How to Run a BYOD Study (Bring Your Own Device)

Why BYOD Studies Are the Future of Wearable-Enabled Research

In clinical and consumer health research, the ability to collect real-world data through wearables has opened new possibilities for understanding behavior, physiology, and product impact. One of the most cost-effective, scalable, and participant-friendly approaches to wearable data collection is the BYOD study - “Bring-Your-Own Devices.” [1]

Rather than sending each participant a new wearable device, researchers can now allow individuals to participate using the devices they already own and wear daily. But while BYOD studies reduce costs and friction, they introduce new challenges in study design, data quality, and population diversity.

At Alethios, our platform supports both device provisioning and BYOD study designs – giving you the flexibility to run studies your way. Below, we break down how to run a successful BYOD study, and what to consider before launching.

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1. Choose Your Device Strategy: Any Device vs. Single Ecosystem

The first and most foundational decision you’ll make is what types of devices to allow in your BYOD study:

• Open Device Approach: Accept data from a wide range of wearables – e.g. Fitbit, Apple Watch, Garmin, WHOOP, Oura, Samsung, etc.
  
  
• Closed Ecosystem Approach: Accept only one type of device or brand ecosystem – e.g. Fitbit-only, or Apple Watch-only.
  
  

Pros and Cons

If your research depends on a very specific endpoint (e.g., HRV during sleep), or you need consistent sampling rates across participants, a closed approach may make sense. But if generalizability, learning or real-world diversity is important, an open device approach will yield a broader sample.

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2. Consider Device Popularity, Pricing, and Bias

Not all wearables are equally adopted across populations. Devices like Fitbit and Apple Watch are more common in the general population, while WHOOP and Oura tend to attract wellness-committed and higher-income users.

When deciding what devices to support:

• Check market distribution – What percentage of your target demographic owns each device type?
  
  
• Evaluate pricing models – WHOOP and Oura require subscriptions, which may affect participation.
  
  
• Anticipate selection bias – BYOD can unintentionally skew your sample toward people who can afford or prefer high-end devices.
  
  

Pro Tip: Consider layering in a short demographic screener during recruitment to assess how device ownership overlaps with your ideal study population.

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3. Factor in Form Factor and Daily Wear Habits

Different devices have different wearability patterns:

• Wrist-based wearables (e.g. Fitbit, Apple Watch): Comfortable for daily use but often removed at night.
  
  
• Rings or patches (e.g. Oura Ring, BioButton): May have higher overnight adherence, useful for sleep-based endpoints.
  
  
• Chest straps or clips: Often removed between uses, less ideal for continuous passive monitoring.
  
  

Form factor influences both data continuity and participant comfort. If your study hinges on sleep data, for instance, make sure the devices you’re accepting are commonly worn overnight.

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4. Ensure Wearable Adherence and Visibility

Adherence is one of the most overlooked challenges in wearable studies — especially BYOD. Just because someone owns a device doesn’t mean they’ll wear it consistently or sync it regularly.

At Alethios, our platform includes a wearable adherence tracker, visible to both researchers and participants. You can:

• Set minimum adherence thresholds (e.g., 12+ hours/day of wear)
  
  
• Flag and follow up with low-adherence participants
  
  
• Let participants track their own adherence in real time, improving engagement and compliance
  
  

This built-in visibility helps ensure data volume and quality – without chasing participants or cleaning unusable datasets post-study.

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5. Handle Data Standardization and Integration

One of the trickiest parts of a BYOD study is harmonizing data from multiple device types. Each wearable brand collects slightly different endpoints, uses different sampling rates, and has proprietary algorithms for metrics like sleep stages or HRV.

How Alethios Helps

Alethios integrates with leading wearable APIs (like Apple Health, Google Fit, Fitbit, Oura, WHOOP, Garmin, and more), and our platform handles:

• Device-specific endpoint mapping
  
  
• Timestamp harmonization
  
  
• Missing data flagging
  
  
• Export-ready datasets for your team or your statistician

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This means you can focus on insights — not wrangling inconsistent files or rebuilding your pipeline for each study.

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6. Decide When BYOD Makes Sense vs. Device Provisioning

BYOD isn’t always the best choice. Sometimes, providing participants with a standardized wearable is worth the extra cost. Here’s a rough guide:

Final Thoughts: BYOD is Powerful If You Design It Thoughtfully

Running a BYOD study can unlock scale, diversity, and speed — but only if you plan for the nuances of device variability, adherence, and participant experience. With Alethios, you can run BYOD, provisioned, or hybrid studies with end-to-end support — from study design to data collection and analysis.

Whether you’re validating a new wellness product or exploring real-world biomarkers, our platform makes wearable-powered studies simple, compliant, and actionable.

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Interested in running a BYOD study?

Get in touch with our team to see how Alethios can help you design and launch your next wearable-enabled trial.