This investigation aimed to assess whether the alternative method of estimating
the maximal accumulated oxygen deficit (MAODalt) can detect changes
in energy system contribution in different substrate availabilities. Following a
graded exercise test to determine maximal oxygen uptake intensity
(iVO2max), 26 recreational runners performed a time to exhaustion
effort (TTE) as baseline at 110% iVO2max. The same TTE was performed
in fasting state, then, a muscle glycogen depletion protocol was executed.
Subsequently, participants received a low-carbohydrate diet and beverages
containing high (H-CHO, 10.8±2.1 g·kg− 1), moderate (M-CHO,
5.6±1.1 g·kg− 1), or zero (Z-CHO, 0.24±0.05 g·kg− 1)
carbohydrates. Another TTE was performed 24 h later. Each energy system
contribution was assessed. Generalized linear mixed models were used for
statistical analysis (p<0.05). H-CHO increased relative anaerobic capacity
(slope effect [baseline –intervention]x[H-CHO – M-CHO]) due to the relative
lactic contribution maintenance (slope effect [baseline – intervention]x[H-CHO –
Z-CHO] or [H-CHO – M-CHO]) and increase in relative alactic contribution
(6.3±3.5 kJ·min− 1). The aerobic contribution was lower
(− 8.7±4.0 kJ·min− 1), decreasing performance (− 34±16 s) for
H-CHO. M-CHO and Z-CHO maintained anaerobic capacity due to increase in alactic
contribution (slope effect [fasting – intervention]x[M-CHO – H-CHO]; and Z-CHO
was 7.3±3.4 kJ·min− 1 higher than baseline). Fasting increased
relative alactic (2.9±1.7 kJ·min− 1) but decreased aerobic
contribution (− 3.3±2.3 kJ·min− 1), impairing performance
(− 17±12 s). In conclusion, MAODalt can detect changes in energy
system supply in different nutritional states. Therefore, participantʼs
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