Wearables + Perception: A Dual Approach to Product Validation

In the validation of health products — whether it’s a supplement, a skincare formulation, or a functional food — researchers often face a false choice: do we lean into subjective perception, or do we rely solely on objective data? At Alethios, we believe the strongest approach is not either/or — but both. By combining the power of wearable or sensor-derived data with the nuance of human perception, we can build a richer, more actionable evidence base that’s meaningful to both regulators and consumers. This dual-pronged approach is not just scientifically sound; it’s what the future of real-world research demands.

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The Opportunity: Consumer Products, Clinical Proof

Across the wellness and consumer health space, claims like “improves sleep,” “boosts focus,” or “reduces stress” are everywhere. But how do we validate these claims in a way that stands up to scrutiny — without requiring a $500K randomized controlled trial? The answer lies in smart study design and modern tools — and taking advantage of the wealth of wearable technology available.

Today’s wearables can track everything from HRV and sleep staging to step count, skin temperature, blood oxygen, and more. When used appropriately, they offer a continuous, high-resolution stream of objective physiological data — often in a way that is less intrusive, more scalable, and more reflective of real-life behavior than lab tests or single-point biomarkers. In fact, some organizations estimate that up to ⅓ of Americans have used electronic sleep trackers in recent years, becoming a much bigger part of daily health-tracking.

But wearables alone can’t tell the whole story in research, which is where rich participant-reported outcomes and surveys come into play. For a consumer deciding whether a product “works,” their own experience is the most powerful form of evidence. Did they feel more rested? Less anxious? More energized? These perceptions drive purchase behavior, adherence, and brand trust. It’s not enough for a product to show signal on a graph. It has to feel like it works — and that too, is data.

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The Science: Wearables as Clinical Endpoints

The clinical utility of wearables is no longer hypothetical. Experts like Marco Altini (whose substack we love at Alethios!) have long advocated for using wearable-derived metrics not just for lifestyle insights, but as meaningful endpoints in structured research.

Drawing from his work in HRV analysis and wearable data modeling, Altini emphasizes the need to:

• Validate devices against gold standards (e.g. ECG, polysomnography)
  
  
• Standardize data pipelines to reduce noise and variance
  
  
• Contextualize measurements to avoid overinterpreting minor fluctuations
  
  
• And most importantly — to ask relevant, health-oriented questions with the data
  
  

Wearables, when grounded in solid methodology, can serve as:

• Primary endpoints: e.g. reduction in resting heart rate over 4 weeks of supplementation
  
  
• Secondary endpoints: e.g. improved REM sleep percentage alongside self-reported calmness
  
  
• Exploratory endpoints: e.g. identifying novel patterns in temperature, activity, or HRV during product use
  
  

As the Digital Medicine Society (DiMe) notes, wearables and digital endpoints are no longer fringe tools — they are foundational to the future of clinical research. In its guidelines and workstreams, DiMe encourages careful alignment between the data collected and the health outcomes being targeted, emphasizing transparency, reproducibility, and participant-centered design.

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The Layered Approach: Objective + Subjective

Here’s what it looks like when we combine these methods in a real-world study:

• Objective Layer (Wearable Data)
  
  
  
  • Metrics: HRV, sleep efficiency, activity level, skin temp
    
    
  • Frequency: Passive, continuous
    
    
  • Benefits: High-resolution, scalable, hard-to-fake
    
    
  • Role: Captures physiological response
    
    
• Subjective Layer (Participant Perception)
  
  
  
  • Metrics: Mood, energy, sleep quality, stress levels
    
    
  • Frequency: Daily check-ins or weekly surveys
    
    
  • Benefits: Personal, experiential, behaviorally predictive
    
    
  • Role: Captures felt experience
    
    

Together, these layers can form a complete picture of product impact: how it works in the body, and how it’s felt in real life.

Why This Matters for Participants

There’s another dimension to this approach that’s often overlooked: how it impacts the participant experience. When participants can track their own wearable data, they often feel more connected to the study. They’re not just providing data. They’re seeing it, interacting with it, and making sense of it in their daily lives. That sense of insight and ownership is powerful. It can drive higher engagement, better retention, and a feeling of partnership between the participant and the researcher.

Plus, for participants in wellness studies, wearable feedback often helps them discover changes they wouldn’t have otherwise noticed — small improvements in sleep timing, heart rate, or consistency that mirror how they feel day to day. At Alethios, we like to embed that feedback loop into our studies, by providing every individual participant with a copy of their data at the end of every single study that runs on our platform.

A Better Way to Build Claims

So how do we turn this dual approach into credible, publishable, and market-ready evidence?

At Alethios, we help companies:

• Design smart studies that layer objective and subjective data
  
  
• Select validated wearable devices based on the right metrics and use case
  
  
• Craft participant experiences that are smooth, cognizant of ethical in research, and engaging
  
  
• Analyze and interpret data through clinically meaningful endpoints
  
  
• Translate findings into claims that are robust, defensible and impactful
  
  

Whether you’re validating a new product, building a claims dossier, or just trying to understand how your formulation affects the people who use it, this layered approach is faster, more scalable, and more human than traditional methods.

Interested in running a wearable-based study with real-world impact?

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