The study was conducted by Lucy Hancock, Kaneezah Begum and Ori Ossmy of Birkbeck, University of London, who recruited 100 young adults aged 20 to 39 and 100 older adults aged 60 to 88. Rather than dividing participants into predetermined categories such as “active” versus “sedentary,” the researchers administered a novel version of the Recent Physical Activity Questionnaire, a validated self-report instrument that captures the types, frequency and intensity of activities people perform in daily life. The questionnaire data were then fed into a k-means clustering algorithm, a form of unsupervised machine learning that identifies natural groupings within multivariate data without any prior labeling. This data-driven strategy, the authors argue, sidesteps a long-standing limitation of the field: the tendency to impose predefined activity classifications that may obscure meaningful individual differences in how people actually move.

Once the algorithm had partitioned the sample into distinct activity profiles, the researchers compared the cognitive performance of each group on two carefully chosen tasks. The first, the Towers of Hanoi, is a classic measure of planning and problem solving in which participants must move a stack of disks between pegs while obeying strict rules, requiring them to think several moves ahead. The second, Virtual Tools, is a modern computer-based task of physical reasoning in which participants must select and release virtual objects to achieve a goal, engaging their intuitive understanding of physics—gravity, momentum and collision. The Virtual Tools paradigm draws on recent computational work showing that humans simulate physical outcomes through rapid, trial-and-error mental modeling, making it a sensitive probe of how well the brain predicts the behavior of objects in the world.

The results were striking. Participants whose activity profiles were characterized by greater variety—engaging in many different types of physical activity—and by mindful, body-aware practices such as yoga or similar mind–body disciplines outperformed other groups on both planning and physical reasoning measures. This advantage held even though the sheer volume or intensity of activity did not uniformly predict better cognition. In other words, it was not the people who exercised hardest who reasoned best, but the people whose movement repertoire was most diverse and most attentive. The finding aligns with a growing body of evidence that cognitively enriched physical activity—movement that also demands coordination, learning and attention—produces stronger cognitive benefits than repetitive exercise alone.

Age, unsurprisingly, mattered. Older adults performed worse than younger adults overall on the cognitive tasks, consistent with decades of research documenting declines in executive function and fluid reasoning with typical aging. But a crucial nuance emerged in the statistical analysis: physical activity profile did not interact significantly with age group for any outcome. The relationship between varied, mindful movement and cognitive performance was statistically indistinguishable in the young and older samples. This absence of an interaction suggests that whatever benefit diverse and mindful activity confers on planning and reasoning, it appears to operate across the adult lifespan rather than being a special advantage reserved for one age group. For researchers of aging, that is an encouraging signal—it hints that the activity profiles associated with sharper cognition remain stable targets well into the eighties.

The study’s methodological approach deserves particular attention. Traditional studies of exercise and cognition typically categorize participants as meeting or not meeting physical activity guidelines, or compare specific interventions such as aerobic training against resistance training. These categorical approaches discard much of the richness of real-world behavior. By contrast, k-means clustering lets the data speak: the algorithm minimizes within-cluster variance and maximizes between-cluster separation across the full multidimensional space of self-reported activity, revealing profiles that no a priori taxonomy would have produced. The researchers also used bootstrapping—a resampling technique that repeatedly re-estimates cluster assignments to assess stability—following established statistical practice for validating k-means solutions. The choice of sample size was informed by power-analysis conventions recommending larger samples for reliable effects, and the two cognitive tasks were selected for their established reliability and validity as measures of executive planning and physical problem solving.

Why might varied and mindful movement be linked to planning and physical reasoning? The authors situate their findings within several converging theoretical frameworks. One possibility involves cognitive enrichment: activities that combine physical execution with strategic demands—such as dance, martial arts, climbing or racquet sports—simultaneously tax motor control, working memory and predictive reasoning, potentially strengthening shared neural circuitry. A second line of reasoning concerns mind–body practices specifically. Meta-analyses of meditation, yoga and tai chi have reported benefits for executive function in older adults, with proposed mechanisms ranging from stress attenuation and reduced cortisol to improved attentional control. Classic work has even shown improved performance on the Tower of London planning test following yoga practice, an intriguing precedent for the present findings. A third framework invokes the general physiology of exercise—increased brain-derived neurotrophic factor, enhanced vascular function and reduced inflammation—but the study’s data suggest these generic mechanisms alone cannot explain why variety and mindfulness, rather than volume, tracked cognitive performance.

