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  • Body Composition and Athletic Performance: What Does the Science Actually Tell Us?

    Body Composition and Athletic Performance: What Does the Science Actually Tell Us?

    Body composition is often discussed as though there is a simple relationship between physique and performance.

    Athletes are told they should be leaner, lighter or more muscular, with the implication that changing body composition will automatically improve performance.

    The scientific literature does not support such a straightforward conclusion.

    Body composition is associated with athletic performance in some sporting contexts, but the strength and direction of that relationship vary according to the sport, the event, the athlete and the performance outcome being measured (Mathisen et al., 2023; Kettunen et al., 2025).

    More importantly, much of the literature describes associations between body composition and performance rather than demonstrating that deliberately changing body composition causes an improvement in performance (Mathisen et al., 2023).

    That distinction is important.

    So, what does the evidence actually tell us?

    What is body composition?

    Body composition refers to the different components that make up total body mass. In sports research, this commonly includes fat mass, fat-free mass and, where measurement methods allow, skeletal muscle and bone mineral components (Pettersson et al., 2024).

    This matters because two athletes can have the same body mass but substantially different amounts of fat and lean tissue.

    Likewise, athletes competing in different sports can have very different body-composition profiles while achieving high levels of performance.

    Recent research comparing endurance, strength and intermittent-sport athletes found distinct body-composition profiles between sporting disciplines, reinforcing the importance of considering body composition in relation to the demands of the sport rather than treating one physique as universally optimal (Sánchez-Oliver et al., 2025).

    The basic question is therefore not simply:

    “How much does the athlete weigh?”

    It is:

    “What does that body mass consist of, and how does it relate to the demands of the sport?”

    Why might body composition influence performance?

    There are several reasons why researchers have investigated body composition as a potential determinant of performance.

    In sports where athletes repeatedly move their own body mass, body mass and its composition may influence the physical demands of movement. In strength and power sports, lean tissue is relevant because skeletal muscle contributes to force production. These relationships mean that the balance between fat mass, lean mass and total body mass can be relevant to different sporting tasks (Pettersson et al., 2024; Mathisen et al., 2023).

    However, the importance of these relationships differs considerably between sports.

    For example, the demands placed on body mass during endurance running are different from those experienced by a rugby forward or an Olympic weightlifter. Consequently, the body composition associated with elite performance in one sport should not automatically be considered appropriate for another (Mathisen et al., 2023; Delany et al., 2025).

    Is lower body fat associated with better performance?

    There is evidence that lower levels of fat mass are associated with better performance in some endurance populations.

    A critical review of 29 longitudinal, prospective and intervention studies found that higher body-fat measures were negatively associated with performance in prolonged endurance events. However, the review also found that training variables and previous performance were sometimes equally or more important predictors of subsequent performance (Mathisen et al., 2023).

    More recent research supports the existence of an association, while also demonstrating why it should not be interpreted too simplistically.

    Kettunen et al. (2025) studied 52 elite endurance athletes and examined changes in body mass and body composition during different stages of the training season alongside endurance performance. The study investigated whether changes in body composition were associated with changes in performance rather than simply comparing athletes at one point in time.

    This type of longitudinal approach is useful because it provides more information than a simple cross-sectional comparison.

    However, it still does not establish that deliberately reducing fat mass will improve performance in every athlete.

    That distinction remains important.

    Lower body fat is not automatically better

    The evidence does not identify a universal body-fat percentage at which athletes become optimally conditioned for performance.

    In fact, the critical review by Mathisen et al. (2023) found no unique body-composition cut-off that could be used to signify a performance advantage.

    Research in volleyball provides a useful sport-specific example. A systematic review and meta-analysis found that body-fat levels differed according to sex but were not clearly associated with competitive level, leading the authors to conclude that relative body fat did not appear to be a strong indicator of performance level in competitive volleyball players (González-Ravé et al., 2023).

    This is an important counterpoint to the idea that lower body fat necessarily means better performance.

    The relationship may exist in some sporting contexts without being a universal predictor of sporting ability.

    Endurance sports may be different

    The evidence for a relationship between body composition and performance is arguably strongest in endurance sports.

    A 2024 observational study of 53 elite athletics competitors found that endurance athletes and power athletes displayed different body-composition profiles. Power athletes had greater body mass and fat-free mass, while endurance and power athletes showed different seasonal patterns of lean-mass changes (Pettersson et al., 2024).

    Importantly, the researchers did not observe significant reductions in whole-body fat mass or percentage body fat from the off-season to the in-season period across the athlete groups (Pettersson et al., 2024).

    This is an interesting finding because it demonstrates that elite athletes do not necessarily need to become progressively leaner during the transition into competition.

    In the same study, increases in lean mass were observed particularly among power athletes, while male endurance athletes also demonstrated an increase in lean mass across the season (Pettersson et al., 2024).

    This provides a useful reminder that athletic development does not always involve losing fat mass.

    What about muscle mass?

    The evidence for lean mass is also important.

    A critical review of body composition and performance found that increases in muscle mass were associated with favourable performance outcomes across a broader range of sports than the evidence for low body fat (Mathisen et al., 2023).

    However, it is important not to confuse an association between muscle mass and performance with proof that increasing muscle mass itself causes the improvement.

    Resistance training provides a good example.

    A 2024 systematic review examining resistance training in elite athletes reported improvements in sport-specific performance outcomes following resistance-training interventions (Makaruk et al., 2024).

    These improvements can occur alongside changes in muscle size, but resistance training produces numerous adaptations beyond hypertrophy, including changes in neural function, force production and power. Consequently, a performance improvement following resistance training cannot automatically be attributed to an increase in muscle mass alone (Makaruk et al., 2024).

    This distinction matters when interpreting body-composition research.

    Muscle mass may contribute to performance without being the sole cause of performance improvement.

    More muscle is not necessarily better

    It is tempting to assume that if muscle mass can contribute to performance, increasing it must always be beneficial.

    The evidence does not support that conclusion.

    Elite athletes competing in different events demonstrate different combinations of lean mass and fat mass. In the 2024 longitudinal athletics study, power athletes had greater fat-free mass than endurance athletes, while both groups demonstrated seasonal changes in lean mass (Pettersson et al., 2024).

    This suggests that the amount of lean mass required is influenced by the sporting event.

    A further consideration is that additional muscle also contributes to total body mass. Whether that additional mass is advantageous depends on the performance task and whether the increase in force or power sufficiently offsets any consequences associated with carrying additional mass (Mathisen et al., 2023).

    Therefore, the more useful concept is not simply “more muscle”, but “appropriate functional lean mass.”

    Strength and muscle mass are related — but not identical

    Recent research also highlights why muscle mass should not be treated as a direct substitute for strength.

    A 2025 study in collegiate athletes examined associations between muscle-mass measures obtained using DXA, bioelectrical impedance analysis and deuterated creatine dilution, and measures of muscular strength (Wilson et al., 2025).

    The study demonstrates that different approaches to estimating muscle mass can provide different information and that muscle mass is related to, but should not simply be equated with, muscular strength (Wilson et al., 2025).

    This has an important practical implication.

    If the performance outcome of interest is strength, then strength should actually be measured.

    If the outcome is sprinting, sprinting should be measured.

    If the outcome is jumping, jumping should be measured.

    Body composition may help explain performance characteristics, but it should not replace direct performance assessment.

    Body composition and football

    Football provides a particularly interesting example because players require a combination of physical qualities rather than one dominant attribute.

    Professional footballers need to perform high-intensity actions alongside prolonged intermittent activity, with physical demands varying according to position and playing role (Sebastiá-Rico et al., 2023a).

    A systematic review and meta-analysis examining professional male footballers found significant positional differences in several measures, including body mass, muscle mass and fat-free mass. However, there were no significant positional differences in several percentage-based measures, including percentage body fat and percentage muscle mass (Sebastiá-Rico et al., 2023a).

    This suggests that even within a single sport, body-composition characteristics are not necessarily uniform.

    It also challenges the idea that there should be a single body-composition target for every footballer.

    Measurement adds another layer of complexity

    A separate systematic review and meta-analysis involving 74 studies of professional male footballers found significant differences in reported fat mass, percentage body fat and fat-free mass depending on the assessment method and equation used (Sebastiá-Rico et al., 2023b).

    This is not a trivial methodological issue.

    If two methods produce different estimates, then comparing a player’s result against a published body-fat reference range requires knowledge of how the reference value was obtained.

    A 12% body-fat result obtained using one method should not necessarily be treated as directly equivalent to 12% obtained using another.

    Consequently, body-composition reference values should be interpreted in the context of the assessment method used (Sebastiá-Rico et al., 2023b).

    Body-composition measurements have error

    This issue extends beyond football.

    Body composition can be assessed using skinfolds, bioelectrical impedance, DXA and other techniques. These methods have different assumptions and sources of measurement error (Pettersson et al., 2024; Moon et al., 2023).

    A systematic review comparing DXA and bioelectrical impedance analysis in athletes found differences between the methods in estimates of body-composition compartments, demonstrating that they should not simply be regarded as interchangeable measurement techniques (Moon et al., 2023).

    The practical consequence is that small changes should be interpreted cautiously.

    The 2024 longitudinal athletics study provides a useful example. The researchers calculated the least significant change for their DXA measurements so that changes could be interpreted against the measurement precision of the equipment and protocol (Pettersson et al., 2024).

    This is a considerably more robust approach than assuming that every change in a body-composition number represents a genuine physiological adaptation.

    Does a lower body mass mean better performance?

    Not necessarily.

    A reduction in body mass can result from changes in fat mass, lean mass, water or combinations of these compartments.

    Therefore, the direction of change in body mass alone does not tell us whether the intervention was beneficial.

    This is particularly apparent when considering weight-category sports.

    Research on combat-sport athletes demonstrates that rapid weight loss is common, but the effects of rapid weight reduction on performance are not uniform across studies and appear to depend on factors including the magnitude of weight loss, timing and the recovery period available before competition (Barley et al., 2022; Pereira et al., 2023).

    A systematic review and meta-analysis found that rapid losses of up to approximately 5% body mass over less than seven days did not significantly impair some measures of physical performance in Olympic combat athletes (Barley et al., 2022).

    However, this should not be interpreted as evidence that rapid weight loss is harmless or that it is an appropriate strategy for all athletes.

