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Digitizing clinical trials
Open accessAbstract
Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a “digital clinical trial” involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost. In April 2019, the US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop bringing together experts in clinical trials, digital technology, and digital analytics to discuss strategies to implement the use of digital technologies in clinical trials while considering potential challenges. This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.
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“Digital clinical trials could leverage multiple sources of participants' data to enable automatic phenotyping and subgrouping 34 , which could be useful for adaptive clinical trial designs that use ongoing results to modify the trial in real time 39,40 .”
From Multimodal biomedical AI (2022) · cites this paper as [34]
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“Data from wearable technology (including heart rate, sleep, physical activity, electrocardiography, oxygen saturation and glucose monitoring) and smartphone-enabled self-reported questionnaires can be useful for monitoring clinical trial patients, identifying adverse events or ascertaining trial outcomes 34 .”
From Multimodal biomedical AI (2022) · cites this paper as [34]
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Cites
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087 · cited 1×
“Examples include wearable sweat sensing for glucose and lactate, as well as electrolytes 34 , cardiogenic chest wall vibrations to assess clinical status of patients with heart failure 35 , or structural health markers for knee joint injury obtained through a brace 36 .”
BackgroundParticipation in Cancer Clinical Trials: Race-, Sex-, and Age-Based Disparities
Murthy, V. H., Krumholz, H. M., & Gross, C. P. (2004) · JAMA · doi:10.1001/jama.291.22.2720 · Cited by 2,078
Self-citationA Randomized Trial of Hydroxychloroquine as Postexposure Prophylaxis for Covid-19
Boulware, D. R., Pullen, M. F., Bangdiwala, A. S., Pastick, K. A., Lofgren, S. M., Okafor, E. C., Skipper, C. P., Nascene, A. A., Nicol, M. R., Abassi, M., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2016638 · Cited by 1,349
Fogel, D. B. (2018) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2018.08.001 · Cited by 1,035
Kennedy-Martin, T., Curtis, S., Faries, D., Robinson, S., & Johnston, J. (2015) · Trials · doi:10.1186/s13063-015-1023-4 · Cited by 878
SMART on FHIR: a standards-based, interoperable apps platform for electronic health records
Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv189 · Cited by 829
Self-citationAccuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data
Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015) · JAMA · doi:10.1001/jama.2014.17841 · Cited by 694
Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.
Klasnja, P., Hekler, E. B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., & Murphy, S. A. (2015) · Health Psychology · doi:10.1037/hea0000305 · Cited by 671
Self-citationShcherbina, A., Mattsson, C., Waggott, D., Salisbury, H., Christle, J., Hastie, T., Wheeler, M., & Ashley, E. (2017) · Journal of Personalized Medicine · doi:10.3390/jpm7020003 · Cited by 625
Unger, J. M., Vaidya, R., Hershman, D. L., Minasian, L. M., & Fleury, M. E. (2018) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djy221 · Cited by 604
Research works printed but not linked: 1
- NIH NHLBI. Digital Clinical Trials Workshop: Creating a Vision for the Future. https://www.nhlbi.nih.gov/events/2019/digital-clinical-trials-workshop-creating-vision-future. Accessed 10 Jun 2020.