The findings also speak to a persistent puzzle in the aging literature: the frequent failure of straightforward physical activity interventions to produce robust cognitive gains. Systematic reviews and meta-analyses of exercise trials in adults over 50 have found modest and heterogeneous effects, and some longitudinal studies have raised the possibility of reverse causation—that cognitively healthier people are simply more likely to stay active. The present study does not resolve the question of causality; its cross-sectional design cannot determine whether varied, mindful movement builds sharper reasoning or whether people with better reasoning gravitate toward richer activity repertoires. The authors are explicit on this point, calling for longitudinal and intervention studies to establish whether these activity profiles causally support cognitive function across adulthood.

Even so, the implications are tantalizing. If the associations hold up under experimental scrutiny, public health guidance might need to emphasize not just how much people move, but how they move. Encouraging older adults to diversify their activity—adding balance-based, skill-based and mindful practices to routine walking or gardening—could be a low-cost strategy for supporting the planning abilities that underpin everyday independence, from managing medications to navigating unfamiliar routes. The finding that the relevant profiles look similar in young and older adults also suggests that cultivating varied movement habits early in life may pay cognitive dividends decades later, framing physical diversity as a form of cognitive investment rather than merely a cardiovascular one.

The research also contributes to a younger scientific field: physical cognition, the study of how humans reason about objects, forces and tool use. Prior work from the same laboratory has examined how action concepts shape physical reasoning in late childhood and how physical reasoning declines with typical aging under both familiar and unfamiliar physics. The new results extend this program into the domain of lifestyle, proposing that the embodied experience of moving one’s body in diverse, deliberate ways may feed the same intuitive physics engine that the Virtual Tools task measures. On this view, the body is not just a vehicle the brain pilots; it is a training ground where the brain continuously learns the statistics of the physical world.

Limitations remain. Physical activity was self-reported, and questionnaires are known to be imperfect measures of energy expenditure, particularly in older populations where recall and interpretation of intensity categories can drift. Self-report also cannot capture the quality or cognitive load of an activity—two people may both report “yoga” while practicing at very different levels of attentional demands. The clustering approach, while flexible, yields group-level profiles that mask individual variability, and the sample, though large by laboratory standards, was recruited online and may not represent the full diversity of the adult population. The authors note that the underlying data will be made publicly available, enabling other researchers to test alternative models and replicating the profiles in independent cohorts.

For now, the study offers a fresh and data-driven lens on an old question. Instead of asking whether exercise is good for the brain, Hancock, Begum and Ossmy ask what kind of exerciser reasons best—and the answer, at least descriptively, is the one who moves in many ways and moves with the mind engaged. Whether prescribing variety and mindfulness can actually sharpen planning in a randomized trial is the obvious next experiment, and one that could reshape how clinicians, trainers and policymakers think about movement across the lifespan. In a field long dominated by step counts and heart-rate zones, the message that the brain may care more about the richness of movement than its raw quantity is a provocative and quietly viral idea—one that turns the daily workout from a metabolic chore into an opportunity for embodied learning.

Enduromics and resistomics mark a shift toward personalized exercise science by leveraging multi-layered omics data to unravel the cellular and biochemical transformations underpinning physical training. While endurance exercise, typically characterized by sustained aerobic activity, orchestrates changes in lipid metabolism, mitochondrial biogenesis, and oxygen utilization efficiency, resistance training promotes muscle hypertrophy through enhanced protein synthesis and neuromuscular remodeling. These disciplines do not merely catalog changes; instead, they identify specific metabolic fingerprints and biomarkers that differ across individuals, providing tailored insights into how people uniquely respond to various exercise modalities.

The ability of enduromics to map out modifications in lipid metabolism pathways is particularly significant. Endurance training stimulates the mobilization and oxidation of fats, enabling the body to sustain prolonged activity by optimizing energy substrates. Through this molecular lens, researchers can delineate how aerobic exercise enhances mitochondrial density and function, critical for ATP generation and overall metabolic health. Such insights extend beyond athletic performance, offering potential interventions for metabolic disorders like diabetes, where mitochondrial dysfunction is prevalent.

Resistomics likewise fills crucial knowledge gaps by focusing on the molecular drivers of muscle growth and strength. Resistance training initiates a cascade of biochemical signals that stimulate muscle fiber hypertrophy, satellite cell activation, and enhanced neuromuscular communication. By integrating proteomic and transcriptomic data, resistomics captures the dynamic remodeling of muscle tissue, revealing novel biomarkers indicative of training efficacy or injury risk. This molecular characterization not only benefits athletes seeking strength gains but also paves the way for therapeutic strategies in muscle-wasting diseases.