    Other evidence has reported impairments in selected physiological and performance outcomes following rapid weight loss, with responses varying according to the protocol and outcome measured (Pereira et al., 2023).

    The literature therefore supports a more nuanced conclusion:

    weight loss does not have one predictable effect on performance.

    What happens when athletes manipulate body composition gradually?

    Gradual body-composition manipulation is different from rapid weight cutting.

    A 2025 scoping review examined 73 international consensus statements, position stands and practice guidelines relating to body-mass and body-composition manipulation in athletes. Across the literature, recommendations generally emphasised individualised targets based on sport, position, sex, age and competition phase, rather than universal body-composition targets (Delany et al., 2025).

    The review also found that relatively few of the guidance documents provided specific evidence-based targets for the magnitude, rate or timing of body-composition change, demonstrating that there remains substantial uncertainty in this area (Delany et al., 2025).

    This is important because practitioners sometimes present specific body-composition targets with considerably more certainty than the underlying evidence justifies.

    What about low energy availability?

    This is an important consideration, but it should be kept in context.

    Reducing body fat generally requires an energy deficit. If energy intake becomes insufficient relative to the demands of exercise and normal physiological function, low energy availability can occur.

    The IOC consensus literature identifies low energy availability as an important health and performance consideration when athletes manipulate body mass or composition (Mountjoy et al., 2023).

    However, this does not mean that all body-fat reduction is harmful.

    A controlled, appropriately planned change in body composition is fundamentally different from chronic under-fuelling.

    The 2025 scoping review of athlete body-composition recommendations found that current expert guidance generally emphasises gradual, individualised approaches that consider performance and health rather than simply maximising weight loss (Delany et al., 2025).

    Therefore, the relevant question is not whether an athlete is losing body fat.

    It is whether the process used to achieve that change is compatible with adequate nutrition, training and health.

    The role of nutrition

    Nutrition can influence body composition, but dietary changes do not necessarily produce predictable changes in athletic performance simply because they alter body mass.

    A 2025 review examining dietary practices, body composition and sports performance highlighted the interaction between dietary intake, body composition and performance, while also noting that nutritional strategies need to be considered in the context of the athlete and sporting demands (Gough, 2025).

    Similarly, a 2025 systematic review examining protein supplementation alongside endurance training found effects on some measures of body composition and physiological adaptation, illustrating that nutritional interventions can influence body-composition outcomes without necessarily producing uniform improvements across every performance measure (Sun et al., 2025).

    This reinforces an important point:

    body composition is an outcome influenced by nutrition and training, but changing body composition is not automatically synonymous with improving performance.

    Body composition may change without fat loss

    One of the more interesting findings from longitudinal athlete research is that changes in body composition do not necessarily follow the simple pattern of “less fat, more muscle”.

    Pettersson et al. (2024) followed elite endurance and power athletes across a competitive season and found meaningful increases in lean mass in many athletes, while there was no significant overall change in whole-body fat mass.

    Among male power athletes, body mass increased by approximately 1.4 kg, with a significant increase in lean mass of approximately 1.4 kg, while fat-mass variables did not significantly change (Pettersson et al., 2024).

    This is useful because it demonstrates that an increase in body mass can occur alongside favourable changes in body composition.

    In other words:

    A heavier athlete is not necessarily a less athletic athlete.

    The composition of the additional mass and its relationship with performance are what matter.

    Body composition versus performance: which matters more?

    This is arguably the most important question.

    The critical review by Mathisen et al. (2023) found that body-composition measures were associated with performance in several studies, particularly endurance performance. However, training variables such as training volume and speed, and previous performance, were sometimes equally or more important predictors of future performance.

    This finding is consistent with the broader sports-performance literature, where performance is influenced by multiple interacting physiological, technical and training variables (Sánchez-Oliver et al., 2025).

    A recent study comparing endurance, strength and intermittent athletes also demonstrated that athlete profiles differ across multiple dimensions, including body composition, diet, physical activity and other characteristics (Sánchez-Oliver et al., 2025).

    This makes it difficult — and probably inappropriate — to isolate body composition as a single determinant of performance.

    Association does not equal causation

    This is one of the biggest limitations of the current literature.

    Suppose elite athletes in a particular sport tend to have lower body-fat levels than recreational athletes.

    There are several possible explanations.

    Lower body fat may contribute to their performance.

    Alternatively, years of training may influence both their body composition and their performance.

    It is also possible that genetics, training history, sport selection and other physiological characteristics influence both variables.

    Cross-sectional research cannot fully distinguish between these explanations.

    Longitudinal research provides stronger evidence, but even longitudinal observational studies cannot necessarily establish that a change in body composition caused the change in performance.

    Intervention studies are therefore particularly valuable.

    However, the evidence base remains relatively small.

    The critical review by Mathisen et al. (2023) identified only 29 longitudinal, prospective or intervention studies specifically examining body composition and performance, with the majority involving endurance athletes.

    That is a relatively limited evidence base considering how frequently body-composition targets are used in sport.

    Is there an ideal body-fat percentage for athletes?

    Based on the current evidence, there is no scientifically defensible universal answer.

    Elite athletes demonstrate substantial variation in body composition across sports and positions (Sebastiá-Rico et al., 2023a; Pettersson et al., 2024).

    The methods used to measure body composition can also produce different estimates (Sebastiá-Rico et al., 2023b; Moon et al., 2023).

    And the available evidence does not identify a single body-composition threshold that consistently predicts superior performance (Mathisen et al., 2023).

    Therefore, a body-fat percentage should not automatically be interpreted as:

    good,
    bad,
    too high, or
    optimal.

    Its meaning depends on the individual athlete and the context in which it is being assessed.

    What about young athletes?

    This is an area where particular caution is required.

    Body composition changes naturally during growth and maturation, and adolescent athletes are not simply smaller versions of adult athletes.

    Consequently, adult body-composition targets should not be transferred directly to young athletes.

    The IOC body-composition recommendations specifically advise against routine body-composition assessment in athletes under 18 years of age, except where there is a clear justification and appropriate professional oversight (Mathisen et al., 2023).

    The rationale is not that body composition has no relevance to youth sport, but that the potential benefits of measurement need to be weighed against the risks associated with unnecessary focus on body weight and physique during a period of development (Mathisen et al., 2023).

    So, should athletes try to change their body composition?

    Sometimes.

    The evidence provides a reasonable rationale for body-composition manipulation when there is a clear relationship between the athlete’s current composition and the demands of their sport.

    For example, reducing excess fat mass may be useful where body mass has a meaningful effect on movement economy or performance.

    Increasing lean mass may be useful where additional muscle contributes to force and power production.

    But these are context-dependent decisions, not universal rules (Mathisen et al., 2023; Delany et al., 2025).

    The 2025 review of international recommendations concluded that body-composition goals should be individualised according to the athlete, sport, position, sex, age and competition phase (Delany et al., 2025).

    That is probably a more defensible approach than prescribing a particular body-fat percentage.

    How should body composition be interpreted?

    Body composition becomes much more useful when it is interpreted alongside actual performance.

    For example:

    Body composition changes + performance improves + health is maintained

    → potentially useful intervention.

    Body composition changes + performance does not improve

    → benefit is uncertain.

    Body composition changes + performance declines

    → the intervention may not have been worthwhile.

    This is not a formal evidence-based decision rule, but it reflects an important principle in performance practice: the purpose of changing body composition should ultimately be linked to the athlete’s sporting outcome rather than the body-composition number itself (Mathisen et al., 2023; Delany et al., 2025).

    What should practitioners monitor?

    There is no single assessment that can answer whether an athlete’s body composition is appropriate.

    Depending on the sport and athlete, useful information may include:

    • Body mass
    • Fat mass
    • Fat-free mass
    • Muscle mass
    • Strength
    • Power
    • Sprint performance
    • Jump performance
    • Endurance performance
    • Training load
    • Training availability
    • Recovery
    • Nutritional intake
    • Relevant health markers

    The reason for using multiple measures is straightforward: body composition and performance represent different constructs.

    A body-composition measurement can tell us something about the athlete’s physical makeup.

    A sprint test tells us about sprint performance.

    A strength test tells us about strength.

    A match-performance measure tells us something about sporting performance.

    None should automatically be used as a substitute for another.

    A non-biased conclusion

    So, where does the evidence leave us?

    The claim that “leaner is always better” is not supported by the current literature.

    There is evidence that higher fat mass is associated with poorer endurance performance in some populations, and there is evidence that lean mass is positively associated with performance characteristics in a number of sporting contexts (Mathisen et al., 2023; Pettersson et al., 2024).

    However, these relationships are not universal.

    Professional footballers demonstrate different body-composition characteristics between positions, but not all percentage-based measures differ between positions (Sebastiá-Rico et al., 2023a).

    Competitive volleyball research has found that relative body fat does not appear to be a strong indicator of competitive level (González-Ravé et al., 2023).

    Elite endurance and power athletes also demonstrate different body-composition profiles and seasonal adaptations, with changes in lean mass occurring without significant reductions in overall fat mass in a recent longitudinal study (Pettersson et al., 2024).

    The evidence also demonstrates that measurement method matters, with different techniques producing different estimates of body-composition compartments (Moon et al., 2023; Sebastiá-Rico et al., 2023b).

    Perhaps most importantly, the evidence base directly testing whether deliberate body-composition manipulation improves sporting performance remains relatively limited (Mathisen et al., 2023).

    Therefore, the most defensible conclusion is:

    Body composition can influence athletic performance, but there is no single body composition that defines an optimal athlete.

    For some athletes, reducing fat mass may improve performance.

    For others, increasing lean mass may be beneficial.

    For others, maintaining body composition may be the most appropriate strategy.

    And for some athletes, attempting to change body composition may offer little performance benefit while introducing unnecessary nutritional or health challenges (Delany et al., 2025; Mountjoy et al., 2023).

    The question should therefore not simply be:

    “How lean should an athlete be?”

    or:

    “What should this athlete weigh?”

    A better question is:

    “Is this athlete’s current body composition limiting performance, and is there good evidence that changing it will make them better?”

    That question places body composition where it belongs:

    as one component of performance, rather than the definition of performance itself.

