Toward exercise as personalized medicine

Abstract

The early 21st century has witnessed a steady push by scientists, industry leaders, and government officials to make medicine more personalized. To date, the concept of personalized medicine has referred largely to the field of pharmacogenomics. In contrast, relatively few data exist regarding the application of preventive strategies such as physical exercise in the context of personalized medicine. Within this review, we highlight the extant literature and propose five strategies for scientists that may propel the exercise and sports science fields toward this global goal. Notably, these approaches are in addition to methods to maintain adherence to training - a well-known factor in determining exercise responsiveness. Briefly, these strategies include (1) evaluating participant responses to training at the individual as well as group level; (2) identifying sources of variability in responsiveness to training; (3) optimizing exercise dosing strategies to maximize benefits while minimizing barriers to participation; (4) evaluating the efficacy of multimodal interventions for relevant population subgroups; and (5) increasing the clinical relevance of study populations and outcomes in exercise trials. We look forward to seeing these strategies considered in trials of preventive health interventions such as exercise. Extensive future research in this area is needed for the vision of exercise as a personalized form of medicine to become a reality.

Fig. 1

Demonstration of the feasibility of providing individual participant training responses in study publications. Sections indicate the change in bodyweight of individual study participants (solid bars) in (a) a non-exercise control group and in response to three exercise dosing strategies; (b) 8 KKW; (c) 4 KKW; and (d) 12 KKW. These data highlight the interindividual variability of participant responses to a standard training programme. Reproduced from Church et al. [16], under the Open-Access Creative Commons Attribution License. KKW kilocalories per week

Fig. 2

Hypothetical scenario depicting the differential impact of three supposed interventions on a given outcome despite identical changes in the group mean. The figure depicts a scenario in which 100 older adults are given an intervention to improve usual-paced gait speed. The x-axis depicts the number of individuals with a specific change in gait speed (y-axis) in response to each intervention. This extreme example highlights how simply evaluating the mean response of a group can be misleading with respect to the clinical utility of a given intervention. N number

Fig. 3

Conceptual study design framework for investigations designed to evaluate heterogeneity in responsiveness to exercise and identify potential alternative interventions for low-sensitivity individuals. The hypothetical scenario above the dotted line depicts a ‘standard’ study design in which the efficacy of an exercise intervention on a specific health outcome is evaluated by comparing the mean change in the outcome between groups. The framework below the dotted line depicts a scenario in which the exercise study evaluates multiple health outcomes at the participant level. This approach allows investigators to identify subgroups of individuals who are either ‘high or low sensitivity’ and provides the framework for follow-up studies to improve efficacy among low-sensitivity individuals. HIIT high-intensity interval training