Designing Better Sleep Studies: Practical Insights from Ambulatory Research

Sleep research has traditionally relied on small samples in controlled laboratory environments. While polysomnography (PSG) remains the clinical gold standard, it comes with high costs, technical barriers, and limited ecological validity. Ambulatory approaches such as HRV-based sleep monitoring with wearables open up new possibilities: larger cohorts, real-world settings, and scalable data collection at lower costs.

In this article, we explore practical considerations for researchers who want to design better sleep studies by using ambulatory methods.

Planning and Study Design

When implementing ambulatory sleep monitoring, study design is crucial. Researchers can now extend their sample size to hundreds or even thousands of individuals, instead of just recruiting a handful of participants (normally around 20) for a few nights in the lab. This makes longitudinal and population-based research feasible while also increasing the statistical power of findings.

Careful planning is still needed: How many nights should be recorded per participant? How will adherence be encouraged? And how should data quality be monitored in decentralized settings? Addressing these questions at the outset ensures robust and reliable outcomes.

Participant Compliance

Compliance is one of the major challenges in sleep studies outside the lab. To maximize adherence, simplicity is key: devices must be comfortable to wear, the app interface should be user-friendly, and participants should receive clear instructions and continuous feedback.

Ambulatory monitoring can also be less intimidating for sensitive populations such as the elderly, patients with insomnia, or children, who may struggle in artificial lab environments. By allowing participants to sleep in their beds, data is not only easier to collect but also more ecologically valid. Worth mentioning here is that only 30-40% of the adults in the western world sleep alone in the bed regularly. Yet practically any sleep study internationally is performed alone (and in addition with electrodes attached to the brain, around the eyes, the chin and usually the breast and/or legs).

Data Management and Access

Collecting data from hundreds of participants quickly generates large datasets. Researchers must consider data storage, preprocessing, and analysis pipelines early in the study. Access to raw data, such as inter-beat intervals (IBIs) or 30-second epoch-by-epoch sleep data, provides flexibility to conduct and apply custom analyses.

Exportable, structured datasets enable seamless integration with existing research workflows and statistical tools. This flexibility helps bridge the gap between standardized sleep metrics (e.g., sleep efficiency, wake after sleep onset) and exploratory research questions.

Cost Efficiency and Scalability

One of the greatest advantages of ambulatory research is scalability. By reducing the need for laboratory infrastructure and highly specialized hardware, studies can be conducted at a fraction of the traditional cost. This makes large cohort studies financially viable and opens new opportunities for collaborations across institutions and countries.

As costs decrease and participant numbers increase, sleep research can finally move closer to the scale seen in other areas of health science, such as epidemiology or cardiology.

Conclusion

Ambulatory sleep monitoring is not just a technological innovation—it is a methodological shift. It empowers researchers to design larger, more inclusive, and more ecologically valid studies. By focusing on study design, participant compliance, data management, and scalability, researchers can fully unlock the potential of ambulatory approaches.

The future of sleep science lies not only in the lab but also in the everyday lives of participants—and ambulatory monitoring provides the tools to make this future a reality.