The Way You Walk

What Your Gait Says About Your Health

We don’t often think about how we walk. It’s automatic, familiar, and uniquely our own. Yet growing research suggests that gait – the pattern of how we move when we walk – can reveal a surprising amount about our health, our well-being, and even our “walking age”.

Researchers at Imperial College London are exploring how walking speed, balance, and movement patterns can act as a window into the body. Their work adds to a growing body of evidence that gait is not just about getting from one place to another — it is a powerful indicator of how our bodies and brains are functioning.

Every gait tells a story

Each person’s walking style is highly individual. Just like a fingerprint, it reflects a combination of anatomy, habits, strength, balance and neurological control.

People recognise us from a distance simply by the way we move. Some of us walk with a heavy, grounded step, while others appear lighter and more spring-like. Even a silhouette can be enough for someone to identify us.

These differences are not random. They reflect underlying features such as:

·       muscle strength

·       joint flexibility

·       coordination

·       balance

·       neurological control

Together, these create a “movement signature” unique to each individual (Banger et al., 2025).

Gait changes can signal early health issues

One of the most important aspects of gait research is its potential to detect early signs of illness.

Changes in walking pattern can occur long before more obvious symptoms appear. In conditions such as Parkinson’s disease or certain types of dementia, subtle differences in balance, stride length, or rhythm are often among the earliest indicators.

In some cases, friends or family may notice these changes up to two years before diagnosis (Del Din et al., 2016; Morris et al., 2019).

Common early warning signs include:

 ·       slower walking speed

·       shorter steps

·       reduced arm swing

·       instability or imbalance

These changes may seem small, but they can reflect changes in brain function or motor control.

Walking speed and biological age

One area of particular interest is the relationship between walking speed and overall health.

Studies have shown that walking speed is strongly linked to life expectancy and general physical condition. Faster walkers tend to have better cardiovascular health, stronger muscles, and lower risk of certain diseases (Studenski et al., 2011).

This has led to the idea of “biological age” - a measure of how well the body is functioning compared to typical age-related expectations.

Two people may both be 50 years old, but:

·       one may move like the average 40-year-old

·       another may show movement patterns more typical of someone older

Gait analysis offers a simple way to estimate this “walking age” and identify early signs of decline.

Predicting recovery and resilience

Gait is also a strong predictor of how people recover from injury or surgery.

Research in patients undergoing knee replacement surgery has shown that those who:

·       move well before surgery

·       have good balance and strength

·       perform strongly in walking tests tend to recover more successfully afterwards (Bade et al., 2010).

This highlights an important point: how we move today can shape our health outcomes in the future.

Technology is changing how we measure movement

Traditionally, gait analysis required specialised laboratories with reflective markers attached to the body. Today, new technology is making the process faster, simpler, and more accessible.

At Imperial College London, researchers, led by engineer Dr Matthew Banger, are using markerless motion capture systems powered by artificial intelligence. Participants simply walk in front of cameras while algorithms analyse their movement.

A short assessment, sometimes just two minutes, can provide detailed insights, including:

·       step cadence

·       movement symmetry

·       joint mobility

·       balance performance

More comprehensive sessions include tasks such as squats, lunges, and single-leg balance tests.

The aim is to build a clearer picture of how movement changes across the lifespan, using large and diverse datasets.

Could gait reveal mental state?

An emerging area of research is the link between gait and mental health.

There is some evidence that emotional state can influence how we walk. For example:

·       depression is associated with slower, more withdrawn movement

·       anxiety may affect rhythm and posture

However, this is a complex area that requires much more data before it can be reliably used in practice (Michalak et al., 2015).

Researchers first need to understand how gait naturally varies with age and physical health before linking it to emotional or psychological states.

Bringing gait analysis into everyday life

One of the long-term goals of this research is to make gait analysis part of routine healthcare.

As technology advances, it may soon be possible to assess movement using everyday devices such as smartphones. This could allow people to:

·       track their mobility over time

·       detect early signs of decline

·       take action before problems become more serious

Public interest in this idea is already strong. Events offering short walking assessments have attracted large numbers of participants, many curious to learn their “walking age”.

Beyond curiosity, the real value lies in early identification, especially for conditions that develop gradually and often go unnoticed in the early stages.

Why this matters

Gait is something we often take for granted. But it is closely connected to almost every system in the body.

By paying attention to how we move, we can:

·       gain insight into our overall health

·       detect early warning signs

·       make informed decisions about lifestyle and care

We cannot stop ageing, but we can understand how our bodies are changing, and respond in ways that support long-term well-being.

References

Bade, M. J., Kohrt, W. M., & Stevens-Lapsley, J. E. (2010). Outcomes before and after total knee arthroplasty compared to healthy adults. Journal of Orthopaedic & Sports Physical Therapy, 40(9), 559–567.

Banger, M., et al. (2025). Markerless motion capture and gait analysis in population health monitoring. Imperial College London (unpublished study summary).

Del Din, S., Godfrey, A., Mazzà, C., Lord, S., & Rochester, L. (2016). Free-living gait characteristics in ageing and Parkinson’s disease: impact for assessment and intervention. Journal of NeuroEngineering and Rehabilitation, 13(1), 46.

Michalak, J., Troje, N. F., & Fischer, J. (2015). Embodiment of sadness and depression—gait patterns associated with dysphoric mood. Psychosomatic Medicine, 77(5), 491–501.

Morris, R., Lord, S., Bunce, J., Burn, D., & Rochester, L. (2019). Gait and cognition: mapping the global and discrete relationships in ageing and neurodegenerative disease. Neuroscience & Biobehavioral Reviews, 64, 326–345.

Studenski, S., et al. (2011). Gait speed and survival in older adults. JAMA, 305(1), 50–58.