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The Quantification of Exercise Energy Expenditure During Traditional Strength Training and Strength Endurance Training in Well-Trained Females

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Dissertation embargoed until 13th February 2026(1.46 MB)

Date

2022

Authors

Supervisor

Kilding, Andrew

Item type

Dissertation

Degree name

Master of Sport, Exercise and Health

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Publisher

Auckland University of Technology

Abstract

Introduction: Energy balance (EB) is important for optimal health and performance. Given the high prevalence of energy deficiencies reported among female athletes in high-performance sport, it is essential that an accurate measurement of exercise energy expenditure (EEE) is attained, as an inaccurate assessment of energy availability (EA) could lead to poorer health outcomes associated with low energy availability (LEA) and the relative energy deficiency in sport syndrome. Despite resistance training (RT) being an integral component of most athletes’ training regimes, little is known about the caloric cost of resistance exercise (RE), particularly in female athletes. Aims: To (1) quantify and compare the energy demands of traditional strength training and strength endurance training in well-trained females; (2) compare physiological responses during traditional strength training and strength endurance training in well-trained females; (3) determine excess post-exercise oxygen consumption (EPOC) of strength training and strength endurance training in well-trained females; and (4) develop an EA prediction equation for a practical assessment of EEE during traditional strength training and strength endurance training in well-trained females. Methods: An acute 2x2 crossover design was adopted in which nine healthy well-trained females participated in the study during the early follicular phase of their menstrual cycle. During the first visit, participants completed measures of resting energy expenditure (REE) and 1 repetition maximum (1-RM) strength tests. On visits two and three, either a supervised strength (5-RM) or strength endurance (15-RM) session was performed during which EEE was determined using indirect calorimetry and blood lactate measurements. Thereafter, participants were immediately seated for up to 45 minutes to determine EPOC. The strength and strength endurance sessions were statistically compared, and linear regression was used to develop a prediction equation model to estimate the caloric cost of both RE sessions. Results: Exercise energy expenditure was significantly greater (37.1%) during the strength endurance session (310.3 ± 43.9 kcal) compared to the strength session (213.3 ± 23.6 kcal; t = [-5.838], p = < .001, g = 2.62). Similarly, when EEE was expressed in kcal.min-1, the strength endurance session (7.8 ± 1.2 kcal.min-1) was 45.7% greater than the strength session (4.9 ± 0.7 kcal.min-1; t = [-6.293], p = < .001, g = 2.83). Measurements of EPOC indicated that oxygen consumption returned to resting levels faster after the strength session compared to the strength endurance session (11.1 ± 4.1 mins versus 20.6 ± 10.4 mins; t = [-2.537), p = .02, g = 1.139). The two derived kcal linear regression equations for the strength session and strength endurance session were as follows: strength EEE (kcal) = (workload * -0.013) + (session duration * -8.981) + (SRPE * -1.172) + 512.05; strength endurance EEE (kcal) = (workload * 0.071) + (session duration * 4.936) + (SRPE * -0.108) + 44.3. Unfortunately, the prediction models did not provide sufficient accuracy in estimating EEE for either the strength session (R2 = 0.54, F = 1.930, p = .243) or strength endurance session (R2 = 0.16, F = 0.312, p = .816). Conclusion: Given the importance of EB for optimal health and performance, these findings may have important implications for quantifying EB and EA in females. Specifically, this data can be used to inform practitioners of the EEE values associated with RT in females and inform nutritional requirements post-RE to ensure an athlete’s dietary intake corresponds to the energy demands of exercise. While we recognise the limitations of our prediction models, we would recommend practitioners use the appropriate kcal.min-1 data to estimate EEE during RE, as this will allow for a more detailed and accurate EA assessment.

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https://hdl.handle.net/10292/15675

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Masters Dissertations