eCOA and Digital Health Technology
In the mid-2010s, a Phase III trial’s data footprint was measured in the hundreds of thousands of data points; case report forms (CRFs), lab values, and site-based assessments collected a handful of times per patient over the course of a study. Tufts estimated that as of 2012, the average Phase III trial generated close to one million data points in total.1 Today, a single wearable sensor will exceed that data volume on its own.
This uptick in data volumes can be linked to digital health technology (sensors and wearables) as well as the bevy of core and non-core data collected through patient-reported outcomes (diaries and questionnaires). Spirometers, ECG patches, continuous glucose monitors, actigraphy trackers, and consumer-grade devices like Fitbit are no longer peripheral add-ons to a protocol. eCOA and digital health technology (DHT) data are rapidly becoming a dominant source of clinical trial data and increasingly support key efficacy and patient-centered endpoints.2
Secondary to this increased proportion of study data is the operational expansion involving how global studies are designed and deployed. Multinational trials routinely require multilingual eCOA implementations that can support consistent endpoint collection across countries, sites, and patient populations, making translation, linguistic validation, localization, and harmonization core study-startup requirements rather than downstream administrative tasks.3 At the same time, eCOA platforms are increasingly being integrated with wearable sensors and other DHTs so sponsors can capture more frequent, objective measures of patient health, activity, mobility, sleep, and symptom burden.4 Together, these trends reinforce the same point: eCOA and DHT are no longer optional digital overlays, but operationally critical components of modern clinical development that must be:
- Planned globally to support consistent implementation across regions, regulatory jurisdictions, languages, and patient populations.
- Validated rigorously to ensure data accuracy, device reliability, patient usability, and regulatory acceptability.
- Managed centrally as a core component of trial infrastructure, governance, data strategy, and operational oversight.
Data Reality: From Add-On to Backbone
The shift is structural, not incremental. A recent analysis of clinicaltrials.gov registrations identified more than a thousand interventional trials incorporating wearable-derived data over the past two decades, with adhesive patch sensors, driven largely by continuous glucose monitoring, now the leading device category, alongside growing use in sleep, cardiovascular, motor function, and neurological signal monitoring.5 Industry forecasts from a few years ago already projected that roughly half of all clinical trials would incorporate wearables and sensors by 2025, up from 10–15% adoption at the time.6 Layer on top of this an ecosystem of more than 318,000 health apps and over 340 consumer wearable devices actively capturing health data,7 and it becomes clear that eCOA and DHT are no longer a niche data stream sitting alongside EDC. In many studies, they are the largest single contributor to the total data volume the trial will produce.
That volume is exactly why the FDA finalized its guidance on Digital Health Technologies for Remote Data Acquisition in Clinical Investigations. The agency’s framing is instructive: DHTs “provide opportunities to record data from trial participants (e.g., biomarkers, performance of activities of daily living, sleep, vital signs) wherever the participants may be,” and, “compared to intermittent trial visits,” their use “may allow for more frequent or continuous data collection” rather than the sparse, periodic snapshots traditional site visits allow.8
Why Direct Data Capture Changes the Safety and Efficacy Equation
This is the piece that distinguishes eCOA and DHT data from almost everything else in a clinical trial: it is direct data capture. There is no site coordinator transcribing a patient’s report, no delay between an event occurring and it being recorded, and no reliance on a patient’s memory of how they felt three weeks ago at their last visit. A spirometer captures lung function as it varies day to day. An ECG patch captures cardiac signals continuously rather than during a single in-clinic snapshot. An actigraphy device captures activity and sleep patterns as they happen, not as they’re recalled.
That directness is what unlocks near real-time analytics into both safety and efficacy. Adverse events that might otherwise surface only at the next scheduled visit, or not at all, if not captured in a patient diary, can be flagged as physiological signals shift. Efficacy trends that used to require waiting for the next data lock can instead be monitored as they emerge, giving medical monitors and biostatisticians a current view of how a drug is performing in the field rather than a retrospective one. For risk-based quality management and central monitoring functions in particular, this continuous stream is a meaningful upgrade over the sparse, visit-based data most programs were built around. It’s also why regulators have leaned in: the FDA’s guidance explicitly recognizes that “novel endpoints based on data captured by DHTs may provide opportunities for additional insight into participant function or performance that was previously not easily measurable (e.g., tremors).”8
When eCOA and DHT Become the Rate-Limiting Factor
The catch is that all this value is conditional on the technology going live, on time, at scale, across every site and patient in the study. And unlike EDC, which most organizations have run for two decades, eCOA and DHT deployment still trips up programs that treat it as a checklist rather than a discipline. Device provisioning and logistics, questionnaire and endpoint configuration, translation and localization across sites, connectivity and data transmission reliability, patient training and compliance, and the harmonization of sensor data with EDC and biostatistics pipelines are all workstreams that have to be sequenced correctly and resourced early, not addressed after protocol finalization.
