Nutritional Assessment describes the tools used to assess an athlete, in order develop an appropriate nutrition plan.
To know what tools may be useful for the Functional Fitness athlete we need to start with an understanding of the exercise physiology needed for the sport. There is limited direct research on the exercise physiology of Functional Fitness / CrossFit®. However, based on the research that does exist in this sport, as well as in the individual disciplines it incorporates, it is possible to hypothesise that the broad capacities outlined in Figure 1 are required (CrossFit® Mainsite, 2002; Maughan and Gleeson, 2010; JTS Strength 2012; Neto and Kennedy, 2019).
Figure 1: A hypothetical model of the exercise physiology required for Functional Fitness. Performance depends on total work capacity over time domains ranging from 1 second to over 30 minutes, and therefore high anaerobic and aerobic energetic capacity. It requires high force generating capacity to lift heavy loads, relative to bodyweight to enable completion of bodyweight movements. Neuromuscular coordination is also expected to be important, to complete complex Olympic lifts and gymnastics, for example.
These physical and metabolic requirements directly impact the nutritional requirements of the athlete. In addition, nutritional requirements are impacted by the athlete’s training load and intensity, lifestyle factors outside of their sport, their physiology, age, gender, and their goals (American Dietetic Association, 2009; Burke et al., 2011; Kerksick et al., 2018). If information on these variables is available for a given athlete, the more likely it is that nutritional guidance can be appropriately tailored to their needs.
Considering the physical requirements of the sport. Through testing an athlete’s physical capacity in the areas outlined in Figure 1, the relative strengths and weaknesses in the athlete’s capacity can be identified (Mendel and Cheatham, 2008, p160). Training and nutritional strategies can then be designed to address the weaknesses and support the strengths, with the goal of improving overall performance.
Although not under standardized conditions, athletes typically test their strength (force generating capacity) periodically within their training cycles (JTS Strength 2012; Comptrain 2020). For example, testing their 1, 3 and 5 repetition maximums for barbell lifts, e.g. the squat. This provides an indication of whether they are improving as expected over time. Improvements in ‘fitness’, incorporating energetic capacity, are inferred from performance in benchmark workouts. However, these are not direct tests of aerobic and anaerobic capacity. Moreover, improvements may come from improvements in other elements of fitness (Bellar et al., 2015; Butcher et al., 2015). For example, the benchmark workout ‘Grace’ is 30 clean and jerks at 60kg (males) for time. Improvements in strength and / or anaerobic capacity may support improvements in Grace performance. Competitive athletes may therefore benefit from direct testing of aerobic and anaerobic capacity. Aerobic capacity can be inferred using the VO2max test (Standard Operating Procedure included in following section) and lactate threshold, and anaerobic capacity can be inferred from tests including the Wingate Anaerobic Test (WAnT) (Mendel and Cheatham, 2008, p159). These tests are non-invasive, reliable and have been independently validated (Bruce et al., 1963; Mendel and Cheatham, 2008, p159). They are also typically conducted on treadmills and / or stationary bikes, which are equipment familiar to a typical functional fitness athlete. This reduces error from unfamiliarity with equipment or of required muscle groups (Hibbert et al., 2017). However, they require specialist equipment, for example breath-by-breath gas analyzers, and therefore must be carried out in specific laboratory or clinical settings. This is likely to make them inaccessible to many recreational athletes. Professional athletes may feel the time and financial cost is worthwhile as a competition may be won or lost by completing just a single movement more than your closest competitor.
Functional Fitness is not a weight class sport, however athletes aim to optimise their strength to bodyweight ratio (McArdle, Katch and Katch, 2015, p766). This is so they can lift maximal external loads, whilst still moving their bodyweight with speed and agility in complex gymnastic movements, for example the muscle up, and repeated explosive bodyweight movements, such as the burpee or box jump (CrossFit® Mainsite, 2002). Body composition is therefore relevant to the athlete. Athletes seek to increase functional muscle mass whilst maintaining relatively low body fat, particularly for competition, as body fat is non-functional weight that must be carried in bodyweight movements (Burke, 2007, p111; McArdle, Katch and Katch, 2015, p766). Having said this, Functional Fitness is metabolically and mechanically demanding and maintaining very low levels of body fat, close to essential levels, may increase the risk the athlete spends extended periods with low energy availability (Mountjoy et al., 2018). The impact this may have on muscle maintenance, immune tolerance, bone health may have adverse impacts on short and long term health and performance (Mountjoy et al., 2018). Estimates of body fat percentage may therefore be useful to determine whether an athlete’s performance may benefit from nutritional changes that support changes in body composition.
