How to Add Body Composition Metrics to a Health Research Study

Body composition metrics provide a direct window into physiological change. Unlike scale weight alone, body composition captures how mass is distributed across fat, lean tissue, and water, offering a more nuanced understanding of metabolic health, functional change, and intervention effects over time.

In decentralized and real-world research, smart scales and connected body composition devices make it possible to collect these metrics longitudinally, in participants’ everyday environments, with minimal burden and high ecological validity.

This post explains why body composition metrics are commonly used in health research, when they are appropriate, how they can be configured in Alethios, and what researchers receive once a study is live.

Why Include Body Composition Metrics in a Health Research Study?

Body composition is closely linked to cardiometabolic risk, physical function, aging, and treatment response across a wide range of conditions. Changes in fat mass, lean mass, and distribution often provide more meaningful insight than changes in total body weight alone, particularly in interventions where weight may remain stable while composition shifts.

In nutrition, metabolic health, GLP-1–related studies, and exercise interventions, body composition metrics are frequently used to distinguish between fat loss and lean mass preservation. In aging and longevity research, they are used to monitor sarcopenia risk, functional decline, and resilience. In recovery and rehabilitation contexts, they help quantify changes in muscle mass that may not be visible on the scale.

When collected longitudinally, body composition data can reveal trends that precede clinically meaningful outcomes, especially when paired with activity, sleep, or dietary data (Heymsfield et al., 2014; Kyle et al., 2004).

When Body Composition Metrics Are a Good Fit

Body composition metrics are commonly included in studies focused on:

• Metabolic health and insulin sensitivity
  
  
• Weight management and body recomposition
  
  
• GLP-1 and appetite-modulating interventions
  
  
• Physical activity and exercise programs
  
  
• Aging, frailty, and sarcopenia risk
  
  
• Nutrition and supplementation studies
  
  

They are less appropriate when short intervention windows are unlikely to produce measurable physiological change, or when participants cannot reliably access a compatible device.

Supported Devices and Sensors

Alethios supports body composition data from the following connected device ecosystems:

• InBody
  
  
• Garmin
  
  
• Withings
  
  

Body composition metrics are standardized for use within Alethios. Participants connect their devices during onboarding, after which data is collected passively according to the study schedule.

In addition to consumer smart scales, Alethios can support integration with more granular or research-grade body composition devices when required. This enables studies that demand higher precision or alternative measurement methodologies to use the same study infrastructure and participant experience.

How to Add Body Composition Metrics to a Study on Alethios

Configuration takes place during the Build phase of the Study Planner.

Step 1: Navigate to Build → Add Digital Endpoint

From the Build section of your study, select Add Digital Endpoint to configure data collection from connected devices.

Step 2: Select Body Composition Metrics

Under Devices & Sensors, select Body Weight Composition. Available metrics may include:

• Body weight
  
  
• Body mass index (BMI)
  
  
• Body fat percentage
  
  
• Lean mass or muscle mass
  
  
• Additional composition metrics, depending on provider
  
  

If enabled for your account, you may select multiple digital endpoints during this step, allowing body composition data to be collected alongside activity, heart rate, or sleep metrics.

Step 3: Configure Participant-Facing Copy

You will be prompted to define participant-facing fields that explain what data is collected and why.

Title
Connect your device

Description (optional)
Connect your device to enable researchers to collect additional health data.

Justification
This data will help our research team better understand the impact of our product on your health. This data will be used for internal analysis only.

Clear justification materially improves connection rates. Participants are more likely to connect a device when they understand both the purpose of the data and how it will be used.

Step 4: Choose Providers

Select which device providers you wish to support. Participants will only see connection options for the providers you enable.

Step 5: Set Requirement Rules

If the endpoint is marked Required, participants must connect a compatible device in order to become active in the study. If left optional, body composition data will enrich the dataset without gating participation.

What Body Composition Data Do Researchers Receive?

Alethios returns structured, time-stamped body composition data aligned to the study schedule. Depending on the device provider, data may include:

• Enrollment ID
  
  
• Study arm
  
  
• Device family
  
  
• Measurement timestamp
  
  
• Body weight
  
  
• Body mass index
  
  
• Body fat percentage
  
  
• Lean or muscle mass metrics
  
  
• Adherence indicators based on data availability
  
  

Data is normalized for analysis and can be exported for downstream statistical workflows or combined with other digital and self-reported endpoints.

Design Considerations for Body Composition Studies

Several design choices can materially improve interpretability:

First, consistency matters. Whenever possible, participants should use the same device for all measurements. Body composition algorithms differ across manufacturers, and switching devices mid-study introduces unnecessary variability.

Second, timing should be standardized. Body composition measurements are sensitive to hydration status, recent activity, and food intake. Providing clear instructions—such as measuring at the same time of day and under similar conditions—reduces noise and improves longitudinal comparability.

Third, body composition metrics are most powerful when analyzed alongside complementary endpoints. Pairing them with activity, dietary intake, or metabolic biomarkers provides context that helps distinguish behavioral effects from physiological adaptation.

Common Uses of Body Composition Data

Researchers frequently use wearable-derived body composition data to:

• Assess changes in fat mass versus lean mass
  
  
• Evaluate metabolic response independent of scale weight
  
  
• Identify responder subgroups
  
  
• Monitor unintended lean mass loss
  
  
• Contextualize changes in physical activity or energy intake
  
  

Best Practices

• Be explicit about which composition metrics are primary versus exploratory.
  
  
• Provide clear measurement instructions to participants.
  
  
• Avoid mixing device families within the same analytic cohort when possible.
  
  
• Pair body composition data with behavioral or physiological endpoints for interpretability.
  
  
• Preview the participant onboarding flow to confirm clarity of device connection steps.
  
  

Next in This Series

This post is part of a broader Alethios series on wearable- and device-derived digital endpoints, including step count, sleep, heart rate, heart rate variability, and recovery metrics.

References

Heymsfield SB, et al. Body composition measurement: advances and limitations. The American Journal of Clinical Nutrition. 2014.

Kyle UG, et al. Bioelectrical impedance analysis—part I: review of principles and methods. Clinical Nutrition. 2004.