Frontline treatments for major depressive disorder (MDD), including psycho- and pharmacotherapy, have
limited effectiveness, with usual care treatment success at just 29% after 1 year. Remission rates for
depression would be enhanced if treatments could be optimized and prescribed to those most likely to benefit.
There is a critical need to develop and test novel, efficacious treatments for MDD and simultaneously work to
optimize their benefits. Resistance exercise training (RET) is a promising but understudied treatment
approach. Our recent meta-analysis found a large antidepressant effect of RET in the few very small trials with
clinically depressed samples (d=0.90), highlighting the potential of RET for treating MDD. These trials, while
underpowered to determine clinically meaningful effects, showed positive results and provide the foundation for
larger mechanistically-informed trials to confirm their promising early effects. Importantly, cerebral blood flow is
lower in adults with MDD, linked with a poor treatment response, and RET can improve cerebral blood flow in
adults. As such, RET may treat MDD via improving cerebral blood flow. However, the mechanistic pathway
linking RET’s antidepressant effects to improved cerebral blood flow in MDD is as-of-yet untested. Further, with
advances in machine learning, the identification of modifiable and stable predictors of clinical and mechanistic
change as well as adherence can inform future precision medicine initiatives for treating MDD. Thus, a trial to
confirm the efficacy of RET for MDD, understand its potential cerebrovascular mechanisms, and uncover the
modifiable predictors of its effects is urgently needed. Toward this end, we propose a confirmatory efficacy 1:1
randomized controlled trial (n=200) of 16 weeks of progressive RET or low dose RET (SHAM) in adults with
DSM-5 diagnosed MDD. Aim 1 will confirm the efficacy of RET vs SHAM on depressive symptoms at 16
weeks, and evaluate both potentially quicker and enduring effects of RET at 8, 26 and 52 weeks. Aim 2 will
determine the effect of RET vs. SHAM on the mechanistic target of cerebral blood velocity and pulsatility and
their potential mediation of antidepressant efficacy. Aim 3 will use supervised machine learning tools to predict
depression changes, cerebrovascular changes, and participant adherence. Upon completion, this study will
build towards our long-term goal of identifying and translating mechanistically-driven behavioral treatments to
reduce the global burden of mental illness by determining the extent to which a promising, accessible,
translatable RET approach can treat MDD by improving cerebrovascular function. Simultaneously, this project
will inform future precision medicine approaches that will target modifiable predictors of treatment response
and adherence to behavioral interventions to optimize MDD treatments and individually prescribe them to
those most likely to benefit. If RET effectively treats MDD, this trial would lay the foundation to apply RET as a
standalone treatment for MDD, and potentially as a standalone or augmentation treatment to reduce the
widespread burden of mood disorders and serious mental illnesses.
