Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape.

[1]  Guang-Zhong Yang,et al.  Sensor Placement for Activity Detection Using Wearable Accelerometers , 2010, 2010 International Conference on Body Sensor Networks.

[2]  Y Ben-Shlomo,et al.  Rivastigmine for gait stability in patients with Parkinson's disease (ReSPonD): a randomised, double-blind, placebo-controlled, phase 2 trial , 2016, The Lancet Neurology.

[3]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[4]  Max A. Little,et al.  Machine learning for large‐scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures , 2016, Movement disorders : official journal of the Movement Disorder Society.

[5]  Armin Haller,et al.  Semantic Sensor Network Ontology , 2017 .

[6]  Christian Gossens Remote Digital Biomarker Monitoring Bringing a smartphonebased diagnostic test for Parkinson's Disease progression into an interventional trial , 2015 .

[7]  Zelma H. T. Kiss,et al.  Smart watch accelerometry for analysis and diagnosis of tremor , 2014, Journal of Neuroscience Methods.

[8]  Harishchandra Dubey,et al.  EchoWear: smartwatch technology for voice and speech treatments of patients with Parkinson's disease , 2015, Wireless Health.

[9]  Suchi Saria,et al.  Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score , 2018, JAMA neurology.

[10]  Andong Zhan,et al.  Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab , 2018, Sensors.

[11]  Jennifer G. Dy,et al.  Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[12]  H R Solbrig,et al.  Metadata and the reintegration of clinical information: ISO 11179. , 2000, M.D. computing : computers in medical practice.

[13]  Filippo Cavallo,et al.  Empowering Patients in Self-Management of Parkinson's Disease Through Cooperative ICT Systems , 2015 .

[14]  Max A. Little,et al.  Wearable Sensors in Huntington Disease: A Pilot Study. , 2016, Journal of Huntington's disease.

[15]  M. Ringel,et al.  Innovation in Regulatory Science Is Meeting Evolution of Clinical Evidence Generation , 2019, Clinical pharmacology and therapeutics.

[16]  Solbrig Hr Metadata and the reintegration of clinical information: ISO 11179. , 2000 .

[17]  Elena S. Izmailova,et al.  Wearable Devices in Clinical Trials: Hype and Hypothesis , 2018, Clinical pharmacology and therapeutics.

[18]  B. Bloem,et al.  Quantitative wearable sensors for objective assessment of Parkinson's disease , 2013, Movement disorders : official journal of the Movement Disorder Society.

[19]  S. Masand,et al.  Accelerating Adoption of Patient-Facing Technologies in Clinical Trials: A Pharmaceutical Industry Perspective on Opportunities and Challenges , 2019, Therapeutic innovation & regulatory science.

[20]  千葉吉輝 CDISC標準規格群は活用できるか?-わが国における状況と将来 4 CJUG(CDISC日本グループ)の活動と今後 1)SDTM(Study Data Tabulation Model) , 2013 .

[21]  Ned Jenkinson,et al.  Rapid tremor frequency assessment with the iPhone accelerometer. , 2011, Parkinsonism & related disorders.

[22]  Sean Khozin,et al.  Biometric monitoring devices for assessing end points in clinical trials: developing an ecosystem , 2017, Nature Reviews Drug Discovery.

[23]  Björn Eskofier,et al.  An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things , 2015, IEEE Journal of Biomedical and Health Informatics.

[24]  J. Jankovic Parkinson’s disease: clinical features and diagnosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[25]  Jeffrey M. Hausdorff,et al.  Gait alterations in healthy carriers of the LRRK2 G2019S mutation , 2011, Annals of neurology.

[26]  F. Cavallo,et al.  How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review , 2017, Front. Neurosci..

[27]  Suchi Saria,et al.  High Frequency Remote Monitoring of Parkinson's Disease via Smartphone: Platform Overview and Medication Response Detection , 2016, ArXiv.

[28]  D. Estrin,et al.  Open mHealth Architecture: An Engine for Health Care Innovation , 2010, Science.

[29]  Erik Duval,et al.  Metadata Standards: What, Who & Why , 2001, Journal of universal computer science (Online).

[30]  Dimitrios Hristu-Varsakelis,et al.  Smartphone-based evaluation of parkinsonian hand tremor: Quantitative measurements vs clinical assessment scores , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.