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It’s no secret that today, studies rely more and more on real-time data, remote participation, and continuous measurement through wearables and other digital tools. This shift has opened the door to richer insights, larger and more diverse populations, and longer observation periods. But at the same time, it has raised a new question that sits at the center of modern research design: what actually makes a digital study infrastructure fit for purpose?
Researchers themselves become more involved in the technical parts of the study, which was before handled in the background. The new reality of research is that a weak setup brings on gaps, noise, and bias that no amount of analysis can fully correct later. A strong one, by contrast, quietly supports the entire study lifecycle, from recruitment and consent to data collection, monitoring, and analysis.
Moreover, researchers look for systems that handle multiple data sources, reduce participant burden, and provide visibility into data flow without constant manual intervention. They need flexibility without sacrificing precision, and speed without compromising trust. And we are here to determine if, in today's research world, it is too much to ask.
This article explores the key questions researchers ask when evaluating digital study infrastructure today, and what separates a convenient tool from a foundation that can support serious, scalable research.
We have summarized it all in one table for a quick look before deep-diving into each aspect below.
In research, data quality is defined less by visual polish and more by scientific reliability. “Research-grade” data must be trustworthy over time, transparent in how it is generated, and robust enough to support validation, replication, and peer review. This is especially critical in digital studies that rely on wearables and remote data collection, where device behavior, user adherence, and environmental factors introduce natural variability.
At a minimum, research-grade data must meet three core criteria:
Believe it or not, data gaps are inevitable in real-world studies, but they must be visible and traceable rather than silently filled or ignored. Equally important is consistency over time. Researchers need confidence that data collected today is comparable to data collected weeks or months later, even when devices, firmware, or user behavior change.
Another common misconception is that highly processed metrics are inherently better. In reality, raw or minimally processed signals often matter more than polished summaries, because they allow researchers to:
Overall, modern research infrastructure must acknowledge imperfection. Noise, missing values, and device variability are not flaws to be hidden, but realities to be managed transparently. The goal is not flawless data, but data that researchers can understand, trust, and critically evaluate, which is ultimately what makes digital studies scientifically credible.
Interoperability has become a defining requirement for modern digital research. Studies that rely on a single device or vendor are becoming less popular. Participants already use a wide variety of wearables, smartphones, and health apps, and forcing everyone into one ecosystem often reduces recruitment, increases dropouts, and limits real-world relevance.
From a research perspective, interoperability enables flexibility and scale. When studies support multiple devices and data sources, they better reflect real-life behavior and produce results that generalize beyond a narrow user group. It also protects research teams from being tied to the roadmap, pricing, or technical limitations of a single manufacturer.
Key reasons interoperability matters include:
Equally important is avoiding vendor lock-in. Research timelines often span years, while device lifecycles are much shorter. An interoperable infrastructure allows researchers to adapt to new sensors, upgrade technology, or expand study scope without redesigning the entire data pipeline.
Ethics committees and regulators play a central role in shaping how digital studies are designed and executed. Their focus is not on slowing innovation, but on ensuring that participant rights, safety, and data integrity are protected from the start.
At a baseline, digital studies must comply with established regulatory frameworks such as GDPR in the EU, HIPAA in the US, and local or institutional ethics requirements. Not brag but we are qualified for both! You can find more details on our API page! But to be fair, compliance alone is rarely enough. Review boards expect research teams to demonstrate that privacy and ethics are embedded into the study architecture, not added as an afterthought.
Key expectations typically include:
Basically, interoperability is a safeguard for scientific integrity, study longevity, and methodological independence, ensuring that research questions, not technology constraints, drive study design.
To keep it short and simple, flexibility is basically a practical requirement for modern research. Studies rarely stay exactly as planned once real-time data collection begins, and rigid systems can quickly turn small adjustments into major delays.
A modern study infrastructure should support protocol changes without forcing a restart. Research teams may need to refine inclusion criteria, adjust data collection windows, or add new endpoints based on early findings. If these changes require rebuilding pipelines or reconsenting participants from scratch, valuable time and data are lost.
Flexibility also matters when scaling beyond the pilot phase. What works for a small, single-country study often breaks when expanded to multiple sites or regions. Infrastructure should be able to handle:
Finally, digital studies must be able to adapt to new devices or sensors mid-study. Wearables evolve quickly, and participants may switch devices over time. A flexible system allows new data sources or endpoints to be added while maintaining longitudinal consistency and minimizing disruption.
In practice, flexibility means modular design, API-based integrations, and data models that can evolve without breaking existing analyses. Research infrastructure should support the reality of iterative science, not lock teams into assumptions made on day one.
Data standardization is one of the most decisive factors in whether a digital study produces usable results or becomes an extended data-cleaning exercise. Without consistent structure and definitions, even high-quality data can be difficult to analyze, compare, or reuse.
One key advantage of standardization is clean exports from day one. When data is harmonized at ingestion, research teams spend less time on post-hoc cleaning and fewer assumptions are introduced during analysis. This reduces error risk and shortens the path from data collection to insight.
Equally important is the clear separation between raw data, derived metrics, and insights. Raw signals should remain intact and traceable, allowing researchers to revisit assumptions, apply new methods, or reprocess data as analysis techniques evolve. Derived metrics and summaries can then be layered on top without obscuring the original observations.
Standardized data also makes studies more valuable beyond their initial scope. Reusable datasets support:
In digital research, the cost of poor standardization compounds over time. Consistent data models, clear metadata, and well-defined units enable reliable analysis, collaboration across teams, and confident interpretation of results. In short, standardization turns collected data into research-ready evidence rather than a one-off dataset tied to a single study.
The best digital research infrastructure is rarely noticed by participants, and that is precisely the point. When infrastructure works well, studies feel seamless: data is collected quietly in the background, participation is simple, and trust is maintained without constant reminders of consent forms, devices, or technical complexity. Good infrastructure removes friction instead of adding to it.
We noticed this pattern in each of our partner studies, which you can find on our success stories page. Therefore, we took care of creating a white-labeled app for research that would take care of all research questions.
Our Research Infrastructure is built to support scalable, compliant, and device-agnostic digital research by providing a solid data foundation rather than prescribing how studies should look. For teams planning their next digital study, exploring infrastructure early, before devices, endpoints, or dashboards, can make the difference between a study that merely runs and one that truly delivers lasting scientific value.
Book a demo with Thryve and bring the modern research infrastructure into your studies!
Paul Burggraf, co-founder and Chief Science Officer at Thryve, is the brain behind all health analytics at Thryve and drives our research partnerships with the German government and leading healthcare institutions. As an economical engineer turned strategy consultant, prior to Thryve, he built the foundational forecasting models for multi-billion investments of big utilities using complex system dynamics. Besides applying model analytics and analytical research to health sensors, he’s a guest lecturer at the Zurich University of Applied Sciences in the Life Science Master „Modelling of Complex Systems“
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