OpenMetabolics Turns a Smartphone Into a Powerful Health Tool

A basic smartphone carried in a pocket could soon serve as a highly capable health device. Researchers at Harvard University have created a system for calculating the energy someone expends during everyday activities, which could be considerably more accurate than many widely used fitness watches.

Scientists from the John A. Paulson School of Engineering and Applied Sciences (SEAS) developed the smartphone-based system, OpenMetabolics. It monitors leg movement to calculate the number of calories an individual burns.

The complete study outlines the system’s operation and why it could address an important health challenge.

Measuring physical activity

Physical inactivity is the fourth biggest cause of death globally. Moving regularly benefits muscle strength, cardiovascular health, mental health, sleep and even brain function.

Yet researchers still find it difficult to establish clear links between physical activity and numerous health outcomes, including weight loss and quality of life for people with certain diseases.

Health bodies, including the World Health Organization, have called for improved ways of measuring physical activity.

To gain a genuine understanding of health, scientists must establish how frequently a person moves, the duration of their activity and its intensity. Brief periods of walking throughout the day are important too, rather than only extended exercise sessions.

The problem with fitness trackers

A large number of smartwatches and fitness trackers calculate calories from heart rate and wrist movements. Their estimates can contain substantial inaccuracies, with some studies reporting errors of between 30 and 80 percent.

Laboratory approaches, including direct calorimetry and respirometry, assess energy expenditure with great precision. However, they depend on specialist equipment and are not practical for routine daily use.

Questionnaires also rely on people reporting their activity levels, but faulty memory and personal bias may result in inaccurate responses.

Smartphones could provide a more suitable alternative. Approximately 70 percent of people worldwide use smartphones, making them more accessible than smartwatches across many regions.

How OpenMetabolics works

OpenMetabolics tracks leg motion rather than wrist movement. When people walk, run, climb stairs or cycle, their leg muscles account for most of the body’s energy use.

Monitoring the leg’s movement therefore allows the system to estimate energy expenditure more directly. The smartphone relies on integrated sensors, including an accelerometer and gyroscope.

The system separates movement into gait cycles, meaning a complete step pattern. A machine-learning model known as gradient-boosted trees then calculates the energy used for every step.

For training, the model drew on data from 36 participants undertaking activities such as walking, running, stair climbing and cycling at varying intensities. It learnt the relationship between leg movements and actual energy expenditure recorded by laboratory equipment.

Forward and backward leg movement provided the most valuable data. Height and weight had a far smaller effect on the predictions. This indicates that leg motion gives a strong indication of the energy used by the body.

Testing OpenMetabolics for accuracy

The researchers assessed OpenMetabolics using new participants who had not contributed to its training data. In real-world walking, it produced a cumulative error of about 13 percent.

When all real-world activities were considered together, the error was about 18 percent. That level of performance was about twice as accurate as many commercial devices.

Participants walked outside on pavements, climbed stairs, ran and cycled. The study compared OpenMetabolics with a Fitbit smartwatch, a heart rate model, a pedometer and a thigh-based accelerometer. OpenMetabolics delivered the lowest overall error.

The findings further indicated that the system’s accuracy was not significantly influenced by age, gender or body mass index. This suggests the tool performs effectively for different groups of people.

Solving the pocket problem

Phone movement within a pocket presented a significant obstacle. Loose garments may make a phone shake in ways that fail to reflect the motion of the leg. The team addressed this by developing a pocket motion correction model.

This model lowered movement errors by about 28 percent. Once the correction had been applied, there was no meaningful difference between a phone secured firmly to the thigh and one carried normally in a pocket.

Users therefore do not require special straps or additional equipment.

Monitoring activity for a full week

The researchers also evaluated OpenMetabolics in a seven-day study. Participants kept a smartphone in their pocket during everyday life, while the system recorded energy expenditure for each step and revealed distinct day-to-day patterns.

For instance, activity commonly rose during commuting periods. The data also indicated that participants were less active on Sundays than on weekdays.

This level of detail could assist doctors, public health specialists, urban planners, nutritionists and researchers in developing more effective health programmes. It could also enable scientists to investigate how everyday routines influence long-term health.

OpenMetabolics, a tool for global health

OpenMetabolics is open source, so researchers can access both its data and code. This allows scientists worldwide to develop and enhance the system more easily.

As smartphones are widespread even in underserved areas, the tool could contribute to narrowing global health gaps.

By providing physical-activity information that is both more accurate and more accessible, OpenMetabolics could help address major health questions and support better decisions by individuals and communities.

A straightforward phone carried in a pocket could soon be among the most powerful health tools available.