    Key takeaways

    1. Body composition is associated with athletic performance, but the relationship is highly dependent on the sport, event and athlete (Mathisen et al., 2023; Pettersson et al., 2024).

    2. Higher body-fat measures are associated with poorer endurance performance in some populations, but this does not establish a universal optimal body-fat percentage (Mathisen et al., 2023; Kettunen et al., 2025).

    3. Lean mass is associated with performance characteristics such as strength and power, but increasing muscle mass is not automatically responsible for performance improvements (Makaruk et al., 2024; Wilson et al., 2025).

    4. Professional footballers demonstrate positional differences in some body-composition measures, but not all measures differ between positions (Sebastiá-Rico et al., 2023a).

    5. Different body-composition assessment methods can produce different results, meaning reference values should be interpreted alongside the method used (Moon et al., 2023; Sebastiá-Rico et al., 2023b).

    6. Elite athletes can increase lean mass during a competitive season without necessarily reducing whole-body fat mass (Pettersson et al., 2024).

    7. Evidence from weight-category sports suggests that the performance consequences of weight loss depend on the magnitude, method and timing of the intervention (Barley et al., 2022; Pereira et al., 2023).

    8. Current international guidance supports individualised rather than universal body-composition targets (Delany et al., 2025).

    9. Much of the body-composition/performance literature is observational, so association should not automatically be interpreted as causation (Mathisen et al., 2023).

    10. The most useful body-composition target is therefore not necessarily the leanest or lightest physique, but the composition that supports the athlete’s specific performance demands while maintaining training capacity and health (Delany et al., 2025).

    References

    Barley, O.R., Chapman, D.W., Guppy, S.N. and Abbiss, C.R. (2022) ‘Rapid weight loss of up to five percent of the body mass in less than 7 days does not affect physical performance in official Olympic combat athletes with weight classes: a systematic review with meta-analysis’, Sports Medicine, 52, pp. 2527–2541.

    Delany, L.V., Costello, N., Jones, B. and Backhouse, S.H. (2025) ‘Dietary recommendations for body mass and composition manipulation in male and female athletes: a scoping review of consensus statements, position stands and practice guidelines from international expert groups’, Sports Medicine, 55, pp. 2445–2487. doi:10.1007/s40279-025-02285-4.

    González-Ravé, J.M., et al. (2023) ‘Body fat of competitive volleyball players: a systematic review with meta-analysis’, Sports Medicine – Open, 9.

    Gough, L.A. (2025) ‘Dietary practices, body composition, and sports performance of athletes’, Nutrients, 17(19), 3102. doi:10.3390/nu17193102.

    Kettunen, O., Mikkola, J. and Ihalainen, J.K. (2025) ‘Associations between body composition and performance in elite endurance athletes’, International Journal of Sports Physiology and Performance, 20(11), pp. 1530–1537. doi:10.1123/ijspp.2024-0506.

    Makaruk, H., Starzak, M., Tarkowski, P., Sadowski, J. and Winchester, J. (2024) ‘The effects of resistance training on sport-specific performance of elite athletes: a systematic review with meta-analysis’, Journal of Human Kinetics, 91, pp. 135–155. doi:10.5114/jhk/185877.

    Mathisen, T.F., Ackland, T., Burke, L.M. et al. (2023) ‘Best practice recommendations for body composition considerations in sport to reduce health and performance risks: a critical review, original survey and expert opinion by a subgroup of the IOC consensus on Relative Energy Deficiency in Sport (REDs)’, British Journal of Sports Medicine, 57(17), pp. 1148–1158. doi:10.1136/bjsports-2023-106812.

    Moon, J.R., et al. (2023) ‘Agreement between dual-energy X-ray absorptiometry and bioelectrical impedance analysis for assessing body composition in athletes: a systematic review and meta-analysis’, Journal of Functional Morphology and Kinesiology, 8.

    Mountjoy, M., Sundgot-Borgen, J., Burke, L. et al. (2023) ‘2023 International Olympic Committee’s consensus statement on Relative Energy Deficiency in Sport (REDs)’, British Journal of Sports Medicine, 57(17), pp. 1073–1097. doi:10.1136/bjsports-2023-106994.

    Pereira, R., et al. (2023) ‘Effects of different rapid weight loss strategies and percentages on performance-related parameters in combat sports: an updated systematic review’, Sports, 11(3), 60. doi:10.3390/sports11030060.

    Pettersson, S., Kalén, A., Gustafsson, M., Grau, S. and Caspers, A. (2024) ‘Off- to in-season body composition adaptations in elite male and female endurance and power event athletics competitors: an observational study’, BMC Sports Science, Medicine and Rehabilitation, 16, 90. doi:10.1186/s13102-024-00877-7.

    Sánchez-Oliver, A.J., et al. (2025) ‘Multidimensional differences between athletes of endurance, strength, and intermittent sports: body composition, diet, resting metabolic rate, physical activity, sleep quality, and subjective well-being’, Nutrients, 17(7), 1172. doi:10.3390/nu17071172.

    Sebastiá-Rico, J., Soriano, J.M., González-Gálvez, N. and Martínez-Sanz, J.M. (2023a) ‘Differences in body composition between playing positions in men’s professional soccer: a systematic review with meta-analysis’, Applied Sciences, 13(8), 4782. doi:10.3390/app13084782.

    Sebastiá-Rico, J., Soriano, J.M., González-Gálvez, N. and Martínez-Sanz, J.M. (2023b) ‘Body composition of male professional soccer players using different measurement methods: a systematic review and meta-analysis’, Nutrients, 15(5), 1160. doi:10.3390/nu15051160.

    Sun, W., et al. (2025) ‘Effects of protein supplementation on body composition, physiological adaptations, and performance during endurance training: a systematic review and meta-analysis’, Frontiers in Nutrition, 12.

    Wilson, M.M., et al. (2025) ‘Association of body composition measures to muscle strength using DXA, D3Cr, and BIA in collegiate athletes’, Scientific Reports, 15.

    What the evidence means in practice

    The literature supports a performance-led rather than physique-led approach to body composition.

    The strongest evidence does not suggest that athletes should be pushed towards a universal body-fat percentage. Instead, body composition should be interpreted in relation to the demands of the sport, the athlete’s individual characteristics, objective performance and longitudinal change (Mathisen et al., 2023; Delany et al., 2025).

    The evidence is also stronger for some relationships than others. The association between lower fat mass and endurance performance is reasonably consistent in relevant populations, whereas evidence that deliberately reducing fat mass improves performance in an individual athlete is considerably less extensive (Mathisen et al., 2023; Kettunen et al., 2025).

    Similarly, greater lean mass appears relevant to strength and power performance, but the performance benefits of resistance training cannot simply be attributed to muscle hypertrophy (Makaruk et al., 2024; Wilson et al., 2025).

    Finally, measurement error should not be ignored. Different assessment techniques can generate different estimates, while longitudinal DXA research demonstrates the value of considering the least significant change before interpreting a small change as physiologically meaningful (Moon et al., 2023; Pettersson et al., 2024).

    The evidence therefore supports a relatively simple principle:

    Don’t optimise body composition for its own sake. Optimise it only when there is a clear, evidence-informed reason to believe that doing so will improve the athlete’s performance.

  • Creatine Supplementation in Male and Female Athletes: An Evidence-Based Review of Mechanisms, Performance, Recovery and Sex-Specific Responses

    Creatine Supplementation in Male and Female Athletes: An Evidence-Based Review of Mechanisms, Performance, Recovery and Sex-Specific Responses

    Introduction

    Creatine monohydrate is one of the most extensively researched and scientifically supported ergogenic aids in sport and exercise science. Contemporary consensus statements confirm that creatine is effective for improving high-intensity exercise performance, increasing lean mass and enhancing training adaptations across a wide range of populations (Kreider et al., 2022; Antonio et al., 2021). Unlike many supplements in sport, creatine has a consistently strong evidence base supported by systematic reviews and meta-analyses, particularly when combined with resistance training (Chilibeck et al., 2017; Candow et al., 2019; Forbes et al., 2021). Although the physiological mechanisms are similar between males and females, emerging evidence suggests sex-specific differences in creatine metabolism, baseline muscle creatine stores and hormonal regulation may influence responsiveness and practical application (Smith-Ryan et al., 2021; Delpino et al., 2022).

    Physiological Role and Mechanisms of Action

    Creatine functions primarily within the phosphagen energy system, supporting rapid ATP regeneration during high-intensity exercise. During maximal effort, ATP is rapidly depleted and resynthesised via phosphocreatine (PCr), catalysed by creatine kinase. Contemporary evidence confirms that creatine supplementation increases intramuscular total creatine and phosphocreatine stores, enhancing ATP resynthesis during repeated high-intensity efforts (Kreider et al., 2022; Forbes et al., 2021). Key physiological effects include increased phosphocreatine availability, enhanced sprint and resistance performance, improved training volume tolerance, intracellular hydration and upregulation of anabolic signalling pathways associated with hypertrophy (Kreider et al., 2022; Antonio et al., 2021). Cell swelling is considered an anabolic stimulus contributing to protein synthesis and reduced protein breakdown (Forbes et al., 2021).

    Creatine and Performance in Males

    Meta-analytical evidence consistently demonstrates creatine improves maximal strength, lean body mass, training volume and muscular hypertrophy when combined with resistance training (Chilibeck et al., 2017; Candow et al., 2019). A meta-analysis reported significantly greater increases in lean mass with creatine supplementation alongside resistance training compared with training alone (Chilibeck et al., 2017). More recent evidence confirms increases in lean body mass of approximately ~1 kg in trained and untrained populations (Delpino et al., 2022). Creatine also improves repeated sprint ability, peak power output and anaerobic performance capacity, making it highly relevant to team sports such as football, rugby and hockey (Kreider et al., 2022; Antonio et al., 2021).

    Creatine and Performance in Females

    Although historically underrepresented in research, recent systematic reviews demonstrate that females benefit from creatine supplementation in strength, high-intensity performance and lean mass adaptations (Smith-Ryan et al., 2021; Delpino et al., 2022). Females typically have lower baseline intramuscular creatine stores and dietary intake, which may influence responsiveness (Smith-Ryan et al., 2021). While absolute gains in lean mass are often smaller than in males, relative improvements are comparable when adjusted for baseline differences (Delpino et al., 2022). Evidence suggests creatine may be particularly relevant in female athletes due to hormonal influences on energy metabolism and creatine kinase activity across the menstrual cycle (Smith-Ryan et al., 2021).

