CC-Nutrition

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  • 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.

    REFERENCES

    Abbasi, B. et al. (2012) ‘The effect of magnesium supplementation on primary insomnia in elderly: A double-blind placebo-controlled clinical trial’, Journal of Research in Medical Sciences.


    Afaghi, A. et al. (2007) ‘High-glycemic-index carbohydrate meals and sleep onset’, American Journal of Clinical Nutrition.


    Bannai, M. and Kawai, N. (2012) ‘New therapeutic strategy for amino acids in sleep’, Journal of Pharmacological Sciences.


    Boyle, N.B. et al. (2017) ‘The effects of magnesium supplementation on subjective anxiety and stress’, Nutrients.


    Clark, I. and Landolt, H. (2017) ‘Coffee, caffeine, and sleep’, Journal of Sleep Research.


    Dattilo, M. et al. (2011) ‘Sleep and muscle recovery’, Sports Medicine.


    de Zambotti, M. et al. (2018) ‘Wearable sleep technology accuracy’, Journal of Clinical Sleep Medicine.


    Drake, C. et al. (2013) ‘Caffeine effects on sleep’, Journal of Clinical Sleep Medicine.


    Ebrahim, I.O. et al. (2013) ‘Alcohol and sleep architecture’, Alcoholism: Clinical and Experimental Research.


    Exelmans, L. and Van den Bulck, J. (2016) ‘Bedtime procrastination’, Journal of Sleep Research.


    Ferracioli-Oda, E. et al. (2013) ‘Melatonin and sleep outcomes’, PLoS One.


    Fullagar, H.H.K. et al. (2015) ‘Sleep and athletic performance’, Sports Medicine.


    Halson, S.L. (2014) ‘Sleep in elite athletes’, Sports Medicine.


    Herxheimer, A. and Petrie, K.J. (2002) ‘Melatonin for jet lag’, Cochrane Database.


    Howatson, G. et al. (2012) ‘Tart cherry juice and recovery’, Scandinavian Journal of Medicine & Science in Sports.


    Inagawa, K. et al. (2006) ‘Glycine and sleep quality’, Journal of Pharmacological Sciences.


    Irish, L.A. et al. (2015) ‘Sleep hygiene review’, Sleep Medicine Reviews.


    King, D.L. et al. (2013) ‘Gaming and sleep’, Journal of Clinical Sleep Medicine.


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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

  • Fuelling for the Finish Line: Nutrition Strategies for Marathon Success

    Running a marathon is as much a nutritional challenge as it is a physical one. Whether you’re a first-time runner or a seasoned athlete, your ability to complete 26.2 miles strongly depends on your nutrition before, during, and after the event. Scientific evidence supports targeted strategies like carbohydrate loading, glycogen sparing, optimal hydration, and post-race recovery to enhance performance and reduce fatigue. Here’s how to fuel your body like a pro.

    1. Carbohydrate Loading: Topping Up Glycogen Stores

    Carbohydrate loading is a well-established strategy used by endurance athletes to maximise glycogen storage in muscles. Glycogen is the primary fuel for prolonged moderate-to-high intensity exercise, and depletion is closely associated with fatigue and “hitting the wall” (Burke et al., 2011).

    Traditionally, athletes would taper their training while increasing carbohydrate intake to 8–12 g/kg of body weight per day in the final 2–3 days before the race (Jeukendrup & Killer, 2010). This method has been shown to improve time to exhaustion and performance in events lasting longer than 90 minutes.

    Practical tip: A 70 kg runner should aim for around 560–840g of carbohydrates per day in the 48 hours before the race. Choose high-GI foods like white rice, pasta, bananas, and sports drinks to maximise uptake.

    High-carb meal plan examples:

    • Breakfast: 2 large bagels with honey, banana, glass of orange juice (approx. 120g carbs)
    • Lunch: White pasta with tomato sauce and lean chicken, 2 slices of garlic bread, fruit smoothie (approx. 150g carbs)
    • Snacks: Rice cakes with jam, energy bars, dried mango
    • Dinner: Basmati rice with sweet potato curry, naan bread, apple crumble with custard (approx. 180g carbs)

    2. Glycogen Sparing: Training and Fueling Smarter

    Glycogen sparing refers to the body’s ability to delay the use of glycogen by increasing the use of fat as a fuel source. Training adaptations such as long runs at a lower intensity, fasted-state training, and incorporating medium-chain triglycerides (MCTs) have been explored to encourage this shift (Spriet, 2014).

    While some athletes use “train low” strategies (training with low carbohydrate availability), this should be approached with caution, as performance benefits are mixed and it may impair high-intensity training capacity (Impey et al., 2016).

    Practical tip: Including some lower-carb, aerobic base runs in your training plan may help improve fat oxidation capacity—but don’t sacrifice carbs during race week or high-intensity sessions.

