Harvard University’s OpenMetabolics could turn your smartphone into a powerful health tool

A basic smartphone carried in your pocket could soon double as a serious health instrument. Researchers at Harvard University have created a new way to measure how much energy people use during everyday movement, and it could prove markedly more accurate than many widely used fitness watches.

The team at the John A. Paulson School of Engineering and Applied Sciences (SEAS) built a smartphone-based system called OpenMetabolics. It estimates how many calories someone burns by analysing how the leg moves.

The study sets out the method in detail and explains why it could help address a significant public-health challenge.

Measuring physical activity

Globally, physical inactivity ranks as the fourth leading cause of death. Moving regularly supports muscle strength, cardiovascular health, mental wellbeing, sleep quality, and even aspects of brain function.

Even so, researchers still find it difficult to draw clear links between physical activity and many outcomes, including weight loss and quality of life for people living with certain illnesses.

Health bodies such as the World Health Organization have called for improved ways to quantify physical activity.

To understand health properly, scientists need to capture how often someone moves, how long that movement continues, and how intense it is. It is also important to account for brief bouts of walking spread across the day, rather than focusing only on longer, planned exercise sessions.

The problem with fitness trackers

A large number of smartwatches and fitness trackers estimate calorie burn from heart rate data and motion at the wrist. In practice, these estimates can be highly unreliable, with some research reporting errors of 30 to 80 percent.

Highly accurate laboratory approaches-such as direct calorimetry and respirometry-can measure energy expenditure very precisely. However, they rely on specialist equipment and are not practical for use in normal day-to-day settings.

Questionnaires can also be used to assess activity, but they depend on self-reporting, where recall and personal bias often lead to inaccurate responses.

Smartphones may provide a more practical route. Roughly 70 percent of people worldwide use smartphones, making them easier to access than smartwatches in many regions.

How OpenMetabolics works

Instead of concentrating on wrist movement, OpenMetabolics targets leg motion. During activities such as walking, running, climbing stairs, and cycling, the legs account for most of the body’s energy use.

By monitoring the way the leg moves, the system can estimate energy expenditure more directly. It relies on a phone’s built-in sensors, including the gyroscope and accelerometer.

The software breaks movement down into gait cycles-one complete step pattern-and then applies a machine-learning approach known as gradient-boosted trees to estimate energy burned for each step.

To train the model, the researchers used data from 36 participants performing walking, running, stair climbing, and cycling at varying intensities. The system learned how leg motion relates to true energy expenditure measured using laboratory instruments.

The strongest predictive signals came from the leg’s forward-and-back movement. By contrast, height and weight contributed far less to the estimates, indicating that leg mechanics carry substantial information about how much energy the body is using.

Testing OpenMetabolics for accuracy

The team then evaluated OpenMetabolics using participants who were not included in the training dataset. In real-world walking trials, the cumulative error was around 13 percent.

When results from all real-world activities were combined, the overall error was about 18 percent-roughly twice as accurate as many commercial wearables.

Participants completed outdoor pavement walks, stair climbs, running, and cycling. The study also compared OpenMetabolics with a Fitbit smartwatch, a heart-rate-based model, a pedometer, and a thigh-mounted accelerometer. Across these comparisons, OpenMetabolics produced the lowest overall error.

The findings further indicated that the system’s accuracy was not meaningfully influenced by age, gender, or body mass index, suggesting it performs consistently across different groups of people.

Solving the pocket problem

One key obstacle was the way a phone can move around inside a pocket. With looser clothing, the handset may wobble in ways that do not mirror the leg’s true motion. To address this, the researchers developed a pocket motion correction model.

This correction reduced motion-related errors by about 28 percent. After applying the fix, there was no meaningful difference between measurements taken with a phone firmly attached to the thigh and those taken with a phone carried normally in a pocket.

As a result, users do not need straps or additional equipment.

Monitoring activity for a full week

OpenMetabolics was also assessed in a seven-day study in which participants carried a smartphone in their pocket as they went about daily life. The system logged energy use once per step and revealed clear patterns from day to day.

For instance, activity levels often rose around commuting times. The records also indicated that participants tended to be less active on Sundays than on weekdays.

Information at this level of detail could support clinicians, public-health specialists, urban planners, nutritionists, and researchers in creating more effective health interventions. It could also help scientists investigate how everyday routines influence long-term health.

OpenMetabolics, a tool for global health

OpenMetabolics is open source, meaning researchers can access both the code and the data. This enables teams worldwide to test, refine, and extend the system.

Because smartphones are widespread even in underserved areas, the approach could help narrow global health inequalities.

By offering more accurate and more accessible measurement of physical activity, OpenMetabolics could help tackle important health questions and support better decision-making for individuals and communities.

A simple phone carried in a pocket may soon become one of the most capable health tools available.