There are multiple techniques that can be used to estimate body fat percentage. Laboratory techniques such as dual-energy X-ray absorptiometry (DEXA) scans, Bioelectrical Impedance Spectroscopy and BodPod require specialist equipment available in limited locations (Duren et al., 2008). This may make them inaccessible to many athletes. In contrast, a technique such as skinfold assessments using calipers is low cost, portable, and quick making it easier to apply in the field (Duren et al., 2008). Skinfold assessments assume that body fat percentage can be inferred by the thickness of the subcutaneous fat layer (Wagner and Heyward, 1999). This is not entirely accurate, as body fat is also determined by other fat stores, for example intraabdominal fat (Wagner and Heyward, 1999). Research studies indicate that the absolute body fat estimate in athletes typically differs from that estimated by other techniques that are considered more reliable, for example DEXA of bioelectrical impedance analysis, although they are typically correlated (Ostojic, 2006; Knechtle et al., 2011; Ploudre et al., 2018). However, if a consistent technique is applied the results are reproducible and may therefore be used to infer changes in body fat over time (Wagner and Heyward, 1999). Different individuals store fat differentially across the body and therefore to increase the accuracy of the results it is important to take measures from multiple sites; common sites include the subscapular, iliac, bicep, tricep and thigh (Wagner and Heyward, 1999). Regression equations to infer body fat percentage from the skinfold measures are population specific, and therefore it is important to select an appropriate equation (population) for each athlete (Wagner and Heyward, 1999).
When developing nutritional guidance, it is important to understand an athlete’s current diet and their current nutritional knowledge (Burke, 2015). This may help identify why they are struggling in a certain area, for example in changing their body composition. It can also inform the nutritional changes that can be implemented, and how quickly. As an extreme example, if an athlete’s diet is considered highly mismatched to their needs they may struggle to understand or implement the multiple changes recommended all at once. They may also not trust that all the changes are necessary prior to experiencing any benefit from them, if no prior relationship has been built (Street et al., 2003). By understanding their current diet a stepwise plan can be developed to gradually change the athlete’s diet over time.
Information on an athlete’s diet may be collected using a variety of methods, including a 24-hour dietary recall, 3- or 5-day food diary and / or a food frequency questionnaire (Burke, 2015). For an athlete, a 3- or 5-day food diary may be the most appropriate as training load and lifestyle may differ between days, and a food frequency questionnaire may not give insight into nutrient timing (Schlundt, 1988; Burke, 2015). Nutrient timing can be important to performance (Kerksick et al., 2017). The utility of a food diary for developing nutritional recommendations depends on its completeness and the accuracy with which an individual records their daily food intake (Starfield et al., 1981; Stewart et al., 1999). Research suggests, particularly in those new to tracking food intake, recorded values may not be an accurate representation of food consumed because not all food is logged and / or portion sizes are in accurate; the analysis performed in one systematic review suggested up to 30% of individuals may underreport food intake when recording a food diary (Vuckovic et al., 2000; Poslusna et al., 2009). However, even if incomplete, they provide information on an athlete’s preferences, budget, cooking ability – all of which are relevant to providing practical guidance the athlete will implement (Burke, 2015). Recording a food diary is also time consuming and therefore it may be impractical for some individuals (Burke, 2015); in this case, a 24 hour recall may be more appropriate.
A comparison of an athlete’s food diary to estimated requirements can be performed manually, or with the aid of nutritional analysis software such as Nutritics®. Again, the utility of the analysis depends on the accuracy of the input food diary data. In addition, for athletes, requirements may differ to the standard entries within software such as Nutritics® (American Dietetic Association, 2009; Burke et al., 2011; Aragon et al., 2017; Kerksick et al., 2018). Although it must be noted that for certain micronutrients, it is not clear whether athlete requirements differ to the general population or to what extent (Burke, 2001; Kerksick et al., 2018; Sale and Elliot-Sale, 2019; Sim et al., 2019). A blood test can indicate specific micronutrient deficiencies, for example vitamin D or iron deficiency, and may be used to further inform nutritional guidance. A blood test is an invasive procedure and may not be accessible by every athlete’s healthcare provider. As such, it may be advisable to undertake such a test if the athlete shows signs of deficiency, for example if an athlete shows signs of anaemia it may indicate an iron deficiency (Sim et al., 2019).
In summary, a variety of nutritional assessment tools may be of utility in supporting Functional Fitness athletes. Each has strengths and limitations, and their use must be considered in relation to the specific requirements of the athlete and their personal circumstances.