    Creatine and the Menstrual Cycle

    Oestrogen and progesterone fluctuations influence substrate utilisation, neuromuscular performance, thermoregulation and fatigue perception. These hormonal changes may also influence creatine kinase activity and energy metabolism (Smith-Ryan et al., 2021). Although phase-specific intervention studies remain limited, creatine’s role in ATP resynthesis suggests potential benefits during phases of increased fatigue or reduced energy availability.

    Creatine, Recovery and Training Adaptation

    Creatine supplementation may enhance recovery between training sessions and improve tolerance to high training loads. Evidence suggests improvements in training volume capacity, reductions in muscle damage markers in some contexts and enhanced glycogen resynthesis when combined with carbohydrate intake (Antonio et al., 2021; Kreider et al., 2022). These effects are most pronounced when creatine is combined with structured resistance or high-intensity training programmes (Candow et al., 2019).

    Creatine and Cognitive Function

    Creatine plays a role in brain energy metabolism, and supplementation may improve working memory, processing speed and cognitive resilience under stress or sleep deprivation (Antonio et al., 2021; Kreider et al., 2022). These effects are most evident in conditions of metabolic stress, making creatine relevant for athletes experiencing travel, congestion, sleep disruption or high cognitive load. This may also be relevant for female athletes experiencing cyclical fatigue or hormonal fluctuations (Smith-Ryan et al., 2021).

    Creatine Across the Female Lifespan

    In adolescence, creatine supports strength and power development alongside training. During reproductive years, it supports high-intensity performance and recovery. In perimenopause and menopause, creatine combined with resistance training improves lean mass, strength and functional performance (Candow et al., 2019; Delpino et al., 2022). Bone health outcomes remain inconclusive, but functional improvements are consistently reported.

    Safety and Long-Term Use

    Consensus statements confirm creatine monohydrate is safe when used at recommended doses in healthy individuals (Kreider et al., 2022). Evidence does not support adverse effects on kidney function, liver function, hydration status or cramping risk (Antonio et al., 2021; Kreider et al., 2022). Long-term studies support its safety in both male and female populations.

    Practical Application

    Loading phase (optional): 20 g/day split into 4 doses for 5–7 days. Maintenance: 3–5 g/day. Alternatively, 3–5 g/day without loading achieves full saturation over ~3–4 weeks. Timing is not critical; total daily intake is the key factor (Antonio et al., 2021). Creatine monohydrate remains the gold standard due to its efficacy, safety, cost-effectiveness and evidence base (Kreider et al., 2022).

    Conclusion

    Creatine monohydrate is one of the most effective and well-supported supplements in sport science. Evidence demonstrates consistent benefits for strength, lean mass, high-intensity performance, recovery and cognition in both males and females. While males show greater absolute gains in lean mass, this is largely due to baseline physiological differences rather than differences in responsiveness. In females, creatine may have additional relevance due to hormonal fluctuations and lower baseline creatine stores. Overall, creatine should be considered a foundational evidence-based supplement for athletes across sexes and performance levels.

    References

    Antonio, J. et al. (2021) Journal of the International Society of Sports Nutrition, 18, pp.1–17.
    Candow, D.G. et al. (2019) Journal of Clinical Medicine, 8, 488.
    Chilibeck, P.D. et al. (2017) Open Access Journal of Sports Medicine, 8, pp.213–226.
    Delpino, F.M. et al. (2022) Nutrition, 103–104, 111791.
    Forbes, S.C. et al. (2021) Nutrients, 13(6), 1915.
    Kreider, R.B. et al. (2022) Journal of the International Society of Sports Nutrition, 19(1), pp.1–46.
    Smith-Ryan, A.E. et al. (2021) Nutrients, 13(3), 877.

  • Bone Health in Athletes: The Role of Energy Availability, Training Load and Stress Fracture Risk

    Bone Health in Athletes: The Role of Energy Availability, Training Load and Stress Fracture Risk

    Introduction

    Bone is a dynamic tissue that responds continuously to mechanical and metabolic stimuli. In athletic populations, bone health is determined by the interaction between mechanical loading, endocrine function, and energy availability rather than isolated nutrient intake alone (Turner, 1998; Tenforde and Fredericson, 2011).

    Although sports participation is generally associated with higher bone mineral density (BMD), certain training environments particularly those characterised by low energy availability are associated with impaired bone turnover and increased risk of stress injury (Mountjoy et al., 2018; Logue et al., 2020). This makes bone health a critical but often under-monitored determinant of long-term athletic performance and injury resilience.

    Bone Remodelling and Mechanotransduction in Sport

    Bone adapts to mechanical loading via remodelling, a process regulated by osteoblast and osteoclast activity. According to mechanostat theory, bone tissue responds to strain magnitude, rate, and frequency, increasing its structural strength when subjected to sufficient mechanical stress (Turner, 1998).

    High-impact, multidirectional loading sports stimulate osteogenesis more effectively than low-impact endurance activities. Evidence consistently shows higher BMD in athletes participating in sports involving jumping, sprinting, and rapid changes of direction compared with cycling or swimming (Tenforde and Fredericson, 2011).

    However, bone adaptation is not solely dependent on mechanical stimulus. Energy availability and endocrine function significantly modulate the remodelling response, with low energy availability attenuating bone formation despite mechanical loading exposure (Ihle and Loucks, 2004).

    Energy Availability as a Central Regulator of Bone Health

    Energy availability (EA), defined as dietary energy intake minus exercise energy expenditure relative to fat-free mass, is a primary determinant of physiological function in athletes (Loucks et al., 2011).

    Low energy availability impairs bone health through multiple mechanisms including suppression of bone formation markers such as osteocalcin and procollagen type 1 N-terminal propeptide (P1NP), alongside increased bone resorption markers such as C-terminal telopeptide (CTX) (Ihle and Loucks, 2004; Logue et al., 2020).

    Endocrine disruption is also central to this process. Low EA reduces insulin-like growth factor-1 (IGF-1), leptin, oestrogen, and testosterone, all of which are essential regulators of bone metabolism (Mountjoy et al., 2018). These hormonal changes shift bone turnover towards net resorption and impair recovery from microdamage accumulation.

    Stress Fractures and Bone Stress Injuries

    Bone stress injuries represent a continuum from periosteal oedema to cortical fracture and occur when repetitive submaximal loading exceeds the bone’s capacity for remodelling and repair (Warden et al., 2014).

    Key risk factors consistently identified in peer-reviewed literature include low energy availability, rapid increases in training load, prior stress fracture history, hormonal disturbances, and low bone mineral density (Mountjoy et al., 2018; Tenforde et al., 2015).

    Athletes with low energy availability exhibit significantly increased incidence of stress fractures due to impaired bone formation and delayed microdamage repair processes (Logue et al., 2020).

    Hormonal Regulation of Bone Metabolism in Athletes

    Bone remodelling is tightly regulated by endocrine signalling. Oestrogen and testosterone are critical for maintaining bone formation and inhibiting resorption (Mountjoy et al., 2018).

    In low energy availability states, oestrogen concentrations may decrease in female athletes, particularly in cases of functional hypothalamic amenorrhoea, while testosterone may also decline in male athletes. Insulin-like growth factor-1 (IGF-1) is suppressed, reducing osteoblastic activity, while cortisol may increase, promoting catabolic effects on bone tissue (Mountjoy et al., 2018).

    These endocrine changes collectively shift bone metabolism towards increased resorption and reduced formation.

    Mechanical Loading: Protective and Dose-Dependent Effects

    Mechanical loading remains one of the most potent stimuli for bone formation. High-impact loading generates strain-induced deformation and fluid flow within the bone matrix, triggering osteogenic responses (Turner, 1998).

    High-impact sports consistently demonstrate greater bone mineral density compared with low-impact endurance sports (Tenforde and Fredericson, 2011). Furthermore, plyometric and resistance training enhance site-specific bone strength adaptations (Tenforde et al., 2015).

    However, excessive repetitive loading without adequate recovery or energy availability results in microdamage accumulation and increased risk of bone stress injury (Warden et al., 2014).

    Nutrition and Bone Health: Beyond Calcium

    Energy availability is the primary nutritional determinant of bone health in athletes. Low energy availability suppresses bone formation even when calcium and vitamin D intake are adequate (Loucks et al., 2011; Mountjoy et al., 2018).

    Calcium plays a key role in bone mineralisation, but its effectiveness is dependent on hormonal status and energy balance. Vitamin D is essential for calcium absorption and bone metabolism, with deficiency associated with increased fracture risk in athletes (Close et al., 2013).

    Protein intake supports bone matrix formation and collagen synthesis. Evidence indicates that higher protein intakes do not negatively impact bone health when calcium intake is sufficient and may enhance IGF-1-mediated anabolic signalling (Shams-White et al., 2017).

    RED-S and Bone Health

    Relative Energy Deficiency in Sport (RED-S) describes impaired physiological function resulting from low energy availability. Bone health is one of the most significantly affected systems (Mountjoy et al., 2018).

    RED-S is associated with reduced bone formation markers, increased bone resorption, impaired attainment of peak bone mass, and increased stress fracture risk. Persistent low energy availability during key developmental periods may result in long-term deficits in bone mineral density (Mountjoy et al., 2018).

    Integration of Training Load and Energy Availability

    Bone adaptation is dependent on the interaction between mechanical loading and energy availability. Mechanical loading is only osteogenic when sufficient energy is available to support remodelling processes.

    When energy availability is low, the osteogenic response to loading is blunted, bone resorption exceeds formation, and adaptation to training is impaired. This explains the high incidence of bone stress injuries in athletes experiencing high training loads without adequate fuelling (Logue et al., 2020; Warden et al., 2014).

    Practical Implications for Athlete Management

    Optimising bone health in athletes requires a multi-factorial approach that includes maintaining adequate energy availability, structured mechanical loading, and appropriate nutritional support.

    Early identification of RED-S risk factors such as menstrual dysfunction, recurrent stress injury, fatigue, and rapid training load increases is essential for prevention (Mountjoy et al., 2018).

