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Veugelers, KR, Naughton, GA, Duncan, CS, Burgess, DJ, and Graham, SR. Validity and reliability of a submaximal intermittent running test in elite Australian football players. J Strength Cond Res 30(12): 3347–3353, 2016—The aim of this article was to determine the validity and reliability of a submaximal intermittent running (SIR) test in elite Australian rules football (ARF) players. Heart rate (HR) responses of 38 elite ARF players to both the SIR and the yo-yo intermittent recovery 2 (YYIR2) tests were compared over 2 trials. Linear regression analysis was used to examine the relationship between SIR test HR responses and YYIR2 test performance. Heart rate responses of 25 elite ARF players to the SIR test were monitored over 3 trials. Day-to-day reliability was determined using intraclass correlation coefficient (ICC), typical error of measurement, coefficient of variation (CV), and smallest worthwhile change. Large inverse correlations were reported between 2-, 3-, and 4-minute HR during the SIR test and YYIR2 test distance (r = -0.58 to -0.61, p < 0.01). Heart rate recovery after 2 and 3 minutes of the SIR test was moderately correlated to YYIR2 distance (r = 0.32–0.35, p < = 0.05). Strong correlations for ICC (r = 0.90–0.97) and low CV (1.3–9.2%) were reported for all HR variables. Monitoring HR during the SIR test is a valid and reliable indicator of YYIR2 test performance in elite ARF players. These findings support the use of the SIR test as a regular and non-fatiguing indicator of intermittent running capacity.

Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting preseason variation of match-play exercise intensity (MEI sim/min) using a variable dose–response model. Methods: A total of 21 professional male AF players completed an 18-wk preseason macrocycle. Preseason internal training load was quntified using the session rating-of-perceived-exertion method (sRPE) and external load from satellite (as distance [Dist] and high-speed distance [HS Dist]) and accelerometer (Player Load [PL]) data. Using a training-impulse (TRIMPs) calculation, external load expressed in arbitrary units was represented as TRIMPs Dist , TRIMPs HSDist , and TRIMPs PL . Preseason training load and MEI sim/min data were applied to a variable dose–response model, which provided estimates of MEI sim/min. Model estimates of MEI sim/min were correlated with actual measures from each match-play drill performed during the preseason macrocycle. Magnitude-based inferences (effect size [90% confidence interval]) were calculated to determine practical differences in the precision of MEI sim/min estimates using each of the internal- and external-load inputs. Results: Estimates of MEI sim/min demonstrated very large and large associations with actual MEI sim/min with models constructed from external and internal training inputs (r [90% confidence interval]; TRIMPs Dist .73 [.72–.74], TRIMPs PL .72 [.71–.73], and sRPE Skills .67 [.56–.78]). There were trivial differences in the precision of MEI sim/min estimates between models constructed from TRIMPs Dist and TRIMPs PL and between internal input methods. Conclusions: Variable dose-response models from multiple training-load inputs can predict the within-individual variation of MEI sim/min across an entire preseason macrocycle. Models informed by external training inputs (TRIMPs Dist and TRIMPs PL) exhibited predictive power comparable to those of sRPE Skills models. Refereed/Peer-reviewed

Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting match exercise intensity (MEI/min) and player-rank score (PRScore) using a variable dose-response model. Methods: A cohort of 25 professional AF players (23 ± 3 y, 188.3 ± 7.2 cm, 87.7 ± 8.4 kg) completed a 24-wk in-season macrocycle. In-season internal training and match load were quantified using session rating of perceived exertion (sRPE) and external load from satellite and accelerometer data. Using a training-impulse (TRIMP) calculation, external load (au) was represented as distance (TRIMPDist), distance ≥4.16 m/s (TRIMPHSDist), and PlayerLoad (TRIMPPL). In-season training load, MEI/min, and PRScore were applied to a variable dose-response model, which provided estimates of MEI/min and PRScore. Predicted MEI/min and PRScore were correlated with actual measures from each match. The magnitude of the difference between MEI/min and PRScore estimates for each model input and the difference between the precision of internal and external load measures to predict MEI/min and PRScore were calculated using the effect size ± 90% confidence interval (CI). Results: Estimates of MEI/min demonstrated very large associations with actual MEI/min (r, 90% CI) (eg, TRIMPDist .76 ± .13, and sRPESkills .73 ± .14). Estimates of PRScore demonstrated associations of large magnitude with actual PRScore using the same inputs. Precision of actual MEI/min was lowest using sRPE compared with (ES ± 90% CI) TRIMPDist, −.67 ± .34, and TRIMPPL, −.91 ± .39. There were trivial and unclear differences in the precision of PRScore estimates between TRIMP and sRPE inputs. Conclusions: Dose-response models from multiple training-load inputs can predict within-individual variation of MEI/min and PRScore. Internal and external training-input methods exhibited comparable predictive power.

The authors aimed to quantify (a) the periodization of physical loading and daily carbohydrate (CHO) intake across an in-season weekly microcycle of Australian Football and (b) the quantity and source of CHO consumed during game play and training. Physical loading (via global positioning system technology) and daily CHO intake (via a combination of 24-hr recall, food diaries, and remote food photographic method) were assessed in 42 professional male players during two weekly microcycles comprising a home and away fixture. The players also reported the source and quantity of CHO consumed during all games (n = 22 games) and on the training session completed 4 days before each game (n = 22 sessions). The total distance was greater (p p