The Metabolic Energy Mile (MEM) Framework: A Systems-Based Approach to Measuring the Cost of Walking a Mile

Introduction

In 2015, I traversed a 50-mile stretch of a route that combined parts of the Sierra High Route, the John Muir Trail, and the Pacific Crest Trail. Skies were clear, temperatures cool, never a cloud in the sky. In spite of my heavy pack weight, elevation gain, terrain, and steepness of the route, I felt like I floated the whole way. A few years later, I returned to repeat it – same route, same terrain. But this time it rained. The rocky surfaces were slick, poor visibility slowed my pace, and temperatures were cold. But my pack was lighter, and I was in better shape. Leading up to this trek – extraordinarily long days of work, little sleep, and a stressful day of disruptive travel to California. This time, I was mentally and physically tired. I ran out of food. I finished it exhausted. The same route that seemed easy before just about broke me.

That disconnect kept rattling around in my head.

I knew that distance and elevation couldn’t explain the difference in effort. The physics didn’t change – this was the exact same route. But something else had, and I was determined to model it. That tension led to the birth of the Metabolic Energy Mile (MEM) model as a way of explaining why the same mile can feel radically different under changing conditions.

This paper introduces the MEM framework. It lays the philosophical groundwork – that energy cost is defined by the friction between intent and disruption. It presents a structured model for quantifying both the mechanical demands of movement and the inefficiencies that amplify those demands in the field. And it critiques the limitations of legacy models (heuristic, terrain-based, and physiological) that fail to capture the lived experience of exertion.

Future installments will extend this foundation into tools and tactics: how to quantify disruption, estimate total energy cost with field-ready proxies, and use MEM to inform gear choices, pacing strategies, route planning, training design, and performance debriefs.

MEM isn’t a replacement for estimating effort based on distance or elevation gain – it just provides the missing context they ignore.

And in wild environments where calories are finite, terrain is complex, and morale is fragile, that context (and the planning required to conquer it) can make the difference between finishing strong and flaming out.

Not All Miles Are Created Equal

We tend to treat a mile as a static metric. It’s a known unit – “5,280 feet of distance” – and we often assume that covering one mile requires a predictable, repeatable amount of exertion. But every hiker, runner, or soldier is acutely aware of the reality: not all miles are created equal.

Sometimes one mile feels effortless, smooth, and even meditative. Other times, the same distance on another trail feels grueling, slow, and demoralizing. And we all know that hiking a route one day can feel very different than walking the exact same route on another day.

What explains this variability?

This is the foundational question that the Metabolic Energy Mile (“MEM”) framework is designed to explore. MEM is not a wearable device or a new caloric estimation formula. It is a conceptual and mathematical model that reframes how we understand energy expenditure in human locomotion, especially in natural environments. MEM integrates the topographical, biomechanical, environmental, physiological, and psychological factors that explain why each mile feels and costs differently.

MEM isn’t just a new variable to plug into your spreadsheet. It’s a lens, a way of seeing effort as the output of interaction between systems, and not just a linear function of time or distance.

A Review of Existing Models

Traditional models of energy expenditure – whether heuristic, empirical, or predictive – have provided useful scaffolding for estimating effort and predicting performance. But they share a critical limitation: they fail to capture the lived experience of effort in the field. The Metabolic Energy Mile (MEM) framework was created to fill this gap. It doesn’t just calculate or forecast – it interprets the felt sense of exertion, contextualizes it in real-world scenarios, and reveals what factors contribute to effort variability across time, terrain, and physical state.

MEM empowers us to:

• Explain why one mile feels harder than another, even under seemingly identical objective conditions.
• Predict effort variability under shifting terrain, increasing fatigue, and dynamic external factors like weather or psychological state.
• Guide real-time decisions in route planning and pacing when standard models offer insufficient granularity.
• Design gear and training strategies that manage not just biomechanical output, but also perceptual strain and disruption-induced inefficiencies.

MEM isn’t meant to replace conventional metrics such as speed, distance, or VO₂-based energy estimates. Instead, it answers a different question – one that’s more relevant in dynamic backcountry environments:

Why did this mile feel harder than the last one, how can I predict that effort in the future, and what is within my control to reduce that effort?

To understand what MEM contributes, it helps to examine where current models fall short. Most fall into one of three buckets: heuristic models, terrain-speed models, and physiological models.

Heuristic Models

Before we had GPS apps, digital elevation profiles, or wearable sensors, hikers and mountaineers still needed a way to predict how long it would take to get from point A to point B. Enter the heuristic model – a rule-of-thumb approach rooted in simplicity, practicality, and broad generalization.

Some of the most well-known examples include Naismith’s Rule, which dates back to 1892 and provides a baseline estimate of travel time based on distance and elevation gain. Then came refinements like Petzoldt’s Energy Mile and Langmuir Adjustments, which attempted to incorporate additional terrain factors. These models were never meant to be precise: they were meant to be good enough for back-of-the-napkin estimates, especially in the era of paper maps and analog compasses. They gave backcountry travelers a quick mental model to assess route difficulty and make planning decisions without needing a calculator.