulatory patients and controls (p < 0.001), but similar between ambulatory patients and controls. Activity measures in patients were significantly affected by age and ambulation status (p < 0.05) but not corticosteroid use.

CONCLUSION

Patients with DMD spent most of their daytime in sedentary and low-intensity activities. Dividing these intensities into three and two subcategories, respectively, allows better characterization of activity patterns in DMD. Ambulation status and age but not corticosteroid use affected activity measures in patients with DMD.