Advances in wearable technology have provided an effective tool to monitor outcomes and facilitate the delivery of interventions in patients across indications, especially those in need of rehabilitation interventions.
In fact, a recent study published in Nature Digital Medicine, demonstrated the effectiveness of wearable technology and machine learning-based algorithms to accurately track motor recovery of individuals with brain injuries, allowing clinicians to choose more effective interventions and to improve outcomes.1 With an estimated 5.3 million individuals living with disability as a result of traumatic brain injury (TBI)2, approaches, such as the one proposed in the paper, can solve unmet needs in TBI, by better capturing and understanding the severity of motor impairments and quality of movement patterns.
Further, using wearables, sensors and other digital health technologies can relieve a significant burden on healthcare systems and economies around the world, creating a need for innovative clinical research strategies to assess these new ways of managing and treating TBI. To meet these demands, emerging technologies are being increasingly applied to TBI clinical research. Here we discuss the role of evolving digital health technologies in TBI clinical research and the corresponding analytical techniques that can be used to harness growing amounts of data.
Emerging digital health technologies for TBI research
Successfully developing new strategies for diagnosing, monitoring and treating TBI, requires clinical researchers to constantly improve their understanding of how TBI impacts patients in the real world. As such, tailored digital health technologies can be used for remote monitoring of trial participants in the post-acute period to produce new insights on how TBI manifests in patients. For example, wearables and sensors can monitor symptoms of TBI and gather real-time data as patients go about their daily lives. Advancements in these technologies have made it possible to directly measure brain function, like electroencephalogram (EEG), or to collect and analyse aspects related to brain function such as sleep and speech patterns, gait or cognition.
Smartphones and apps can also be used to gather information about the manifestation of TBI in patients in a remote setting. For example, a study from 2013 demonstrated that smartphones can be leveraged as a reliable and validated assessment tool for providing real-time and remote analysis of cognitive and motor function when compared to gold standard approaches.3 Additionally, Apple’s ResearchKit platform can be a resource for developing tailored apps as well as a variety of patient performance tests and assessments, including tests of cognition, spatial memory or reaction time.4
Another area of TBI clinical research that can benefit from the advent of new digital health technologies is the identification of biomarkers. In a 2019 ICON survey, respondents ranked targeting biomarkers as the therapeutic approach most likely to benefit from digitally enabled technologies – a trend that we are already witnessing become reality.5 One pharma company, for example, has partnered with the Department of Defense to develop a digital health platform that analyses biomarkers released into the blood following a concussive brain injury.6 This platform would improve the current gold standard of using CT scans, which is not always readily available, enabling researchers to measure the severity of an individual's brain injury in a matter of minutes.6
Finally, some companies have pursued innovations in the assessment of TBI using video games and simulations. These approaches can measure symptoms of TBI including reaction time, memory or emotional responses by leveraging smartphone and tablet technology. For example, Akili Interactive Labs has developed a game that can be used to measure and assess a player’s ability to process distractions.7 This game platform is currently being tested in a variety of global clinical studies in multiple patient populations, including TBI.7
Making smarter use of data
With the arrival of digital health innovations has come a deluge of data that can inform TBI clinical trials. For example, real-world data (RWD) collected from wearables and sensors, self- assessment apps or smartphones can be used to generate real-world evidence (RWE). Moreover, data from electronic health records (EHRs) may offer new opportunities to study the biology of TBI by offering information on pre-injury medical conditions, time-of-injury procedures, diagnoses and post-injury follow-up care.8
To make the most of this information and glean meaningful insights related to TBI, clinical researchers will need a strategy for collecting, harmonising and analysing data. In the 2019 ICON survey, 80 percent of respondents said their companies plan to use, or are using, artificial intelligence (AI) or big data approaches.5 Moreover, two-thirds of survey respondents said they will pilot or use these analytic technologies in selected programmes in the next 5 years. These survey results reflect the increased use of advanced analytical tools for creating actionable insights from mass amounts of data.
In the context of TBI studies, AI and machine learning (ML) can be applied to either identify behavioural patterns or the most beneficial treatment strategies. Also, by harnessing analytical tools to digest large datasets coming from a registry, researchers can collect information about a patient's treatment history and subsequent health outcomes following TBI. As an example, ICON has provided site management and source data verification (SDV) for a pan-European TBI observational study that aims to improve understanding of TBI to drive the development of more targeted therapies and overall better patient care.9 ICON’s contribution to the study has resulted in the verification of over 13,000 data points, which could benefit from AI and ML to analyse.9
Finally, advanced algorithms can be used to analyse TBI-related data from EHRs, eliminating the need for manual review, which can be time-consuming and costly.8
As the prevalence of TBI continues to be a significant burden on healthcare systems globally, there remains a growing need for innovations in TBI clinical research. Digital health technologies such as data from sensors and wearables have potential to streamline clinical trials and improve our assessment of prevention and rehabilitation strategies for TBI. To learn more about ICON’s expertise in this area, read our whitepaper, “Traumatic Brain Injury: From identifying biomarkers to improving clinical trial efficiency.”Read the whitepaper
- Adans-Dester, C., Hankov, N., O’Brien, A. et al. (2020). Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. npj Digit. Med. 3, 121. https://doi.org/10.1038/s41746-020-00328-w
- Centers for Disease Control and Prevention. (2019). Surveillance Report of Traumatic Brain Injury-related Emergency Department Visits, Hospitalizations, and Deaths—United States, 2014. Centers for Disease Control and Prevention, U.S. Department of Health and Human Services. https://www. cdc.gov/traumaticbraininjury/get_the_facts.html
- Brouillette, R. M., Foil, H., Fontenot, S., Correro, A., Allen, R., Martin, C. K., Bruce-Keller, A. J., & Keller, J. N. (2013). Feasibility, reliability, and validity of a smartphone based application for the assessment of cognitive function in the elderly. PloS one, 8(6), e65925. https://doi.org/10.1371/journal.pone.0065925
- Apple Inc. AppleResearchKit. Accessed 7 November, 2020. https://www.apple.com/uk/researchkit/
- Digital disruption in biopharma: How digital transformation can reverse declining ROI in R&D. (2019). ICON plc. https://www.iconplc.com/insights/digital- disruption/digital-disruption-in-biopharma/
- Wicklund, E. (2019). Abbott, Military To Test mHealth Platform for Concussion Diagnosis. mHealth Intelligence. https://mhealthintelligence.com/news/abbott-military-to-test-mhealth-platform-for-concussion-diagnosis
- Byrom, B.(2015). Clinical trials Re-spec: The role of games and gamification in clinical trials. International Conference on Interactive Technologies and Games (iTAG) IEEE Xplore. http://ieeexplore.ieee.org/document/7399486
- Dennis, J. et al. (2019). Diagnostic algorithms to study post-concussion syndrome using electronic health records: validating a method to capture an important patient population. Journal of Neurotrauma. http://doi.org/10.1089/neu.2018.5916
- ICON Contributes to Major Study on Treatment for Traumatic Brain Injury. ICON, plc. https://www.iconplc.com/news-events/press-releases/traumatic-brain-injury-study/