A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach

Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.

[1]  Eva Balcells,et al.  Physical activity in COPD patients: patterns and bouts , 2012, European Respiratory Journal.

[2]  Jennifer C. Goldsack,et al.  Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative. , 2019, Digital biomarkers.

[3]  Lynn Rochester,et al.  Research with older people in a world with COVID-19: identification of current and future priorities, challenges and opportunities , 2020, Age and ageing.

[4]  Clemens Becker,et al.  Toward a Regulatory Qualification of Real-World Mobility Performance Biomarkers in Parkinson’s Patients Using Digital Mobility Outcomes , 2020, Sensors.

[5]  A. Cereatti,et al.  Inter-leg Distance Measurement as a Tool for Accurate Step Counting in Patients with Multiple Sclerosis , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[6]  Lynn Rochester,et al.  The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control , 2019, Brain sciences.

[7]  G. Onder,et al.  Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. , 2020, JAMA.

[8]  Alan M Jette,et al.  Late life function and disability instrument: I. Development and evaluation of the disability component. , 2002, The journals of gerontology. Series A, Biological sciences and medical sciences.

[9]  Bastiaan R. Bloem,et al.  Measurement instruments to assess posture, gait, and balance in Parkinson's disease: Critique and recommendations , 2016, Movement disorders : official journal of the Movement Disorder Society.

[10]  J. Kurtzke Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[11]  Idsart Kingma,et al.  A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly , 2016, Journal of NeuroEngineering and Rehabilitation.

[12]  Jennifer C. Goldsack,et al.  Digital Medicine: A Primer on Measurement , 2019, Digital Biomarkers.

[13]  Thierry Troosters,et al.  The PROactive instruments to measure physical activity in patients with chronic obstructive pulmonary disease , 2015, European Respiratory Journal.

[14]  Jaap H. van Dieën,et al.  Physical Performance and Physical Activity in Older Adults: Associated but Separate Domains of Physical Function in Old Age , 2015, PloS one.

[15]  M Daumer,et al.  Considerations for development of an evidence dossier to support the use of mobile sensor technology for clinical outcome assessments in clinical trials. , 2020, Contemporary clinical trials.

[16]  L. Ferrucci,et al.  A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. , 1994, Journal of gerontology.

[17]  Francesca Cerreta,et al.  Digital technologies for medicines: shaping a framework for success , 2020, Nature reviews. Drug discovery.

[18]  K. Takakusaki Functional Neuroanatomy for Posture and Gait Control , 2017, Journal of movement disorders.

[19]  Jeffrey M. Hausdorff,et al.  Long-term unsupervised mobility assessment in movement disorders , 2020, The Lancet Neurology.

[20]  Alan Godfrey,et al.  Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis , 2017, Physiological measurement.

[21]  B. Galna,et al.  Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length , 2016, Journal of NeuroEngineering and Rehabilitation.

[22]  Sanjay Ranka,et al.  Adaptive walk detection algorithm using activity counts , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[23]  Jeffrey A Cohen,et al.  Validity of the timed 25-foot walk as an ambulatory performance outcome measure for multiple sclerosis , 2017, Multiple sclerosis.

[24]  Kamiar Aminian,et al.  An Inertial Sensor-Based Method for Estimating the Athlete's Relative Joint Center Positions and Center of Mass Kinematics in Alpine Ski Racing , 2017, Front. Physiol..

[25]  Jeffrey M. Hausdorff,et al.  Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait , 2014, Journal of NeuroEngineering and Rehabilitation.

[26]  Diane Podsiadlo,et al.  The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons , 1991, Journal of the American Geriatrics Society.

[27]  A J Thompson,et al.  Measuring the impact of MS on walking ability , 2003, Neurology.

[28]  S. Fritz,et al.  White paper: "walking speed: the sixth vital sign". , 2009, Journal of geriatric physical therapy.

[29]  Subashan Perera,et al.  Gait Speed Predicts Incident Disability: A Pooled Analysis. , 2016, The journals of gerontology. Series A, Biological sciences and medical sciences.

[30]  J. Jankovic,et al.  Movement Disorder Society‐sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS): Scale presentation and clinimetric testing results , 2008, Movement disorders : official journal of the Movement Disorder Society.

[31]  Suchi Saria,et al.  Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. , 2019, Digital biomarkers.

[32]  Sungjae Hwang,et al.  Computational methods to detect step events for normal and pathological gait evaluation using accelerometer , 2010 .

[33]  Celso Arango,et al.  Digital health technologies in clinical trials for central nervous system drugs: an EU regulatory perspective , 2020, Nature Reviews Drug Discovery.

[34]  C. Mazzà,et al.  Free‐living monitoring of Parkinson's disease: Lessons from the field , 2016, Movement disorders : official journal of the Movement Disorder Society.

[35]  Sean Pearson,et al.  Continuous Monitoring of Turning in Patients with Movement Disability , 2013, Sensors.

[36]  Martijn A Spruit,et al.  “Can do” versus “do do”: A Novel Concept to Better Understand Physical Functioning in Patients with Chronic Obstructive Pulmonary Disease , 2019, Journal of clinical medicine.

[37]  R. Roubenoff,et al.  How soon will digital endpoints become a cornerstone for future drug development? , 2019, Drug discovery today.

[38]  Claudia Mazzà,et al.  Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods. , 2016, Gait & posture.

[39]  S. Studenski,et al.  Gait speed and survival in older adults. , 2011, JAMA.

[40]  Bjoern M. Eskofier,et al.  Unsupervised harmonic frequency-based gait sequence detection for Parkinson's disease , 2019, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[41]  Fernando Seoane,et al.  Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system , 2019, Ergonomics.

[42]  Enkelejda Miho,et al.  Traditional and Digital Biomarkers: Two Worlds Apart? , 2019, Digital Biomarkers.

[43]  Julius Hannink,et al.  Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson’s Disease , 2019, Sensors.

[44]  Kamiar Aminian,et al.  Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.

[45]  Alan Godfrey,et al.  Ambulatory activity in incident Parkinson’s: more than meets the eye? , 2013, Journal of Neurology.

[46]  Paul Watson,et al.  Developing cloud applications using the e-Science Central platform , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[47]  Alan Godfrey,et al.  Defining ambulatory bouts in free-living activity: Impact of brief stationary periods on bout metrics. , 2015, Gait & posture.

[48]  A. Woodcock,et al.  Two-, six-, and 12-minute walking tests in respiratory disease. , 1982, British medical journal.

[49]  Beatrix Vereijken,et al.  Multiple days of monitoring are needed to obtain a reliable estimate of physical activity in hip-fracture patients. , 2014, Journal of aging and physical activity.

[50]  Thierry Troosters,et al.  Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review , 2020, BMJ Open.

[51]  Jennifer C. Goldsack,et al.  Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs) , 2020, npj Digital Medicine.

[52]  Jochen Klenk,et al.  Validation of an accelerometer for measurement of activity in frail older people. , 2018, Gait & posture.

[53]  Maria Hagströmer,et al.  Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis , 2019, BMJ.

[54]  Beatrix Vereijken,et al.  A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology—The ADAPT Study Data-Set , 2017, Sensors.