ALGUNAS EXPERIENCIAS NACIONALES RECIENTES

Increasing adherence to exercise is the ultimate long-term goal of the work presented in this thesis, as well as many researchers across the globe. Although the end goal is shared, approaches vary dramatically. As part of the EM approach that has guided all investigation herein, understanding the mechanisms by which an intervention elicits

44 its effect on behaviours is of fundamental importance. Therefore, behavioural outcomes, as well as putative target engagement needs to be quantifiable.

Essentially, completing exercise, or indeed any other instrumental behaviour is the result of a decision where a particular action/behaviour is chosen in favour of competing alternatives. Therefore, what ultimately determines adherence is a systematic bias, resulting in individuals repeatedly choosing to engage in exercise in favour of competing alternatives. Although adherence can be measured directly, either by monitoring attendance at supervised exercise classes (e.g., Klonoff et al., 1994), or via a physical activity recall (e.g., Williams et al., 2008; Williams, Dunsinger, Jennings, & Marcus, 2012) this is a big undertaking, requiring relatively long time-frames and an extended commitment from participant to engage in the follow-up, or significant resourcing to run supervised exercise session for participants to attend. However, understanding this decision-making process mechanistically can be achieved in a relatively shorter time frame and with few participants than would a large scale RCT testing the effect of caffeine on adherence to exercise.

Traditionally choice as a behavioural outcome, related to exercise behaviours in humans has sought to quantify how reinforcing the behaviour is. This has been achieved using complex choice paradigms. An example of one of these paradigms was used in a study (Williams & Raynor, 2013) to determine preference for exercise. In this study participants were asked whether they would prefer to walk for one mile at a self-selected intensity or sort paper clips for 2 minutes. Subsequent questions required participants to choose between walking for one mile at self-selected intensity versus sorting for 4, 6, 8, 10, 12, 14, 16, 18, and 20 min. There were various iterations of this question exploring differences in preference for self-selected- vs imposed-intensity physical activity, but that is not the subject of this discussion. The fact is that the established way to quantify

45 preference for exercise is to equate to sorting paperclips. This is useful when comparing two conditions directly, for example, testing path X of the EM approach, where it would be possible to determine whether exercise was preferable following caffeine ingestion or not. However, it is not possible to attribute the difference in task preference to the engagement of a putative target. In other words, even if caffeine (or an alternative intervention) did increase preference it would not be possible to determine why. An alternative approach is the effort expenditure for rewards task, which is a behavioural measure of cost/benefit decision-making in humans. In this decision making paradigm participants play a game in which they are given an opportunity on each trial to choose between two different task difficulty levels (effortful gripping vs merely holding the gripping device) to obtain varying monetary rewards (e.g., Klein-Flügge, Kennerley, Saraiva, Penny, & Bestmann, 2015; Kurniawan et al., 2010; Treadway et al., 2012).

There is also a version of this paradigm which has been used to investigate the role of mental effort in decision making.For example, in the first study of its kind (Kool, McGuire, Rosen, & Botvinick, 2010), participants were introduced to two decks of cards. Participants were told that they were free to choose from either deck on any trial and that

they should “feel free to move from one deck to the other whenever you choose,” but also that “if one deck begins to seem preferable, feel free choose that deck more often”.

Unannounced to participants, the decks could be distinguished by their demand, as one deck required task switching on 90% of occasions, whilst for the other deck task switching only occurred on 10% of occasions. Thus, deck preference was used to determine the role of mental effort/demand on decision-making behaviours.

On a small scale, these effort-based decision-making tasks are not only providing a measure of initial task preference but also what is essentially adherence. If you consider one hand grip effort decision to be equivalent to each day where a decision is made about

46 whether or not to engage in exercise. In this scenario, selection for the low effort option would be equivalent to selecting a sedentary behaviour in favour of exercise. Clearly assertions from the findings of these hand-grip studies (e.g., Klein-Flügge, Kennerley, Saraiva, Penny, & Bestmann, 2015; Kurniawan et al., 2010; Treadway et al., 2012) cannot be made about whole body exercise behaviour as they neither matched in terms of energy expenditure or perceived value (i.e., a brief handgrip task will elicit the same health benefits as HIIT or moderate intensity continuous exercise). However, conceptually this paradigm makes sense. The advantage of using hand-grip is the ability to explore the mechanistic relationship between the putative target and the outcome behaviour (i.e., preference) on several choice occasions per minute. The issue is that this cannot translate to whole-body exercise at a level that is required to, for example, meet public health guidelines of ≥150 min∙week-1 of moderate- or ≥75 min∙week-1 of vigorous intensity exercise (Garber et al., 2011). Therefore, an adaptation of this paradigm would be required. However, there is an alternative approach that is currently available to help establish where there is an initial preference for a particular condition.

Preference tests are not common in this field, however, they are prominent in consumer sensory evaluation, where the preference for cosmetic products, for example, helps to predict consumer purchasing behaviour (Cochrane, Dubnicka, & Loughin, 2005). In simple terms, if people prefer something they are more likely to choose it in the future. This can be applied to physical activity behaviour. For example, if an intervention elicits a change in preference for a particular condition, such as an exercise modality, theory (i.e., MIT and HT) suggests that the change in preference will predict positive changes in subsequent physical activity behaviour (i.e., adherence). Traditionally these preference tests are completed without replication, effectively leading to a single 0/1 (binary) measurement. Which would equate to participants completing one training session after ingesting caffeine, and another after ingesting placebo before choosing which condition

47 they preferred. However, with only a single preference test for each ‘consumer panellist’,

it is difficult to tell whether the consumer’s response is based on true preference, or

whether they cannot differentiate between the two products and simply selects a product at random (Cochrane et al., 2005). The response to this limitation in consumer research has been to adopt a replicated preference test approach, resulting in binomial counts of preference for each panellist. This would equate to participants completing several training sessions after consuming either caffeine or placebo and comparing each pair of sessions to establish a binomial measure of preference. Taking this approach (i.e., replicated preference tests), measuring preference for exercise conditions such as between a treatment and placebo can be used as a behavioural outcome which is related to ultimate behavioural outcome (Sheeran et al., 2017), which is exercise adherence.