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Whitelaw, S., Pellegrini, D. M., Mamas, M. A., Cowie, M., & Van Spall, H. G. C. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab005 · Cited by 241
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Gumz, M. L., Shimbo, D., Abdalla, M., Balijepalli, R. C., Benedict, C., Chen, Y., Earnest, D. J., Gamble, K. L., Garrison, S., Gong, M. C., et al. (2022) · Hypertension · doi:10.1161/hypertensionaha.122.19372 · Cited by 105
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Rogers, A., Paoli, G. D., Subbarayan, S., Copland, R. R., Harwood, K. E., Coyle, J., Mitchell, L., MacDonald, T. M., & Mackenzie, I. S. (2021) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15205 · Cited by 96
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Harms, R., Ferrari, A., Meier, I. B., Martínková, J., Santus, E., Marino, N., Cirillo, D., Mellino, S., Catuara‐Solarz, S., Tarnanas, I., et al. (2022) · The EPMA Journal · doi:10.1007/s13167-022-00284-3 · Cited by 22
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Cosoli, G., Scalise, L., Poli, A., & Spinsante, S. (2021) · Health and Technology · doi:10.1007/s12553-021-00540-y · Cited by 22
Kadakia, K. T., Asaad, M., Adlakha, E. K., Overman, M. J., Checka, C. M., & Offodile, A. C. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00169 · Cited by 22
Ganti, V. G., Carek, A. M., Jung, H., Srivatsa, A. V., Cherry, D., Johnson, L. N., & Inan, O. T. (2021) · JMIR mhealth and uhealth · doi:10.2196/27466 · Cited by 25
Self-citationMarra, C., Gordon, W. J., & Stern, A. D. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-047341 · Cited by 22
An, S., Medda, A., Sawka, M. N., Hutto, C. J., Millard-Stafford, M. L., Appling, S., Richardson, K. L. S., & Inan, O. T. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3095176 · Cited by 22
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Tivay, A., Kramer, G. C., & Hahn, J. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3103141 · Cited by 21
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Naz‐McLean, S., Kim, A., Zimmer, A., Laibinis, H. H., Lapan, J., Tyman, P., Hung, J., Kelly, C. J., Nagireddy, H., Narayanan-Pandit, S., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269127 · Cited by 20
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Self-citationSmartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors
Haddad, F., Saraste, A., Santalahti, K., Pänkäälä, M., Kaisti, M., Kandolin, R. M., Simonen, P. P., Nammas, W., Dehkordi, K. J., Koivisto, T., et al. (2024) · JACC Heart Failure · doi:10.1016/j.jchf.2024.01.022 · Cited by 18
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Brezing, C., Luo, S. X., Mariani, J. J., & LEVIN, F. R. (2020) · Journal of Addiction Medicine · doi:10.1097/adm.0000000000000733 · Cited by 17
Mousavi, M., Manshadi, M. D., Soltani, M., Kashkooli, F. M., Rahmim, A., Mosavi, A. H., Kvasnica, M., Atkinson, P. M., Kovács, L., Koltay, A., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105511 · Cited by 18
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Wright, M., Matheson, J., Watson, T. M., Sproule, B. A., Foll, B. L., & Brands, B. (2024) · Substance Use & Misuse · doi:10.1080/10826084.2024.2330892 · Cited by 16
Jacobs, F., D’Amico, S., Zazzetti, E., Gaudio, M., Benvenuti, C., Saltalamacchia, G., Gerosa, R., Gentile, D., Lasagna, A., Pedrazzoli, P., et al. (2024) · Digital Health · doi:10.1177/20552076241288821 · Cited by 22
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Metadata
- Authors
- Inan, O. T., Tenaerts, P., Prindiville, S. A., Reynolds, H. R., Dizon, D. S., Cooper-Arnold, K., Turakhia, M., Pletcher, M. J., Preston, K. L., Krumholz, H. M., Marlin, B. M., Mandl, K. D., Klasnja, P., Spring, B., Iturriaga, E., Campo, R., Desvigne-Nickens, P., Rosenberg, Y., Steinhubl, S. R., & Califf, R. M.
- Affiliations
- Georgia Institute of Technology (US)
- Duke University (US)
- National Institutes of Health (US)
- New York University (US)
- Brown University (US)
- VA Palo Alto Health Care System (US)
- University of California, San Francisco (US)
- Yale New Haven Hospital (US)
- University of Massachusetts Amherst (US)
- Boston Children's Hospital (US)
- University of Michigan (US)
- Northwestern University (US)
- Scripps Research Institute (US)
- Corresponding author
- Inan, O. T.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2020-07-31
- Volume
- 3
- Issue
- 1
- Pages
- 101-101
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC7395804
- OpenAlex
- W3046739387
- Cited by
- 329
- References
- 44
- Citations by year
- 2026: 29 · 2025: 55 · 2024: 67 · 2023: 56 · 2022: 70 · 2021: 49 · 2020: 3
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (1922762)
- American Heart Association
- Bristol-Myers Squibb
- Pfizer
- AstraZeneca
- Sanofi
- Yale University
- Cytokinetics
- Shenzhen Center for Health Information
- Verily Life Sciences
- National Institutes of Health (UL1TR001114)
- U.S. Food and Drug Administration
- Abbott Vascular
- National Heart, Lung, and Blood Institute
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- digital health technologies, participant recruitment and retention, mobile health, wearable devices, application programming interfaces, data security and privacy, artificial intelligence in clinical trials, machine learning, data analytics, regulatory oversight
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