    Conclusion

    Bone health in athletes is governed primarily by the interaction between energy availability, endocrine function, and mechanical loading rather than isolated nutrient intake. Low energy availability is the most significant modifiable risk factor for impaired bone metabolism and stress injury development. Maintaining adequate energy availability alongside structured loading strategies is essential for optimal skeletal adaptation and injury prevention.

    References

    Close, G.L., Leckey, J., Patterson, M., et al. (2013) ‘Vitamin D and skeletal muscle strength in athletes’, Scandinavian Journal of Medicine & Science in Sports.

    Ihle, R. and Loucks, A.B. (2004) ‘Dose-response relationships between energy availability and bone turnover’, Journal of Bone and Mineral Research.

    Logue, D.M., Madigan, S.M., Melin, A., et al. (2020) ‘Low energy availability in athletes’, Sports Medicine.

    Loucks, A.B., Kiens, B. and Wright, H.H. (2011) ‘Energy availability in athletes’, Journal of Sports Sciences.

    Mountjoy, M., Sundgot-Borgen, J., Burke, L., et al. (2018) ‘IOC consensus statement on RED-S’, British Journal of Sports Medicine.

    Shams-White, M.M., Chung, M., et al. (2017) ‘Protein intake and bone health’, American Journal of Clinical Nutrition.

    Tenforde, A.S. and Fredericson, M. (2011) ‘Influence of sports participation on bone health’, Sports Health.

    Tenforde, A.S., et al. (2015) ‘Impact activity and bone density’, PM&R.

    Turner, C.H. (1998) ‘Three rules for bone adaptation’, Bone.

    Warden, S.J., Davis, I.S. and Fredericson, M. (2014) ‘Stress fracture biomechanics’, British Journal of Sports Medicine

  • Continuous Glucose Monitoring in Healthy Individuals: Evidence, Interpretation, and Practical Value Beyond Clinical Use

    Continuous Glucose Monitoring in Healthy Individuals: Evidence, Interpretation, and Practical Value Beyond Clinical Use

    Introduction

    Continuous glucose monitoring (CGM) systems are well established in diabetes care, where strong evidence demonstrates improvements in glycaemic variability, time in range, and hypoglycaemia prevention in both type 1 and insulin-treated type 2 diabetes populations (Battelino et al., 2019; Beck et al., 2017). In recent years, CGMs have increasingly been adopted by individuals without diabetes for purposes such as dietary optimisation, metabolic health tracking, and performance monitoring. This expansion reflects interest in precision nutrition, although evidence supporting clinical benefit in healthy populations remains limited (Liao et al., 2026). The key question is whether additional metabolic data improves outcomes in already well-regulated physiology.

    Physiological Basis of CGM Technology

    CGMs measure glucose in interstitial fluid using enzymatic sensors. Interstitial glucose is physiologically linked to blood glucose but is not identical. A consistent limitation is the time lag between blood and interstitial compartments, typically 5–15 minutes depending on metabolic state and perfusion (Torimoto and Okada, 2021). This lag becomes more pronounced during rapid changes such as postprandial absorption or exercise. Accuracy is also influenced by sensor kinetics, calibration algorithms, and tissue-level variability. Facchinetti (2016) notes that CGM accuracy is generally acceptable in diabetic ranges but is reduced at lower glucose levels and during rapid glycaemic shifts, which are more typical in healthy individuals. This means CGM outputs in normoglycaemic populations should be interpreted as trend-based estimates rather than precise biochemical measurements.

    Inter-Individual Variability in Glycaemic Response

    A key rationale for CGM use in personalised nutrition is inter-individual variability in postprandial glycaemic responses (PPGRs). Zeevi et al. (2015) demonstrated that identical meals produce highly variable glucose responses driven by factors including microbiome composition, insulin sensitivity, sleep, and anthropometrics. Their machine-learning model was able to predict PPGRs and showed that personalised dietary interventions could reduce postprandial glucose excursions. Mendes-Soares et al. (2019) replicated these findings in an independent cohort, confirming that glycaemic responses are highly individualised. Mechanistically, variability reflects differences in gastric emptying, insulin secretion dynamics, hepatic glucose output, and peripheral glucose uptake. However, variability in physiological response does not necessarily imply that reducing all glucose excursions improves long-term health outcomes.

    CGM Effects in Non-Diabetic Populations

    The most comprehensive synthesis of evidence is provided by Liao et al. (2026), who conducted a systematic review and meta-analysis of CGM use in non-diabetic populations. They reported small reductions in mean glucose and improved dietary awareness, but no consistent improvements in BMI, glycaemic variability, or long-term metabolic outcomes in healthy individuals. Benefits were more evident in individuals with impaired glucose regulation, suggesting CGM utility may be dependent on baseline metabolic status. These findings indicate CGMs function more effectively as behavioural feedback tools than as metabolic intervention devices in healthy populations.

    Do Postprandial Glucose Spikes Matter?

    Postprandial increases in glucose are a normal physiological response to carbohydrate ingestion. In healthy individuals, glucose homeostasis is maintained through coordinated insulin secretion, hepatic regulation, and peripheral uptake. DeFronzo et al. (2015) describe these responses as central to metabolic flexibility rather than pathological dysfunction. While glycaemic variability has been associated with adverse outcomes in diabetic populations, causality in healthy individuals is not established. No randomised controlled trials demonstrate that reducing physiological glucose excursions improves cardiovascular outcomes, body composition, or longevity in normoglycaemic populations.

    CGM Accuracy and Interpretation Limitations

    CGM interpretation is limited by both physiological and technical factors. Interstitial lag introduces temporal discrepancy between blood and tissue glucose (Torimoto and Okada, 2021). Sensor accuracy decreases during rapid glucose fluctuations and at lower glucose ranges (Facchinetti, 2016). In healthy individuals, where glucose variability is relatively small, these limitations may disproportionately influence interpretation, increasing the risk of misclassifying normal physiological variation as meaningful metabolic disturbance.

    Behavioural and Psychological Considerations

    CGMs provide continuous physiological feedback, which can influence behaviour. Vettoretti et al. (2020) highlight that while CGMs may improve awareness of dietary patterns, continuous monitoring can also increase cognitive load and attention bias toward short-term fluctuations. This may lead to over-interpretation of normal glucose variability, increased dietary restriction, and reduced dietary flexibility in some individuals. Importantly, there is no evidence that focusing on minimising all glucose excursions improves dietary quality or long-term health outcomes in healthy populations.

    CGMs in Sport and Exercise

    CGMs are increasingly used in athletic populations to monitor carbohydrate availability, fuelling strategies, and recovery nutrition. However, exercise significantly alters glucose kinetics through catecholamine-mediated hepatic glucose output, increased skeletal muscle uptake, and changes in insulin sensitivity. These physiological responses complicate interpretation of CGM data during training and recovery. Jeukendrup (2017) notes that while carbohydrate availability is central to performance, there is no strong evidence that CGM-guided nutrition improves athletic performance outcomes in controlled trials.

    Future Directions: Precision Nutrition

    CGMs are being integrated into precision nutrition models alongside microbiome and dietary data. Zeevi et al. (2015) and Mendes-Soares et al. (2019) demonstrated that machine-learning approaches can predict individual glycaemic responses with moderate accuracy, supporting the concept of metabolic phenotyping. However, translation into clinical practice remains limited due to lack of long-term outcome data, limited external validity, and absence of large-scale randomised controlled trials demonstrating clinical benefit.

    Practical Implications

    Current evidence suggests CGMs may improve short-term dietary awareness and engagement behaviours (Liao et al., 2026). Individual variability in glycaemic response is well established (Zeevi et al., 2015), but does not justify routine intervention in healthy populations. Physiological glucose excursions are not inherently harmful (DeFronzo et al., 2015). CGM data must be interpreted cautiously due to physiological lag and measurement limitations (Facchinetti, 2016). Behavioural effects may be beneficial or maladaptive depending on the individual context (Vettoretti et al., 2020).

    Are CGMs Worth Using in Healthy Individuals?

    In healthy individuals, CGMs are not currently supported as a routine metabolic optimisation tool. Evidence suggests their primary value is educational and behavioural rather than clinical. They may help increase awareness of dietary patterns and individual variability but do not currently demonstrate improvements in body composition, performance, or long-term health outcomes (Liao et al., 2026). In individuals with impaired glucose regulation, CGMs may have greater utility as part of lifestyle intervention strategies. In athletes, CGMs may provide descriptive insights into fuelling responses but lack evidence for performance enhancement. Overall, CGMs should be considered informational rather than interventional tools in healthy populations.

    Conclusion

    Continuous glucose monitoring is a well-established clinical tool in diabetes management and an emerging technology in personalised nutrition. However, current evidence does not support routine use in healthy individuals for improving metabolic health, performance, or body composition. While CGMs provide valuable insight into inter-individual variability in glycaemic responses, physiological glucose excursions in healthy individuals are not inherently pathological, and the clinical significance of modifying them remains unproven. CGMs are best viewed as research and educational tools rather than essential health optimisation devices in normoglycaemic populations.

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    References

    Battelino, T., Danne, T., Bergenstal, R.M., Amiel, S.A., Beck, R., Biester, T., et al. (2019) ‘Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range’, Diabetes Care, 42(8), pp. 1593–1603.

    Beck, R.W., Riddlesworth, T.D., Ruedy, K., Ahmann, A., Bergenstal, R., Haller, S. (2017) ‘Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections’, Annals of Internal Medicine, 167(6), pp. 365–374.

    DeFronzo, R.A., Ferrannini, E., Groop, L., Henry, R.R., Herman, W.H., Holst, J.J., et al. (2015) ‘Type 2 diabetes mellitus’, Diabetes Care, 38(1), pp. 142–150.

    Facchinetti, A. (2016) ‘Continuous glucose monitoring sensors: past, present and future algorithmic challenges’, Sensors, 16(12), 2098.

    Jeukendrup, A.E. (2017) ‘Periodized nutrition for athletes’, Sports Medicine, 47(S1), pp. 51–63.

    Liao, X., et al. (2026) ‘Continuous glucose monitoring in non-diabetic individuals: systematic review and meta-analysis’, European Journal of Medical Research, 31, pp. 1–15.