The regulatory dimension adds another layer of risk. Formal qualification of wearable-derived endpoints remains rare; to date, stride velocity 95th centile (SV95C) in Duchenne muscular dystrophy is the only wearable-derived measure to have achieved formal regulatory qualification as a primary efficacy endpoint.5, 9 That gap means most DHT strategies still require careful analytical and clinical validation planning worked out with regulators well before first patient in, not worked out reactively when a query comes back from an agency reviewer.
When any of these elements slip – a device vendor’s provisioning timeline, a site’s connectivity readiness, a translation/licensing delay – the downstream effect isn’t confined to the digital data stream. It cascades into database lock, interim analyses, DSMB reviews, and ultimately submission timelines. The very technology that was supposed to accelerate the trial becomes the reason it’s late.
What Effective Project Management Looks Like
The organizations getting this right treat eCOA and DHT scoping as a cornerstone of study start-up. That means assembling all key stakeholders during protocol design, so that questionnaire complexity, visit schedules, device selection, and data integration requirements are incorporated into the key study milestones as well any study feasibility and risk planning. It means building in the operational expertise to anticipate how a given device or configuration choice will ripple into training burden, site compliance, and budget, rather than discovering it mid-study. And at the program level, it means standardizing device selection and configuration approaches across studies where possible, so every trial isn’t relearning the same integration and validation lessons from scratch.
Digital health technology has already earned its place as one of the most valuable data sources in modern clinical research, not despite its complexity, but because of what direct, continuous, patient-generated data makes possible for safety and efficacy monitoring. Whether that value shows up as a competitive advantage, or a timeline liability comes down, almost entirely to how disciplined project management is behind it. Technology isn’t the rate-limiting factor. The planning around it is.
About Astrix
Astrix is the global leader in delivering innovative strategies and solutions to the life sciences industry. Powered by world-class people, proven processes, and advanced technology, Astrix partners with clients to drive measurable improvements in business performance, scientific advancement, and clinical outcomes—ultimately driving towards a goal of improving quality of life. Founded by scientists to address industry’s most complex challenges, Astrix provides a growing portfolio of strategic and technical services that deliver immediate impact while enabling long-term digital transformation. Our deep expertise spans strategic planning, data strategy, AI/ML readiness and technologies, lab informatics, and modern clinical operations and eClinical platforms so we can successfully deliver solutions that have high impact and drive better outcomes for everyone.
Turning Digital Innovation into Operational Success
Whether you’re implementing eCOA for a global study, integrating wearable technologies, or establishing a scalable digital data strategy, Astrix can help you navigate the complexities of modern clinical development.
Contact Astrix our experts to discuss your clinical technology and project management needs.
References
- Society for Clinical Data Management. (2022). The 5Vs of clinical data (Version 1). https://scdm.org/wp-content/uploads/2024/03/SCDM-The-5Vs-of-Clinical-Data-FINAL.pdf
- Mahadik, S., Sen, P., & Shah, E. J. (2025). Harnessing digital health technologies and real-world evidence to enhance clinical research and patient outcomes. Digital Health, 11, Article 20552076251362097. https://doi.org/10.1177/20552076251362097
- Sesen. (n.d.). Best practices for multilingual eCOA and ePRO translation in global clinical trials. https://www.sesen.com/resources/blog-insights/multilingual-ecoa-epro-translation-best-practices/
- Critical Path Institute. (2023). eCOA: COA Program projects and collaborations updates. https://media.c-path.org/wp-content/uploads/20240427171353/2023_Session6_eCOA.pdf
- Fayad, Z. A., Hirten, R. P., Nadkarni, G. N., & Kim, Y. S. (2026). Wearable technologies in clinical trials for drug development: Trends and emerging opportunities. Nature Reviews Drug Discovery, 25(6), 448–468. https://doi.org/10.1038/s41573-026-01403-9
- Ilancheran, M. (2021, October 26). Use of wearable and sensor applications in clinical trials is booming. Clinical Leader. https://www.clinicalleader.com/doc/use-of-wearable-and-sensor-applications-in-clinical-trials-is-booming-0001
- IQVIA Institute for Human Data Science. (2017). The growing value of digital health: Evidence and impact on human health and the healthcare system. IQVIA. https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/the-growing-value-of-digital-health
- U.S. Food and Drug Administration. (2023). Digital health technologies for remote data acquisition in clinical investigations: Guidance for industry, investigators, and other stakeholders. U.S. Department of Health and Human Services. https://www.fda.gov/media/155022/download
- European Medicines Agency. (2023). Qualification opinion for stride velocity 95th centile as primary endpoint in studies in ambulatory Duchenne muscular dystrophy studies (EMADOC-1700519818-1127132). https://www.ema.europa.eu/en/documents/scientific-guideline/qualification-opinion-stride-velocity-95th-centile-primary-endpoint-studies-ambulatory-duchenne-muscular-dystrophy-studies_en.pdf