    Food tips for fat-adapted sessions:

    • Train in the morning before breakfast (fasted cardio)
    • Small pre-run coffee (caffeine enhances fat oxidation—Spriet, 2014)
    • Post-run meal should include balanced carbs and protein: e.g. scrambled eggs, oats with berries, Greek yogurt.

    3. Race Day Nutrition: Fuelling Every Mile

    Pre-Race Breakfast (2.5–3 hours before)

    Should be high-carb, low-fat, moderate protein, and low in fibre.

    Examples:

    • 2 slices of white toast with jam + banana + isotonic sports drink (60–80g carbs)
    • Porridge made with milk + honey + raisins + small coffee
    • White rice with scrambled eggs and soy sauce (for savoury eaters)

    Avoid: High-fat meals (e.g. bacon, croissants), high-fibre cereals (e.g. bran flakes), or spicy foods.

    4. During the Race: Carbohydrate and Fluid Strategies

    To maintain blood glucose and delay fatigue, carbohydrate intake during the marathon is crucial. The recommended intake is 30–60g of carbohydrates per hour, and up to 90g/hour may be tolerated when multiple transportable carbohydrates (e.g., glucose + fructose) are consumed (Jeukendrup, 2014).

    Hydration is equally important. Dehydration exceeding 2% of body weight can impair performance, but overhydration may cause hyponatremia. The goal is to drink to thirst, ideally using sports drinks that supply both carbohydrates and electrolytes (Sawka et al., 2007).

    Strategy:

    • Start hydrated (urine should be pale yellow pre-race)
    • Drink small sips at water stations
    • Use electrolyte drinks if sweating heavily or conditions are hot

    Drink examples:

    • SIS GO Electrolyte
    • Nuun tablets in 500ml water
    • Coconut water with a pinch of salt and honey (DIY)

    Practical tip: Use race rehearsals to test your nutrition strategy. Opt for gels, chews, or isotonic drinks that deliver glucose and electrolytes without causing GI distress.

    5. Caffeine: A Legal Performance Booster

    Caffeine is a well-supported ergogenic aid that can improve endurance performance by reducing perceived exertion and enhancing fat oxidation (Spriet, 2014). Doses of 3–6 mg/kg body weight, consumed ~60 minutes before exercise, are considered effective.

    Food examples:

    • 1 strong coffee (~100–150mg caffeine)
    • Caffeinated gel (e.g. 75mg per gel – check label)
    • Matcha green tea shot or caffeine tablets (with caution)

    Practical tip: A 70 kg athlete may benefit from 210–420 mg of caffeine before or during the race—but individual tolerance varies, so trial it in training first. Caution: Too much may cause jitters or GI upset.

    6. Post-Marathon Recovery: Rehydrate, Rebuild, Replenish

    Recovery nutrition should focus on the three R’s:

    • Rehydrate: Replace lost fluids with water and electrolytes.
    • Replenish: Consume carbohydrates (~1.0–1.2 g/kg/hour for the first 4 hours) to restore glycogen.
    • Rebuild: Include 20–25g of high-quality protein to stimulate muscle repair (Thomas et al., 2016).

    Recovery meal/snack ideas:

    Quick snack: Chocolate milk + flapjack or sports recovery bar

    Smoothie: Banana, oats, whey protein, almond butter, milk (60g carbs, 25g protein)

    Post-race meal: Chicken wrap with hummus + sweet potato fries + fruit yogurt

    References

    • Burke, L. M., Hawley, J. A., Wong, S. H. S., & Jeukendrup, A. E. (2011). Carbohydrates for training and competition. Journal of Sports Sciences, 29(sup1), S17–S27.
    • Impey, S. G., Hearris, M. A., Hammond, K. M., Bartlett, J. D., Louis, J., Close, G. L., & Morton, J. P. (2016). Fuel for the work required: a theoretical framework for carbohydrate periodization and the glycogen threshold hypothesis. Sports Medicine, 48(5), 1031–1048.
    • Jeukendrup, A. E., & Killer, S. C. (2010). The myths surrounding pre-exercise carbohydrate feeding. International Journal of Sport Nutrition and Exercise Metabolism, 20(1), 1–7.
    • Jeukendrup, A. E. (2014). A step towards personalized sports nutrition: carbohydrate intake during exercise. Sports Medicine, 44(Suppl 1), S25–S33.
    • Sawka, M. N., Burke, L. M., Eichner, E. R., Maughan, R. J., Montain, S. J., & Stachenfeld, N. S. (2007). American College of Sports Medicine position stand. Exercise and fluid replacement. Medicine & Science in Sports & Exercise, 39(2), 377–390.
    • Spriet, L. L. (2014). Exercise and sport performance with low doses of caffeine. Sports Medicine, 44(2), 175–184.
    • Thomas, D. T., Erdman, K. A., & Burke, L. M. (2016). Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and athletic performance. Journal of the Academy of Nutrition and Dietetics, 116(3), 501–528.