Mechanisms Underlying Antidepressant Effects of Physical Activity

It is well established that any level of physical activity can help prevent and treat depression, with more strenuous activity having a greater effect. Understanding the mechanisms driving this antidepressant effect is important because it could allow exercise programmes to be made more effective, accessible, and targeted. Such knowledge could contribute to social prescribing, increasingly a priority for mental healthcare. Importantly, physical activity is highly scalable, low cost, well suited to early intervention, and has beneficial impacts on physical health co-morbidities. This trial may provide initial indications of whether there are sub-groups of depressed individuals who are particularly likely to benefit from physical activity, lead to strategies to personalise physical activity prescription based on motivational factors, and pave the way for augmentative approaches, for example combining physical activity with psychological interventions. To date the mechanisms driving the antidepressant effects of physical activity in humans are poorly understood. Building on links between depressive symptoms, reward processing and dopamine, plus evidence from animal studies that physical activity is anti-inflammatory and boosts both dopamine and reward processing, the overarching aim of this trial is to understand the mechanisms underlying the effects of physical activity in depression, focusing on the concept of motivation. The key objective is to conduct a randomised controlled trial (RCT) in N=250 depressed participants comparing aerobic exercise to a stretching/relaxation control condition, examining a range of mechanistic factors. The proposed trial will examine the impact of physical activity at multiple, linked potential levels of explanation: (1) immune-metabolic markers; (2) dopamine synthesis capacity; (3) activation in the brain's reward and effort processing circuitry;(4) effort-based decision making incorporating computational analysis; and (5) symptom networks based on fine-grained, daily measurements.