American Dietetic Association, Dietitians of Canada, American college of Sports Medicine, Rodriguez, N.R., Di Marco, N.M. and Langley, S. (2009). American College of Sports Medicine position stand. Nutrition and athletic performance. Medicine and Science in Sports & Exercise, 41 (3), 709-731.
Aragon, A.A., Schoenfeld, B.J., Wildman, R., Kleiner, S., Van Dusseldorp, T., Taylor, L., Earnest, C.P., Arciero, P.J., Wilborn, C., Kalman, D.S., Stout, J.R., Willoughby, D.S., Campbell, B., Arent, S.M., Bannock, L., Smith-Ryan, A.E. and Antonio, J. (2017). International society of sports nutrition position stand: diets and body composition. Journal of the International Society of Sports Nutrition, 14, 16. Available from https://doi.org/10.1186/s12970-017-0174-y [accessed 1st October 2018].
Bellar, D., Hatchett, A., Judge, L.W., Breaux, M.E. and Marcus, L. (2015). The relationship of aerobic capacity, anaerobic peak power and experience to performance in CrossFit® exercise. Biology of Sport, 32 (4), 315-320.
Borg, G.A. (1974). Perceived exertion. Exercise and Sports Sciences Reviews, 2, 131-153.
Burke, L.M. (2001). Energy needs of athletes. Canadian Journal of Applied Physiology, 26 (S1), S202 – S219.
Burke, L.M. (2007). Middle- and Long- Distance Running. In: Burke, L.M. (ed.). Practical Sports Nutrition, 1st ed. Leeds: Human Kinetics, p111.
Burke, L.M. (2015). Dietary assessment methods for the athlete: pros and cons of different methods. Sports Science Exchange, 28 (150), 1 – 6.
Bruce, R.A., Blackmon, J.R., Jones, J.W. and Strait, G. (1963). Exercise testing in adult normal subjects and cardiac patients. Pediatrics, 32, 742 – 756.
Burke, L.M., Hawley, J.A., Wong, S.H.S. and Jeukendrup, A.E. (2011). Carbohydrates for training and competition. Journal of Sports Sciences, 29 (sup 1), S17-S27. Available from https://doi.org/10.1080/02640414.2011.585473 [accessed 1st October 2018].
Butcher, S.J., Neyedly, T.J., Horvey, K.J. and Benko, C.R. (2015). Do physiological measures predict selected CrossFit® benchmark performance? Journal of Sports Medicine, 6, 241-247. Available from https://doi.org/10.2147/OAJSM.S88265 [accessed 1st October 2018].
Comptrain (2020). Pro Comptrain. Available from https://comptrain.co/pros/ [accessed 1st March 2020].
Cooper Institute for Aerobics Research (1998). The Physical Fitness Specialist Certification Manual. In: Heyward, V. (ed.). Advance Fitness Assessment & Exercise Prescription, 3rd ed. Champaign, IL: Human Kinetics, p48.
CrossFit® Mainsite (2002). What is fitness and who is fit? CrossFit®. Available from https://journal.crossfit.com/article/what-is-fitness [accessed 1st November 2018].
Duren, D.L., Sherwood, R.J., Czerwinski, S.A., Lee, M., Choh, A.C., Siervogel, R.M., and Chumlea, W.MC. (2008). Body composition methods: comparison and interpretation. Journal of Diabetes in Science and Technology, 2 (6), 1139 – 1146.
Fox III, S.M., Naughton, J.P. and Haskell, W.L. (1971). Physical activity and the prevention of coronary heart disease. Annals of Clinical Research, 3 (6), 404 – 432.
Hibbert, A.W., Billaut, F., Varley, M.C., and Polman, C.J. (2017). Familiarization protocol influences reproducibility of 20-km cycling time-trial performance in novice participants. Frontiers in Physiology, 8, 488.
JTS Strength (2012). JTS Classic: Maximal Strength Programming for CrossFit. JTS Strength. Available from https://www.jtsstrength.com/jts-classic-maximal-strength-programming-for-crossfit/ [accessed 1st March 2020].
Kerksick, C.M., Arent, S., Schoenfeld, B.J., Stout, J.R., Campbell, B., Wilborn, C.D., Taylor, L., Kalman, D., Smith-Ryan, A.E., Kreider, R.B., Willoughby, D., Arciero, P.J., VanDusseldorp, T.A., Ormsbee, M.J., Wildman, R., Greenwood, M., Ziegenfuss, T.N., Aragon, A.A. and Antonio, J. (2017). International society of sports nutrition position stand: nutrient timing. Journal of the International Society of Sports Nutrition, 14, 33. Available from https://doi.org/10.1186/s12970-017-0189-4
[accessed 1st October 2018].
