How to Add Calories Burned to a Health Research Study

Calories burned — also referred to as active energy expenditure — is a widely collected digital endpoint in health research. It reflects the energy an individual expends through physical activity above resting metabolic rate, as estimated by wearable devices. In decentralized and real-world studies, wearable-derived caloric expenditure provides a low-burden, continuous measure of behavioral and physiological change over time.

This post explains why calories burned is commonly included in health research studies, when it is most appropriate, how it is configured in Alethios, and what data researchers receive once a study is live.

Why Include Calories Burned in a Health Research Study?

Active energy expenditure is a well-established marker of physical activity behavior and metabolic function. It has been used extensively in studies of obesity, metabolic syndrome, cardiovascular health, and behavioral interventions, where understanding whether participants are moving more — and burning more energy — is central to the research question.

Beyond its use as a primary behavioral endpoint, calories burned functions as a meaningful contextual variable. Changes in energy expenditure can reflect shifts in activity patterns, motivation, intervention adherence, or physical recovery. When paired with other endpoints such as body composition, resting heart rate, or sleep, caloric expenditure helps build a more complete picture of participant physiology and behavior.

In real-world and decentralized designs, longitudinal tracking of caloric expenditure is typically more informative than single-point estimates. Trends and change-from-baseline analyses can reveal sustained behavioral adaptation — or its absence — in ways that episodic assessments cannot.

When Calories Burned Is a Good Fit

Calories burned is commonly included in studies focused on:

• Weight management and obesity
• Metabolic health and insulin sensitivity
• Physical fitness, activity, and rehabilitation
• Lifestyle, nutrition, and behavioral interventions
• Longevity and healthy aging
• Supplement or product efficacy where increased activity is a proposed mechanism

It is less appropriate as a standalone primary endpoint in populations where wearable-derived estimates carry high variance, such as individuals with atypical gait patterns or those using medications that alter heart rate, unless those factors are addressed analytically.

Supported Devices

Alethios supports calories burned data from the following personal wearable ecosystems:

• Apple Health
• Fitbit
• Garmin
• Google Fit
• Oura
• Whoop

Participants connect their devices during onboarding, after which data is collected passively according to the study schedule. It is worth noting that active energy expenditure algorithms vary meaningfully across device families and are not directly interchangeable. These methodological differences should be considered during study design, particularly in multi-device studies.

How to Add Calories Burned 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 wearable-derived physiological data collection.

Step 2: Select Digital Endpoints

Within the Digital Endpoints modal, select Calories Burned.

If enabled for your account, additional endpoints — such as step count, resting heart rate, heart rate variability, or sleep metrics — can be selected and collected concurrently from the same connected device, depending on provider support and study design.

Step 3: Configure Participant-Facing Copy

You will define participant-facing fields explaining what data is collected and why.

Title Calories Burned

Description (optional) Passive tracking of daily active energy expenditure to assess physical activity levels and their change over the course of the study.

Justification Calories burned provides an objective measure of daily physical activity. Tracking changes over time helps researchers understand how an intervention affects energy expenditure, behavioral engagement, and overall physical activity.

Clear justification improves wearable connection rates and participant understanding.

Step 4: Choose Providers

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

Step 5: Set Requirement Rules

If marked Required, participants must connect a wearable device to become active in the study. If left optional, calories burned data will enrich the dataset without gating participation.

What Calories Burned Data Do Researchers Receive?

Alethios returns structured, time-aligned active energy expenditure data organized by study day. Returned fields include:

• Enrollment ID
• Enrollment ID (short)
• Study arm
• Device family
• Current adherence
• Date
• Active calories burned

In analysis, this daily value reflects device-estimated energy expended through activity, distinct from total daily energy expenditure, which also includes basal metabolic rate. Researchers should document the distinction in their analysis plans.

How Researchers Use Calories Burned in Analysis

In practice, active energy expenditure is most useful when interpreted longitudinally and in context. Researchers commonly use it to:

• Track within-participant behavioral change across study phases
• Assess whether intervention conditions are associated with increased or decreased activity
• Contextualize changes in body composition, metabolic markers, or symptom burden
• Support subgroup or responder analyses stratified by baseline activity level
• Monitor intervention engagement and tolerability

The analytic emphasis is typically on change from baseline, weekly trends, or between-arm comparisons rather than absolute thresholds.

Design Considerations for Calories Burned

Several design choices can improve interpretability:

• Run-in periods help establish stable baseline estimates before the intervention begins.
• Seasonal or contextual controls are worth considering in studies where external factors — weather, work schedules — may independently influence activity.
• Complementary endpoints such as step count, body composition, or sleep efficiency help distinguish meaningful behavioral change from device noise or transient fluctuations.

Best Practices

• Treat calories burned as a behavioral or supportive physiological endpoint unless explicitly powered otherwise.
• Document device family and derivation method in analysis plans, as algorithms vary by provider.
• Avoid over-interpreting short-term day-to-day variability.
• Pair caloric expenditure with complementary endpoints to strengthen interpretation.
• Preview the participant onboarding flow to ensure clarity around wearable connection requirements.

References

Ainsworth BE, et al. Compendium of physical activities: an update of activity codes and MET intensities. Medicine & Science in Sports & Exercise. 2000;32(9 Suppl):S498–504.

Drenowatz C, et al. Differences in the association of sedentary behavior, physical activity, and fitness with obesity markers. Medicine & Science in Sports & Exercise. 2014;46(6):1193–1200.

Jakicic JM, et al. Association between wearable technology and physical activity behaviors. JAMA. 2016;316(11):1161–1171.

Freedson PS, et al. Calibration of the Computer Science and Instruments accelerometer. Medicine & Science in Sports & Exercise. 1998;30(5):777–781.

Bent B, et al. Investigating sources of inaccuracy in wearable optical heart rate sensors. npj Digital Medicine. 2020;3:18.

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