    Mendes-Soares, H., et al. (2019) ‘Assessment of a personalized approach to predicting postprandial glycemic responses’, Cell Host & Microbe, 26(3), pp. 424–435.

    Torimoto, K. and Okada, Y. (2021) ‘Accuracy and limitations of continuous glucose monitoring systems’, Diabetology International, 12, pp. 1–10.

    Vettoretti, M., Facchinetti, A. and Sparacino, G. (2020) ‘Continuous glucose monitoring: interpretation and behavioural implications’, Diabetes Technology & Therapeutics, 22(9), pp. 1–10.

    Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., et al. (2015) ‘Personalized nutrition by prediction of glycemic responses’, Cell, 163(5), pp. 1079–1094

  • Sleep Optimisation for Athletes: A Critical Evidence-Based Review of Recovery, Behaviour and Performance

    Sleep Optimisation for Athletes: A Critical Evidence-Based Review of Recovery, Behaviour and Performance

    Introduction

    Sleep is widely recognised as a foundational biological process underpinning recovery, cognitive performance and physiological adaptation. In athletic populations, sleep is increasingly considered a modifiable performance variable alongside training load and nutrition. A consensus statement on sleep and the athlete reports that many athletes fail to achieve recommended sleep durations, particularly during periods of travel, competition and intensified training (Walsh et al., 2021). This article critically evaluates peer-reviewed evidence on sleep and athletic performance, with emphasis on physiological mechanisms, behavioural constraints and applied strategies relevant to coaches and athletes.

    Sleep Physiology and Performance-Relevant Functions

    Sleep consists of non-rapid eye movement (NREM) and rapid eye movement (REM) stages, both contributing to recovery and adaptation. Evidence indicates NREM sleep is associated with tissue repair, immune regulation and growth hormone secretion (Dattilo et al., 2011; Halson, 2014) while REM sleep is associated with memory consolidation and motor learning (Walker and Stickgold, 2006; Rasch and Born, 2013). Sleep is therefore involved in neuromuscular adaptation, cognitive processing and recovery from training load (Fullagar et al., 2015; Halson, 2014).

    Consequences of Sleep Restriction

    Sleep restriction has been associated with:

    • Impaired cognitive performance and reaction time (Lim and Dinges, 2010; Pilcher and Huffcutt, 1996)
    • Reduced endurance performance and increased perceived exertion (Fullagar et al., 2015; Halson, 2014)
    • Reduced sprint and sport-specific performance (Mah et al., 2011; Waterhouse et al., 2007)
    • Increased injury risk in youth athletes (Milewski et al., 2014; Watson, 2017)
    • Impaired glucose regulation and insulin sensitivity (Spiegel et al., 1999; Tasali et al., 2008)
    • Altered appetite regulation and increased energy intake (Taheri et al., 2004; St-Onge et al., 2016)

    Why Athletes Experience Sleep Disruption

    Evening training, elevated sympathetic activity, increased core temperature and travel all contribute to disrupted sleep patterns (Fullagar et al., 2015; Samuels, 2012).

    Lifestyle Behaviours and Sleep

    Video gaming, social media use, streaming and bedtime procrastination are all associated with delayed sleep onset and reduced sleep duration (Weaver et al., 2010; Levenson et al., 2017; Exelmans and Van den Bulck, 2016).

    Caffeine and Alcohol

    Caffeine reduces sleep duration and quality even when consumed up to 6 hours pre-bed (Drake et al., 2013). Alcohol disrupts REM sleep and increases nocturnal awakenings (Ebrahim et al., 2013).

    Sleep Extension and Napping

    Sleep extension improves sprint performance and reaction time in athletes (Mah et al., 2011). Short naps improve alertness and cognitive performance (Waterhouse et al., 2007).

    Sleep Hygiene

    Consistent routines and reduced evening stimulation improve subjective sleep quality but show variable objective effects (Irish et al., 2015).

    Nutritional Interventions and Sleep

    Nutrition may influence sleep via neurotransmitter synthesis, thermoregulation, glucose metabolism and circadian signalling, although the evidence base remains heterogeneous.

    Carbohydrate Timing and Glycaemic Response

    High glycaemic carbohydrate intake may reduce sleep onset latency via insulin-mediated amino acid shifts increasing tryptophan availability (Afaghi et al., 2007). However, systematic reviews highlight inconsistent findings and strong dependence on timing, dose and individual variability (St-Onge et al., 2016). In practice, carbohydrate intake should prioritise performance recovery rather than sleep manipulation.

    Protein Intake and Pre-Sleep Nutrition

    Pre-sleep protein ingestion does not impair sleep architecture and may support overnight muscle protein synthesis (Res et al., 2012; Trommelen and van Loon, 2016). However, there is no evidence that protein directly improves sleep quality, reinforcing its role in recovery rather than sleep optimisation.

    Tart Cherry Juice

    Tart cherry supplementation may improve sleep duration and efficiency, potentially via melatonin content and anti-inflammatory effects (Howatson et al., 2012; Pigeon et al., 2010). Evidence remains limited by small sample sizes and short trial durations.

    Glycine Supplementation

    Glycine may improve subjective sleep quality and reduce fatigue via thermoregulatory and inhibitory neurotransmission pathways (Inagawa et al., 2006; Bannai and Kawai, 2012). However, replication in athletic populations is lacking.

    Magnesium

    Magnesium influences neuromuscular excitability and stress regulation relevant to sleep physiology (Boyle et al., 2017). A randomised controlled trial showed improved sleep quality in older adults with insomnia symptoms (Abbasi et al., 2012). However, systematic reviews highlight limited and inconsistent evidence, with poor generalisability to young athletic populations (Boyle et al., 2017). Magnesium should therefore be targeted primarily at individuals with low dietary intake rather than used universally.

    Melatonin

    Melatonin is effective for circadian disruption such as jet lag but shows inconsistent benefits in healthy non-shifted populations (Herxheimer and Petrie, 2002; Ferracioli-Oda et al., 2013).

    Caffeine–Sleep Interaction

    Caffeine significantly impairs sleep duration and quality even when consumed 6 hours before bedtime (Drake et al., 2013; Clark and Landolt, 2017).

    Wearable Sleep Tracking

    Wearable devices are widely used in sport for sleep monitoring but show only moderate accuracy for total sleep time and poor accuracy for sleep staging compared with polysomnography (de Zambotti et al., 2018; Chinoy et al., 2021). Their primary value lies in tracking behavioural metrics such as sleep opportunity, bedtime consistency and wake timing rather than physiological sleep architecture. Athletic populations may experience further inaccuracies due to elevated heart rate and training stress. Psychological effects such as orthosomnia may also influence sleep perception. Wearables should therefore be used as behavioural monitoring tools rather than diagnostic instruments.

    Practical Recommendations (Evidence-Graded)

    Strong Evidence

    • Aim for 8–10 hours sleep opportunity per night (Walsh et al., 2021)
    • Avoid caffeine within 6 hours of sleep (Drake et al., 2013)
    • Avoid alcohol before bedtime (Ebrahim et al., 2013)
    • Maintain consistent sleep–wake schedules (Irish et al., 2015)

    Moderate Evidence

    • Sleep extension during heavy training (Mah et al., 2011)
    • Short naps (~20–30 min) for performance and alertness (Waterhouse et al., 2007)
    • Sleep hygiene strategies (Irish et al., 2015)
    • Reduce evening digital stimulation (Levenson et al., 2017)

    Emerging Evidence

    • Tart cherry supplementation (Howatson et al., 2012)
    • Glycine supplementation (Inagawa et al., 2006)
    • Melatonin for jet lag/circadian disruption (Ferracioli-Oda et al., 2013)

    Conclusion

    Sleep is a key recovery modulator in athletic performance, with strong evidence linking restriction to impaired cognitive, metabolic and physical outcomes. However, sleep is influenced by behavioural, nutritional and environmental factors including caffeine, alcohol, digital media use and training schedules. The strongest interventions remain behavioural: increasing sleep opportunity, reducing evening stimulation and maintaining consistent routines. Nutritional and technological interventions may offer adjunct support but remain secondary to foundational sleep behaviours.

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  • Peptides in the Fitness Industry: Mechanisms, Adaptation, Evidence, Risks and Scientific Limitations.

    Peptides in the Fitness Industry: Mechanisms, Adaptation, Evidence, Risks and Scientific Limitations.

    Peptides have moved rapidly from biomedical research into mainstream fitness culture, marketed as a targeted means of enhancing muscle growth, recovery and overall physiological function. They are often presented as a “precision” alternative to traditional performance-enhancing approaches, promising specific, controllable effects with fewer risks. However, a closer examination of the scientific literature reveals a more complex and far less certain picture. While peptide biology is well understood at a mechanistic level, the evidence supporting meaningful improvements in training adaptation and athletic performance is limited, inconsistent and frequently constrained by methodological weaknesses. The key issue is therefore not whether peptides can influence physiology, but whether they meaningfully improve adaptation to training, which remains the primary determinant of performance outcomes.

    What Are Peptides

    Peptides are short chains of amino acids that function predominantly as signalling molecules within the body. Unlike larger proteins, which primarily serve structural or enzymatic roles, peptides regulate biological processes by binding to receptors and initiating intracellular responses. A number of critical physiological regulators are peptides, including insulin and insulin-like growth factor‑1, which plays a central role in skeletal muscle growth, regeneration and adaptation through its influence on satellite cell activity and protein synthesis pathways (Ahmad et al., 2020). In applied fitness settings, peptides typically refer to synthetic analogues designed to manipulate these signalling systems, often through hormonal or regenerative pathways.

    Mechanisms of Action

    The Growth Hormone–IGF‑1 Axis

    The most extensively discussed mechanism underpinning peptide use in fitness is the growth hormone–IGF‑1 axis. Growth hormone is secreted from the pituitary gland and stimulates the production of IGF‑1 both systemically and within muscle tissue. IGF‑1 then binds to receptors on muscle cells, activating intracellular pathways such as PI3K/Akt and mTOR, which regulate protein synthesis, cell proliferation and survival (Machida and Booth, 2004; Ahmad et al., 2020). Through these mechanisms, IGF‑1 facilitates satellite cell activation, muscle fibre hypertrophy and tissue repair following damage.

