Training load quantification in elite swimmers using a modified version of the training impulse method

García-Ramos, Amador; Feriche, Belén; Calderón, Carmen; Iglesias, Xavier; Barrero, Anna; Chaverri, Diego; Schuller, Thorsten; Rodríguez, Ferrán A.

doi:10.1080/17461391.2014.922621

Cited by 32 publications

(29 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is possible that shorter or longer distances could limit its attainment, despite the extremely fast VO 2 kinetics of swimmers, as discussed below. In this study, no differences were noted between VO 2peak (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) at 200TT and 400TT tests (3 194 ± 706 vs. 3 245 ± 651; p = 0.62), suggesting that both distances yield the same VO 2peak in competitive swimmers.…”

Section: Methodscontrasting

confidence: 54%

“…As shown in ▶ Table 4, the semilogarithmic BE procedures did not differ from the criterion, with the exceptions of LOG (20) No differences were noted between the criterion VO 2peak values and those estimated using the HR/VO 2 modelling procedure (▶Table 5), and there was slightly lower bias and better predictive capacity shown by VO 2peak (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), in which the first 5 s after the cessation of exercise were excluded in the estimation (mean ∆ = 0.1 and 1.6 % for 200TT and 400TT, respectively). ▶ Fig.…”

Section: Series Bmentioning

confidence: 94%

“…None of the estimated values differed from the criterion. However, the best estimates of the criterion values were provided by both modelling procedures, e. g., pVO 2peak (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and pVO 2peak (0-20), which showed almost perfect correlation with the criterion values (r 2 > 0.84) and the lowest mean differences (mean ∆ < 1.6 %) and had a low estimation bias (SE E = 7 %). Linear BE methods overestimated the criterion values by 7.6-13.3 %, on average, whereas pVO 2peak (0-20) underestimated the measured values by 3.4 %.…”

Section: Series Amentioning

confidence: 94%

“…Peak VO 2 measured during exercise (200SS) and estimated from postexercise measurements by the HR/ modelling procedure following the same test (SS200) and after a 400-m unimpeded swim (TT400). pVo 2peak (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) .…”

Section: Series Bmentioning

confidence: 99%

See 3 more Smart Citations

Validity of Postexercise Measurements to Estimate Peak VO2 in 200-m and 400-m Maximal Swims

et al. 2017

Self Cite

View full text Add to dashboard Cite

To assess the validity of postexercise measurements to estimate oxygen uptake (V˙O) during swimming, we compared V˙O measured directly during an all-out 200-m swim with measurements estimated during 200-m and 400-m maximal tests using several methods, including a recent heart rate (HR)/V˙O modelling procedure. 25 elite swimmers performed a 200-m maximal swim where V˙O was measured using a swimming snorkel connected to a gas analyzer. The criterion variable was V˙O in the last 20 s of effort, which was compared with the following V˙O estimates: 1) first 20-s average; 2) linear backward extrapolation (BE) of the first 20 and 30 s, 3×20-s, 4×20-s, and 3×20-s or 4×20-s averages; 3) semilogarithmic BE at the same intervals; and 4) predicted V˙O using mathematical modelling of 0-20 s and 5-20 s during recovery. In 2 series of experiments, both of the HR/V˙O modelled values most accurately predicted the V˙O (mean ∆=0.1-1.6%). The BE methods overestimated the criterion values by 4-14%, and the single 20-s measurement technique yielded an underestimation of 3.4%. Our results confirm that the HR/V˙O modelling technique, used over a maximal 200-m or 400-m swim, is a valid and accurate procedure for assessing cardiorespiratory and metabolic fitness in competitive swimmers.

show abstract

Section: Methodscontrasting

confidence: 54%

Section: Series Bmentioning

confidence: 94%

Section: Series Amentioning

confidence: 94%

Section: Series Bmentioning

confidence: 99%

See 2 more Smart Citations

Validity of Postexercise Measurements to Estimate Peak VO2 in 200-m and 400-m Maximal Swims

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…The exercise load EL (a.u.) was calculated based on the "training impulse" method TRIMP for monitoring exercise load [35] by means of different updated procedures [36]. The fatigue interval FI (s) was calculated as the time elapsed since the start of the race up to a given segment.…”

Section: Exercise Load Characterization During the Racementioning

confidence: 99%

Assessment of Heart Rate Variability during an Endurance Mountain Trail Race by Multi-Scale Entropy Analysis

Vallverdú

Ruiz-Muñoz

Roca

et al. 2017

Entropy

View full text Add to dashboard Cite

Abstract:The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports.

show abstract

Relationship between physical activity and biomarkers of pathology and neuroinflammation in preclinical autosomal‐dominant Alzheimer's disease

Guzmán‐Vélez,

Rivera‐Hernández,

Fabrega

et al. 2024

A&D Transl Res & Clin Interv

View full text Add to dashboard Cite

ObjectivePhysical activity (PA) has been linked to reduced Alzheimer's disease (AD) risk. However, less is known about its effects in the AD preclinical stage. We aimed to investigate whether greater PA was associated with lower plasma biomarkers of AD pathology, neural injury, reactive astrocytes, and better cognition in individuals with autosomal‐dominant AD due to the presenilin‐1 E280A mutation who are virtually guaranteed to develop dementia.MethodsTwenty‐eight cognitively unimpaired mutation carriers (ages x̄ = 29.28) wore a FitBit Charge‐4 for 14 days. We calculated their average steps to measure locomotion, and Training Impulse (TRIMP) to quantify the intensity and duration of PAs using heart rate. Plasma amyloid beta 42/40 ratio, phosphorylated tau 181, neurofilament light chain, and glial fibrillary acidic protein (GFAP) were measured. Cognition was assessed with the Consortium to Establish a Registry for Alzheimer's Disease word list learning and delayed recall, Trail Making Test Part A, and Wechsler Adult Intelligence Scale‐version IV Digit Span Backward. We conducted multiple linear regressions controlling for age, sex, body mass index, and education.ResultsThere were no associations among steps or TRIMP with plasma biomarkers or cognition. Greater TRIMP was related to higher GFAP levels.ConclusionsPA was not associated with cognition or plasma biomarkers. However, greater intensity and duration of PAs were related to higher GFAP. Participants engaged very little in moderate to vigorous PA. Therefore, light PA may not exert a significant protective effect in preclinical AD. Future work with larger samples and longitudinal data is needed to elucidate further the potential impact of PA on AD progression in the preclinical stages.Highlights Locomotion (average steps) was not associated with plasma biomarkers or cognition. Greater training load (training impulse) was related to higher glial fibrillary acidic protein levels in mutation carriers. Light physical activity may not suffice to exert a protective effect on Alzheimer's disease.

show abstract

Training load quantification in elite swimmers using a modified version of the training impulse method

Cited by 32 publications

References 40 publications

Validity of Postexercise Measurements to Estimate Peak VO2 in 200-m and 400-m Maximal Swims

Validity of Postexercise Measurements to Estimate Peak VO2 in 200-m and 400-m Maximal Swims

Assessment of Heart Rate Variability during an Endurance Mountain Trail Race by Multi-Scale Entropy Analysis

Relationship between physical activity and biomarkers of pathology and neuroinflammation in preclinical autosomal‐dominant Alzheimer's disease

Contact Info

Product

Resources

About