Saturday, 1st July 2023

Evidence Part 1

Over the past few months I have taken you through some of the important and, I hope, interesting facts about how exercise works to increase physical fitness – and also about how much we do and how much we need to do to get the most out of physical activity.

You can use the list of “Categories” opposite  to identify any subject which interests you. There you will find descriptions of the health benefits of exercise in the prevention and treatment of most of the degenerative diseases to which we are prone as the years march by.

In each case what I write is backed by scientific evidence. Full lists of the relevant scientific papers (nearly 700 in all) are given in my two books on the subject – “Get of the couch before it’s too late” and, as co-author,  “Exercise. A Scientific and Clinical Overview”.

In future blogs I intend to highlight new information about the effects of physical activity using scientific evidence. So today I thought that you might be interested to read about the ways in which such evidence is gathered.

Evidence: Interpreting the Science

The fact that an opinion has been widely held is no evidence whatever that it is not utterly absurd. Bertrand Russell

Evidence is different to proof and it is important to understand its quality and limitations.  A starting point is to consider evidence as applied to facts and beliefs in a medical or clinical setting. In the context of exercise and health a key principle is that of cause and effect.


When it comes to exercise as a factor in health and disease, an important source of evidence is found in epidemiology.

‘Epidemiology is the study and analysis of the patterns, causes, and effects of health and disease conditions in defined populations. It is the cornerstone of public health, and shapes policy decisions and evidence-based practice by identifying risk factors for disease and targets for preventive healthcare’.  In simple terms, epidemiology is the observation of associations between disease and certain behaviours, environmental conditions and physical states.

For example, when Professors Richard Doll and Tim Peto from Oxford University identified cigarettes as the main cause of lung cancer, they used epidemiological evidence – the observation that people who smoked cigarettes had a far higher incidence of lung cancer than those who did not.

A substantial amount of the evidence related to the effects of exercise are based on epidemiology. A specific example is the finding that people who take a large amount of exercise are less likely to develop type 2 diabetes.

Both these examples demonstrate only associations, not proof of cause. Association is not the same as causation – though it may be! The following is a light-hearted example. During the period in the 20th century when the incidence of coronary disease was increasing rapidly, so was the use of the radio. It might be inferred that radio waves cause coronary disease – a fairly absurd conclusion. Clearly, few people believe that coronary disease is caused by radio waves. In the case of smoking and lung cancer, however, there is no other reasonable explanation and there are good biological reasons to believe that smoking might cause lung disease.

In the case of exercise and diabetes, it is not possible to be quite so certain. There may be ‘confounders’. Confounders are other factors with which both exercise and diabetes are associated, such as sugar consumption. If exercisers consume less sugar than non-exercisers, that might be the explanation for the association. There are many differences between exercisers and non-exercisers and it would be impossible to find groups of each whose other characteristics were identical. This means that many of the associations described below are just that – associations with a very strong presumption of a causal link.

Clinical trials

Stronger evidence of causation is provided by the clinical trial. The purest form is the randomised, double-blind, controlled trial (RCT). This is best understood in the example of a drug trial. A group of individuals with a particular condition is selected to clarify the effect of the drug and they are divided randomly into two groups. One group receives the treatment and the other (the control group) receives a placebo. A placebo is simply  an inert pill that looks the same as the real treatment. Neither the person giving the treatment and assessing its effect, nor the patient receiving it, knows who is getting the real treatment and who is getting the placebo. At the end of a predefined period the code is broken and the difference between the two groups analysed. If a trial of a cancer treatment is being tested, this might be the cure rate, and if the cure rate is significantly higher in the treatment group than in the control group it can be concluded that the drug is effective.

There are lots of possible pitfalls even in this very straightforward scenario. The patients might not have complied properly with the treatment. This is most likely for the treatment group if the drug has unpleasant side effects. The randomisation process might not be perfect. Despite the randomisation, there may be unexpected differences between the two groups.

The statistical analysis has its own problems. In clinical trials, statistical significance is reached if the chance of the difference found between the two groups is less than 1 in 20 (in statistical shorthand, p = <0.05). With a clearly effective treatment this can be derived from a trial with small numbers of patients. The less obvious the effect, the more patients will be needed in the trial to demonstrate it. The more patients needed in the trial to show an effective outcome, the less effective the drug must be. If the number needed is very great, a statistically significant effect might be clinically insignificant. This means that although statistical significance is eventually reached, the clinical outcome is questionable. The counter side to this is that small trials are more likely to produce erroneous results. The bigger the trial, the more reliable the test will be.

Even apparently well-conducted RCTs can yield results with problems of interpretation. For instance when such trials are carried out by pharmaceutical firms on their own products, they are far more likely to have a positive outcome than independently funded and conducted trials.

Next time

Next time I will discuss how evidence can be interpreted – and misinterpreted.



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