    Interaction With Exercise Physiology

    Resistance exercise itself strongly activates the same pathways targeted by peptides. Mechanical loading increases local IGF‑1 expression within muscle tissue and stimulates mTOR signalling, which is central to muscle protein synthesis (Machida and Booth, 2004). This highlights an important limitation: peptides are not introducing new biological mechanisms but attempting to manipulate systems already maximally stimulated through appropriate training and nutrition.

    Tissue Repair and Regeneration Pathways

    Some peptides are proposed to influence recovery through mechanisms such as angiogenesis, enhanced collagen synthesis, modulation of inflammatory pathways and improved fibroblast activity. These mechanisms underpin claims relating to improved healing of connective tissues and reduced injury recovery time. However, the evidence supporting these claims is heavily dominated by preclinical animal research, with limited high-quality human validation.

    Training Adaptation: The Central Issue

    Training adaptation is a multifactorial process driven by the interaction between mechanical, metabolic and biological signals. It depends on progressive overload, motor unit recruitment, neuromuscular adaptation, nutrient availability and recovery processes rather than a single signalling pathway. Peptides influence only a narrow component of this system, primarily intracellular signalling.

    Adaptation follows a sequence whereby a sufficient training stimulus produces intracellular signalling, leading to protein synthesis, structural change and ultimately functional improvement. Peptides act at the signalling stage but do not replace the initial mechanical stimulus. This leads to a critical principle: increasing signalling alone does not produce meaningful adaptation in the absence of appropriate training.

    A consistent finding across the literature is the discrepancy between molecular responses and functional outcomes. Studies often demonstrate increases in IGF‑1, activation of anabolic signalling pathways and changes in gene expression, yet these do not consistently translate into increased strength, improved power output or enhanced performance. For example, collagen peptide studies show increased signalling pathway activation without significant improvements in strength or functional performance (Centner et al., 2022; Balshaw et al., 2022). This highlights that molecular changes are necessary but not sufficient for meaningful adaptation.

    Adaptation is also constrained by limiting factors such as training stimulus, protein intake, energy availability and recovery. Peptides do not override these constraints, meaning increased signalling cannot compensate for inadequate training or nutrition. Additionally, most peptide studies are conducted in untrained or clinical populations, where adaptive capacity is higher. In trained athletes, physiological systems are already optimised, meaning the marginal benefit of additional signalling is likely to be minimal due to ceiling effects.

    Evidence Base

    Growth Hormone and Related Interventions

    The strongest human evidence comes from research on growth hormone. Randomised controlled trials demonstrate that growth hormone administration can increase lean body mass and reduce fat mass, particularly in ageing or hormone-deficient populations (Hoffman et al., 2004; Fernández‑Garza et al., 2025). However, interpretation of these findings is complex. Growth hormone increases extracellular fluid retention and connective tissue mass, meaning increases in lean mass do not necessarily represent increases in contractile muscle tissue.

    Despite changes in body composition, functional outcomes are inconsistent. Upper-body strength often shows no significant improvement, while lower-body strength gains are modest and variable (Tavares et al., 2013). Performance outcomes are rarely improved, indicating that growth hormone-related hypertrophy is not equivalent to training-induced hypertrophy.

    Growth hormone interventions are also associated with metabolic consequences, including reduced insulin sensitivity and impaired glucose tolerance (Fernández‑Garza et al., 2025). These findings raise concerns regarding long-term health risks and highlight the importance of risk–benefit analysis.

    Collagen Peptides and Resistance Training

    Research on collagen peptides provides additional insight into the disconnect between molecular signalling and functional outcomes. Acute studies demonstrate increased activation of anabolic signalling pathways following collagen supplementation and resistance exercise (Centner et al., 2022). However, longer-term studies show increases in muscle volume without corresponding improvements in strength or performance (Balshaw et al., 2022). This suggests that structural changes at the tissue level do not necessarily translate into functional improvements.

    Protein Versus Peptides

    Comparative studies consistently demonstrate that protein quality and quantity are more important determinants of adaptation than peptide supplementation. Whey protein has been shown to produce greater increases in muscle size than collagen peptides, despite matched leucine content, while strength gains remain similar (Jacinto et al., 2022). This reinforces established principles of sports nutrition, where total protein intake and amino acid availability drive adaptation.

    Recovery Peptides

    Recovery peptides such as BPC‑157 are widely discussed within fitness circles but lack robust human evidence. Systematic reviews indicate that the majority of studies are preclinical, with very few human trials and a lack of randomised controlled evidence (Vasireddi et al., 2025). Narrative reviews further confirm that although animal models demonstrate promising effects, these findings have not been reliably replicated in humans (McGuire et al., 2025). Current claims regarding recovery peptides are therefore not supported by strong clinical data.

    Study Design Limitations

    The peptide evidence base is limited by consistent methodological issues. Many studies involve small sample sizes, reducing statistical power and increasing variability. Research is often conducted in non-athletic populations, limiting applicability to trained individuals. Study durations are typically short, preventing long-term conclusions about adaptation or safety.

    There is a heavy reliance on surrogate outcomes such as lean body mass, hormone concentrations and gene expression, which do not necessarily reflect real-world performance outcomes. Confounding variables such as training programme design, nutritional intake and recovery practices are often not well controlled. Additionally, there is a lack of replication across independent studies and a significant translational gap between animal and human research, particularly in recovery peptide investigations.

    Safety Considerations

    Acute risks include fluid retention, impaired glucose metabolism, reduced insulin sensitivity and injection-related complications. Chronic risks are less well understood but potentially more serious. IGF‑1 promotes cell proliferation and inhibits apoptosis, and chronic elevation is associated with increased cancer risk (Ahmad et al., 2020). Long-term concerns also include cardiovascular strain, endocrine disruption and metabolic dysfunction. A key limitation is the absence of long-term human safety data, meaning the true risk profile remains unclear.

    Practical Implications

    Peptides should not be considered first-line interventions for performance enhancement. Training, nutrition and recovery remain the primary drivers of adaptation. Peptides should be viewed as experimental due to the limited and inconsistent evidence base. The risk–reward profile is currently unfavourable, with modest potential benefits and uncertain long-term risks.

    Practitioners should prioritise evidence-based strategies and educate athletes on the limitations of current knowledge. Any consideration of peptide use should occur within a medically supervised context. Focus should remain on progressive resistance training, adequate protein intake, creatine supplementation and sleep optimisation, all of which are supported by high-quality evidence.

    Final Conclusion

    Peptides are biologically plausible and mechanistically sound, influencing key pathways involved in muscle growth and recovery. However, the current evidence indicates that they do not meaningfully enhance training adaptation or performance beyond what can be achieved through well-structured training and nutrition.

    The literature is constrained by methodological weaknesses, non-athletic populations, reliance on surrogate outcomes and limited long-term data. At the same time, safety concerns remain unresolved.

    From a performance perspective, peptides do not replace training, do not reliably enhance adaptation and should currently be regarded as experimental rather than evidence-based tools. The fundamentals of performance continue to provide the most effective and reliable outcomes.

    References

    Ahmad, S.S. et al. (2020) Implications of insulin-like growth factor‑1 in skeletal muscle and various diseases. Cells, 9(8), 1773

    Balshaw, T.G. et al. (2022) The effect of specific bioactive collagen peptides on function and muscle remodeling during human resistance training. Acta Physiologica

    Centner, C. et al. (2022) Supplementation of specific collagen peptides following high-load resistance exercise upregulates gene expression. Frontiers in Physiology

    Fernández‑Garza, L.E. et al. (2025) Growth hormone and aging: a clinical review. Frontiers in Aging

    Hoffman, A.R. et al. (2004) Growth hormone replacement therapy in adult-onset GH deficiency. Journal of Clinical Endocrinology & Metabolism

    Jacinto, J.L. et al. (2022) Whey protein supplementation is superior to leucine-matched collagen peptides. International Journal of Sport Nutrition and Exercise Metabolism

    Machida, S. and Booth, F.W. (2004) Insulin-like growth factor‑1 and satellite cell proliferation. Proceedings of the Nutrition Society

    McGuire, F.P. et al. (2025) Regeneration or risk? A narrative review of BPC‑157. Current Reviews in Musculoskeletal Medicine

    Tavares, A.B. et al. (2013) Effects of growth hormone administration on muscle strength. International Journal of Endocrinology

    Vasireddi, S. et al. (2025) Systematic review of BPC‑157 for orthopaedic applications. American Journal of Sports Medicine

  • Multi-Ingredient Pre-Workout Supplements: What Does the Science Actually Say?

    Introduction

    Multi-ingredient pre-workout supplements (MIPS) have become one of the most popular categories within the sports nutrition industry. Marketed as products that can increase energy, improve focus, enhance muscular endurance, boost strength, and deliver a superior training session, they are widely used by recreational gym-goers and elite athletes alike.

    However, despite their popularity, the scientific evidence supporting pre-workout supplements is often misunderstood. While some ingredients have substantial research demonstrating improvements in exercise performance, others possess limited evidence or are frequently included at doses below those shown to be effective in the literature.

    Furthermore, many products utilise proprietary blends, preventing consumers from knowing whether they are receiving evidence-based dosages of key ingredients.

    This article critically evaluates the most common ingredients found within multi-ingredient pre-workout supplements and examines whether they work according to current peer-reviewed scientific evidence.

    What Are Multi-Ingredient Pre-Workout Supplements?

    Multi-ingredient pre-workout supplements are formulations designed to be consumed before exercise and typically contain a combination of:

    • Stimulants
    • Amino acids
    • Ergogenic aids
    • Nootropics
    • Vitamins and minerals

    The rationale behind these products is that combining multiple ingredients may produce synergistic effects that enhance both physical and cognitive performance.

    Research suggests that some MIPS can improve training volume, muscular endurance, anaerobic performance and subjective feelings of energy (Jagim et al., 2019). However, many of these benefits appear to be driven primarily by a small number of evidence-based ingredients.

    Caffeine

    What is it?

    Caffeine is a naturally occurring stimulant found in coffee, tea, cocoa and numerous sports supplements.

    Does it work?

    Yes.

    Caffeine is arguably the most effective acute ergogenic aid available to athletes. Numerous systematic reviews and meta-analyses have demonstrated improvements in:

    • Strength
    • Power output
    • Muscular endurance
    • Sprint performance
    • Endurance performance
    • Cognitive function
    • Alertness and reaction time

    Caffeine acts primarily through antagonism of adenosine receptors within the central nervous system, reducing perceptions of fatigue and increasing alertness (Guest et al., 2021).

