Diet and fitness is never as simple as 'one size fits all'. Staying healthy requires persistence in order to find a method that works for your unique body.cottonbro, Pexels

The world of exercise is awash with questions such as these. Deceptively simple, many of them resist a firm and definitive answer. If you were to go to your local gym and conduct a survey as to whether, for example, stretching prevents injury, you are likely to receive a very mixed response. But what is the cause of the general uncertainty surrounding these questions, and what does science have to say about it?

Many studies have attempted to find categorical answers to these basic questions. The implicit premise is often that a particular approach to exercise will be universally effective or ineffective. Stretching either prevents injury or it doesn’t. Protein supplements either aid muscle growth or they don’t. But what if the picture isn’t quite so simple?

In 2015, the computational biologist Eran Segal set out to measure people’s blood sugar response to different foods. A sharp glucose ‘spike’ after eating a certain food is undesirable, as it stimulates the over-production of insulin. An excess of insulin, which signals the conversion of glucose into fat, can subsequently ‘tank’ an individual’s blood sugar, making them feel hungrier and therefore inducing them to eat more. Consistently high blood sugar levels also place additional stress on the body’s systems.

It is for these reasons that sharp glucose spikes are associated with diabetes, obesity, and other health issues. It is therefore critical to identify foods that induce these spikes so they can be avoided. This is traditionally done by referring to a food’s glycemic index (GI) – the higher the number, the faster the food is broken down and the higher the risk of a spike. A food’s GI value is usually calculated by measuring the average blood sugar response of a sample population after eating it. Experiments have shown that ‘healthy’ foods such as fruits have lower GI values compared to ‘unhealthy’ ones such as ice-cream.

But when Segal analysed his data, he came to a startling conclusion. Although the received wisdom regarding high-GI and low-GI foods held on average, it certainly did not apply in each individual case. The assumption, for example, that fruits are good for blood sugar and that ice-cream is bad for it was indeed true for many. However, a significant number of subjects experienced the precise opposite. One unfortunate woman reported significant blood sugar spikes after eating nectarines, while only experiencing a modest rise following the consumption of high-fat milk. Eating a ‘healthy’ diet high in fruits and vegetables was in fact the cause of her many health problems.

These results underline the importance of appreciating human diversity. It is fallacious to claim that the ‘average’ result of a study will necessarily apply to everyone, because hardly anyone is precisely ‘average’. Indeed, Segal suspects that his results were influenced by unique composition of each individual’s gut biome.

“Every so often, a fitness company gets busted for telling outright lies about the efficacy of their products.”

In recognising this, there is now a growing movement towards the notion of ‘personalised diets’ – nutritional advice based on the individual response of a particular client towards certain foods. Through machine learning and data analysis techniques, researchers can extrapolate trends from existing data regarding the relationship between different foods, their corresponding blood sugar responses, and certain personal attributes (e.g. age, weight, gender, stool sample analysis). A client’s own data can then be mapped onto these trends, allowing their response to different foods to be predicted. Although still in their early experimental stages, these innovations are greatly promising.

But does any of this apply for what we’ve been told about exercise?

No study as comprehensive as Segal’s has been done specifically on the topic of exercise or on any of the common myths that surround it. But it is quite possible that the fallacious thinking that idolised nectarines and demonised ice-cream has also infected the realms of exercise science. If our bodies are different enough such that a perfectly healthy diet for one person could be disastrous for another, then the same could well apply for exercise regimes. It is perfectly plausible that, for example, stretching before exercise prevents injury for athletes of a certain body type whilst having absolutely no effect for others. In realising this, we may be able to reconcile advice that would otherwise appear contradictory.

There is also a more cynical reason to doubt the universality of common exercise advice. Consumers like certainty. It is far easier to convince someone to purchase a particular supplement, training package, or piece of equipment if it is ‘scientifically proven’ to work. Saying “this may or may not work for you depending on various factors including your body type, age, gut biome, and metabolism” simply doesn’t have the same ring to it.

This is not mere speculation. Every so often, a fitness company gets busted for telling outright lies about the efficacy of their products. In 2012, Reebok agreed to pay $25 million in refunds to customers who had bought its EasyTone shoes on the (false) belief that the shoes strengthened key muscles by creating pockets of ‘micro-instability’. And in 2010, the manufacturers of ‘Power Balance’ bracelets (once sported by the likes of Kevin Pietersen and Kobe Bryant) were found to be in breach of Australian consumer law by claiming that the bracelets emitted a ‘holographic energy field’ that purportedly improved the wearer’s balance and strength. At the point where fitness companies occasionally resort to such fraud, it is trite to suggest that they will also take advantage of scientific ‘half-truths’ when and where they arise.


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So, what are we to do? The future brings the potential of personalised exercise plans driven by algorithms and machine learning, akin to the personalised diets currently being researched by Segal and his team. Mainstream use, however, seems a long way away, especially considering how much work there is still to do in the area of personalised diets itself. Successfully commercialising these processes in a dietary context may be necessary before they can be applied to questions of exercise and fitness with confidence.

Which leaves us with trial and error. Amidst all the obfuscation and puffery, we do tend to know when something isn’t working for us. Realising that exercise advice is rarely universal therefore gives us a licence to change things when they aren’t delivering results.

Perhaps more importantly, that realisation ejects us from the cycle of self-blame that can occur when we fail to live up to expectations. Instead of seeing failure as a moral defect on our part (‘if only I’d tried harder…’), we can appreciate fitness as a process that is tinged with luck. Some are fortunate enough to quickly settle into an effective regime, while others may require a period of experimentation. If you fall into the latter category, remember this – it isn’t you.