Beyond their biological implications, these emerging fields have profound practical applications in exercise prescription. Molecular profiling through enduromics and resistomics enables the tailoring of training regimens based on an individual’s unique molecular response patterns. Such personalized exercise programs could maximize fitness improvements while minimizing the likelihood of overtraining or injury. This approach democratizes exercise science, expanding its reach from professional athletes to the general population, thereby fostering public health and reducing the burden of non-communicable diseases linked to sedentary lifestyles.

This paradigm shift is timely, given the global rise in lifestyle-related illnesses such as cardiovascular disease and type 2 diabetes. Conventional exercise recommendations often adopt a one-size-fits-all strategy, which may overlook the molecular heterogeneity among individuals. With enduromics and resistomics, clinicians and trainers can embrace a precision medicine approach to exercise, identifying who would benefit most from endurance versus resistance training and how to modulate intensity and duration effectively.

The technological foundation supporting enduromics and resistomics is rooted in multi-omics platforms that integrate metabolomics, proteomics, genomics, and transcriptomics data. Advances in high-throughput sequencing, mass spectrometry, and bioinformatics empower researchers to manage vast datasets and identify meaningful biological signatures. Notably, these techniques allow for non-invasive or minimally invasive sampling, such as blood draws, circumventing the ethical and practical barriers associated with muscle biopsies in large populations.

Professor Katsuhiko Suzuki and his colleagues have been instrumental in advocating for this innovative approach. Their work distinguishes enduromics and resistomics from the related, yet more narrowly focused field of sportomics, which traditionally centers on elite athletes’ molecular alterations. By extending the scope to encompass a larger, more diverse population, these fields aim to generate comprehensive molecular knowledge with broader applicability.

Looking ahead, the integration of enduromics and resistomics insights with wearable technology and continuous monitoring devices could revolutionize real-time exercise feedback. For example, molecular biomarkers identified could be tracked in conjunction with physiological data such as heart rate variability and oxygen saturation, enabling dynamic adjustment of training protocols. Such integration would mark a new frontier in personalized health optimization and athletic coaching.

Moreover, understanding the molecular mechanisms governing exercise adaptation has far-reaching implications beyond fitness. Insights from these disciplines could inform novel preventative strategies and therapeutic interventions for chronic diseases, harnessing exercise as a complementary treatment. The future of medicine might well see molecularly guided exercise prescriptions implemented alongside pharmacological therapies, creating holistic management plans tailored to individual biology.

In essence, the rise of enduromics and resistomics heralds a new era in exercise science, one that moves beyond empirical training methods toward data-driven, molecularly informed strategies. This transition holds promise not only for enhancing athletic performance but also for public health promotion and disease prevention. As research in these fields expands, we can anticipate refined understanding of human physiological diversity, optimized training regimens customized for molecular profiles, and ultimately, healthier societies empowered by precision exercise science.

The potential impact on rehabilitation medicine is also noteworthy. Patients recovering from injury or surgery could benefit from exercise programs precisely calibrated to their molecular response, accelerating recovery while minimizing complications. Similarly, elderly populations vulnerable to sarcopenia may gain from resistomics-informed resistance training protocols designed to maximize muscle preservation and functional independence.

From a scientific standpoint, enduromics and resistomics exemplify the power of interdisciplinary collaboration, merging expertise in molecular biology, bioinformatics, sports science, and clinical medicine. This holistic approach synergizes data sets previously analyzed in isolation, culminating in unprecedented insights into the systemic effects of exercise. The continuing development of computational tools to analyze these complex data layers will further enhance the precision and applicability of findings.

In conclusion, these emerging fields stand at the forefront of a revolution in how we study and prescribe physical activity. By capturing the molecular signatures of endurance and resistance training across a wide population, enduromics and resistomics offer a powerful framework to personalize exercise, improve health outcomes, and advance our understanding of human biology. As research progresses, they will undoubtedly serve as cornerstones for next-generation exercise interventions that are as unique as the individuals they serve.

Subject of Research: People

Article Title: From Multi-omics To Personalized Training: The Rise of Enduromics and Resistomics

News Publication Date: 14-May-2025

Web References:
https://sportsmedicine-open.springeropen.com/articles/10.1186/s40798-025-00855-4
https://www.waseda.jp/top/en
https://katsu.suzu.w.waseda.jp/ISEI2026_Tokyo.html

References:
Authors: Kayvan Khoramipour, Sergio Maroto-Izquierdo, Simone Lista, Alejandro Santos-Lozano, and Katsuhiko Suzuki
DOI: 10.1186/s40798-025-00855-4

Image Credits: Professor Katsuhiko Suzuki from Waseda University

Keywords: Omics, Physical exercise, Human health, Biometrics, Molecular biology, Structural biology, Protein analysis, Metabolites