    A meta-analysis by Grgic et al. (2020) concluded that caffeine supplementation significantly improves maximal strength and muscular power across a range of exercise modalities.

    Effective Dose

    Current recommendations suggest:

    3–6 mg·kg⁻¹ body mass

    Consumed approximately 30–60 minutes before exercise (Guest et al., 2021).

    For a 75 kg athlete this equates to approximately 225–450 mg of caffeine.

    Verdict

    ★★★★★

    Strong evidence.

    If a pre-workout supplement improves performance acutely, caffeine is often the primary reason.

    Beta-Alanine

    What is it?

    Beta-alanine is a non-essential amino acid that increases intramuscular carnosine concentrations.

    Carnosine acts as an intracellular buffer, helping to reduce the accumulation of hydrogen ions during intense exercise.

    Does it work?

    Yes, but not immediately.

    Unlike caffeine, beta-alanine does not provide an acute performance benefit following a single serving. Instead, benefits occur following chronic supplementation over several weeks.

    Research suggests improvements in exercise lasting approximately 60–240 seconds, where metabolic acidosis contributes to fatigue (Saunders et al., 2017).

    The tingling sensation commonly associated with beta-alanine supplementation (paresthesia) is harmless but unrelated to performance enhancement.

    Effective Dose

    3.2–6.4 g per day

    For at least 4–8 weeks (Trexler et al., 2015).

    Verdict

    ★★★★☆

    Strong evidence for chronic use.

    Less relevant as an acute pre-workout ingredient.

    Citrulline Malate

    What is it?

    Citrulline is a non-essential amino acid involved in nitric oxide production.

    Nitric oxide promotes vasodilation, potentially increasing blood flow and nutrient delivery to working muscles.

    Does it work?

    Current evidence suggests that citrulline supplementation can:

    • Increase training volume
    • Reduce perceived fatigue
    • Improve muscular endurance
    • Enhance recovery between repeated efforts

    A systematic review by Trexler et al. (2019) reported that citrulline may improve resistance training performance, particularly during higher-volume sessions.

    Effective Dose

    6–8 g citrulline malate

    or

    6 g L-citrulline

    Consumed approximately 60 minutes before exercise.

    Common Problem

    Many commercial pre-workout products contain substantially less than the recommended dosage, limiting the likelihood of meaningful physiological benefits.

    Verdict

    ★★★★☆

    Good evidence when adequately dosed.

    Creatine Monohydrate

    What is it?

    Creatine is a naturally occurring compound stored within skeletal muscle as phosphocreatine.

    Its primary role is to facilitate rapid ATP regeneration during high-intensity exercise.

    Does it work?

    Absolutely.

    Creatine is one of the most extensively researched sports supplements available and consistently demonstrates improvements in:

    • Strength
    • Power
    • Sprint performance
    • Lean mass gains
    • Training adaptations

    A comprehensive review by Kreider et al. (2022) concluded that creatine remains one of the safest and most effective nutritional supplements for improving exercise capacity and increasing lean tissue mass.

    Effective Dose

    3–5 g daily

    Timing is considerably less important than consistent daily consumption.

    Verdict

    ★★★★★

    Exceptional evidence.

    One of the few supplements that consistently improves training adaptations.

    Betaine

    What is it?

    Betaine (trimethylglycine) is a naturally occurring compound found in foods such as beetroot and spinach.

    It functions as an osmolyte and methyl donor within the body.

    Does it work?

    Research remains mixed.

    Some studies have demonstrated improvements in:

    • Muscular endurance
    • Power production
    • Training volume

    However, evidence remains less consistent than that supporting caffeine or creatine.

    Effective Dose

    Approximately 2.5 g daily.

    Verdict

    ★★★☆☆

    Promising but requires further investigation.

    Taurine

    What is it?

    Taurine is an amino acid involved in numerous physiological processes including:

    • Muscle contraction
    • Calcium regulation
    • Cellular hydration
    • Antioxidant defence

    Does it work?

    Evidence suggests taurine may improve endurance performance and reduce fatigue under certain conditions.

    However, findings remain inconsistent and effects appear relatively modest compared with caffeine or creatine.

    Effective Dose

    1–3 g prior to exercise.

    Verdict

    ★★★☆☆

    Potentially beneficial but not a primary performance enhancer.

    L-Tyrosine

    What is it?

    Tyrosine is a precursor for dopamine, adrenaline and noradrenaline.

    It is often included in pre-workout supplements to improve focus and cognitive performance.

    Does it work?

    Tyrosine appears most effective during situations involving:

    • Mental fatigue
    • Sleep deprivation
    • Psychological stress

    Evidence supporting direct improvements in physical performance is limited.

    Effective Dose

    500–2000 mg pre-exercise.

    Verdict

    ★★★☆☆

    May support cognitive performance rather than physical performance.

    B Vitamins

    What are they?

    Many pre-workout supplements contain large doses of:

    • Vitamin B6
    • Vitamin B12
    • Niacin
    • Riboflavin

    Manufacturers often market these ingredients as “energy boosters.”

    Do they work?

    Not in individuals who are already meeting nutritional requirements.

    B vitamins play essential roles in energy metabolism, but supplementation beyond physiological requirements does not appear to enhance exercise performance in healthy individuals.

    Verdict

    ★★☆☆☆

    Important for health but unlikely to improve performance unless a deficiency exists.

    The Problem with Proprietary Blends

    One of the greatest concerns surrounding many commercial pre-workout supplements is the use of proprietary blends.

    These blends allow manufacturers to disclose the total weight of a mixture without revealing individual ingredient quantities.

    Consequently, consumers cannot determine whether evidence-based dosages are present.

    Research analysing commercially available pre-workout supplements found that many ingredients are under-dosed relative to scientifically supported recommendations (Jagim et al., 2019).

    When selecting a pre-workout supplement, transparency is often a positive indicator of product quality.

    Should Athletes Use Pre-Workout Supplements?

    For athletes, context is critical.

    Before Strength Training

    A caffeine-containing pre-workout may improve:

    • Training quality
    • Power output
    • Resistance training performance

    Before Technical Training

    Benefits may be smaller, particularly if training intensity is moderate.

    Before Matches

    Caffeine can enhance performance, but individual tolerance must be assessed carefully.

    Potential drawbacks include:

    • Gastrointestinal discomfort
    • Increased anxiety
    • Sleep disruption following evening fixtures

    For many players, targeted caffeine supplementation may be more appropriate than a highly stimulant-based pre-workout product.

    Practical Recommendations

    When evaluating a pre-workout supplement, look for:

    Ingredient

    Evidence-Based Dose

    Caffeine

    3–6 mg·kg⁻¹

    Creatine Monohydrate

    3–5 g daily

    Beta-Alanine

    3.2–6.4 g daily

    Citrulline Malate

    6–8 g

    Betaine

    2.5 g

    Taurine

    1–3 g

    Be cautious if:

    • Ingredient amounts are hidden
    • Proprietary blends dominate the label
    • Marketing claims exceed the available scientific evidence

    Conclusion

    Multi-ingredient pre-workout supplements can improve exercise performance, but their effectiveness depends largely on the ingredients and dosages they contain.

    The strongest evidence supports caffeine, creatine monohydrate, beta-alanine and citrulline. These ingredients have consistently demonstrated meaningful performance benefits within peer-reviewed research.

    Many other ingredients commonly found in pre-workout supplements show promise, but currently possess weaker evidence bases.

    Rather than selecting a product based on marketing claims, athletes should evaluate supplements according to transparent labelling and evidence-based dosing strategies.

    Ultimately, no pre-workout supplement can compensate for poor nutrition, inadequate sleep, or suboptimal training. Supplements should enhance an already robust performance programme rather than serve as its foundation.

    References

    Grgic, J., Trexler, E.T., Lazinica, B. and Pedisic, Z. (2020) ‘Effects of caffeine intake on muscle strength and power: A systematic review and meta-analysis’, Journal of the International Society of Sports Nutrition, 17(1), pp. 1–10.

    Guest, N.S., VanDusseldorp, T.A., Nelson, M.T., Grgic, J., Schoenfeld, B.J., Jenkins, N.D.M., Arent, S.M., Antonio, J., Stout, J.R., Trexler, E.T. and Smith-Ryan, A.E. (2021) ‘International Society of Sports Nutrition Position Stand: Caffeine and Exercise Performance’, Journal of the International Society of Sports Nutrition, 18(1), pp. 1–37.

    Jagim, A.R., Harty, P.S., Camic, C.L. and Kerksick, C.M. (2019) ‘Common ingredient profiles of multi-ingredient pre-workout supplements’, Nutrients, 11(2), pp. 254–266.

    Kreider, R.B., Kalman, D.S., Antonio, J., Ziegenfuss, T.N., Wildman, R., Collins, R., Candow, D.G., Kleiner, S.M., Almada, A.L. and Lopez, H.L. (2022) ‘International Society of Sports Nutrition Position Stand: Safety and efficacy of creatine supplementation in exercise, sport and medicine’, Journal of the International Society of Sports Nutrition, 19(1), pp. 1–46.

    Saunders, B., Elliott-Sale, K., Artioli, G.G., Swinton, P.A., Dolan, E., Roschel, H., Sale, C. and Gualano, B. (2017) ‘β-Alanine supplementation to improve exercise capacity and performance: A systematic review and meta-analysis’, British Journal of Sports Medicine, 51(8), pp. 658–669.

    Trexler, E.T., Smith-Ryan, A.E., Stout, J.R., Hoffman, J.R., Wilborn, C.D., Sale, C., Kreider, R.B., Jäger, R., Earnest, C.P., Bannock, L. and Campbell, B.I. (2015) ‘International Society of Sports Nutrition Position Stand: Beta-Alanine’, Journal of the International Society of Sports Nutrition, 12(30), pp. 1–14.

    Trexler, E.T., Keith, D.S. and Smith-Ryan, A.E. (2019) ‘Citrulline supplementation and exercise performance: A systematic review and meta-analysis’, Journal of Strength and Conditioning Research, 33(12), pp. 3574–3586.