Kerksick, C.M., Wilborn, C.D., Roberts, M.D., Smith-Ryan, A., Kleiner, S.M., Jager, R., Colins, R., Cooke, M., Davis, J.N., Galvan, E., Greenwood, M., Lowery, L.M., Wildman, R., Antonio, J. and Kreider, R.B. (2018). ISSN exercise and sports nutrition review update: research & recommendations. Journal of the International Society of Sports Nutrition, 15 (1), 38. Available from https://doi.org/10.1186/s12970-018-0242-y [accessed 1st October 2018].
Knechtle, B., Wirth, A., Knechtle, P., Rosemann, T., Rust, C.A., and Bescos, R. (2011). A comparison of fat mass and skeletal muscle mass estimation in male ultra-endurance athletes using bioelectrical impedance analysis and different anthropometric methods. Nutricion Hospitalaria, 26 (6), 1420 – 1427.
Maughan, R. and Gleeson, M. (2010). All chapters. In: Maughan, R. and Gleeson, M. (eds.). The biochemical basis of sports performance, 2nd ed. New York: Oxford University Press.
McArdle, W.D., Katch, F.I. and Katch, V.l. (2015). Carbohydrates, Lipids, and Proteins. In: McArdle, W.D., Katch, F.I. and Katch, V.l. (eds.) Exercise physiology: nutrition, energy, and human performance, 8th ed. Baltimore, MD : Wolters Kluwer Health/Lippincott Williams & Wilkins, p766 – 769.
Mendel, R.W. and Cheatham, C.C. (2008). Laboratory and field techniques for measuring performance. In: Antonio, J., Kalman, D., Stout, J.R., Greenwood, M., Willoughby, D.S. and Haff, G.G. (eds.) Essentials of Sports Nutrition and Supplements, 1st ed. Totowa, NJ : Humana Press, p159 – 172.
Mountjoy, M., Sundgot-Borgen, J.K., Burke, L.M., Ackerman, K.E., Blauwet, C., Constantini, N., Lebrun, C., Lundy, B., Melin, A.K., Meyer, N.L., Sherman, R.T., Tenforde, A.S., Torstveit, M.K., and Budgett, R. (2018). IOC consensus statement on relative energy deficiency in sport (RED-S): 2018 update. British Journal of Sports, 52, 687 – 697.
Neto, J.H.K., and Kennedym M.D. (2019). The multimodal nature of high-intensity functional training potential applications to improve sports performance. Sports, 7, 33.
Ostojic, S.M. (2006). Estimation of body fat in athletes: skinfolds vs bioelectrical impedance. Journal of Sports Medicine and Physical Fitness, 46 (3), 442 – 446.
Ploudre, A., Arabas, J.L., Jorn, L., and Mayhew, J.L. (2018). Comparison of techniques for tracking body composition changes across a season in college women basketball players. International Journal of Exercise Science, 11 (4), 425 – 438.
Poslusna, K., Ruprich, J., de Vries, J.H., Jakubikova, M., and Van't Veer, P. (2009). Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. British Journal of Nutrition, 101, S73 – S85.
Sale, C., and Elliot-Sale, K. (2019). Nutrition and athlete bone health. Sports Medicine, 49 (Suppl 2), S139 – S151.
Schlundt, D.G. (1988). Accuracy and reliability of nutrient intake estimates. Journal of Nutrition, 118 (12), 1432 – 1435.
Sim, M., Garvican-Lewis, L.A., Cox, G.R., Govus, A., McKay, A.K.A., Stellingwerff, T., and Peeling, P. (2019). Iron considerations for the athlete: a narrative review. European Journal of Applied Physiology, 119 (7), 1463 – 1478.
Starfield, B., Wray, C., Hess, K., Gross, R., Birk, P.S., and D’Lugoff, B.C. (1981). The influence of patient-practitioner agreement on outcome of care. American Journal of Public Health, 71, 127 – 132.
Stewart, M., Brown, J.B., Boon, H., Galajda, J., Meredith, L. and Sangster, M. (1999). Evidence on patient-doctor communication. Cancer Prevention and Control, 3 (1), 25 – 30.
Vuckovic, N., Ritenbaugh, C., Taren, D.L., and Tobar, M. (2000). A qualitative study of participants’ experiences with dietary assessment. Journal of the American Dietetic Association, 100 (9), 1023 – 1028.
Wagner, D.R., and Heyward, V.H. (1999). Techniques of body composition assessment: a review of laboratory and field methods. Research Quarterly for Exercise and Sport, 70 (2), 135 – 149.