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Franco M. Impellizzeri

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Franco M. Impellizzeri and Samuele M. Marcora

We propose that physiological and performance tests used in sport science research and professional practice should be developed following a rigorous validation process, as is done in other scientific fields, such as clinimetrics, an area of research that focuses on the quality of clinical measurement and uses methods derived from psychometrics. In this commentary, we briefly review some of the attributes that must be explored when validating a test: the conceptual model, validity, reliability, and responsiveness. Examples from the sport science literature are provided.

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Franco M. Impellizzeri, Samuele M. Marcora, and Aaron J. Coutts

Exercise is a stressor that induces various psychophysiological responses, which mediate cellular adaptations in many organ systems. To maximize this adaptive response, coaches and scientists need to control the stress applied to the athlete at the individual level. To achieve this, precise control and manipulation of the training load are required. In 2003, the authors introduced a theoretical framework to define and conceptualize the measurable constructs of the training process. They described training load as having 2 measurable components: internal and external load. The aim of this commentary is to extend, clarify, and refine both the theoretical framework and the definitions of internal and external training load to avoid misinterpretation of this concept.

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Ian Rollo, Franco M. Impellizzeri, Matteo Zago, and F. Marcello Iaia

The physical-performance profiles of subelite male footballers were monitored during 6 wk of a competitive season. The same squad of players played either 1 (1G, n = 15) or 2 (2G, n = 15) competitive matches per week. On weeks 0, 3, and 6, 48 h postmatch, players completed countermovement jump (CMJ), 10- and 20-m sprints, the Yo-Yo Intermittent Recovery Test (YYIRT), and the Recovery-Stress Questionnaire. Both groups undertook 2 weekly training sessions. The 2G showed after 6 wk lower YYIRT (–11% to 3%, 90% CI –15.8% to –6.8%; P < .001) and CMJ performances (–18.7%, –21.6 to –15.9%; P = .007) and higher 10-m (4.4%, 1.8–6.9%; P = .007) and 20-m sprints values (4.7%, 2.9% to 6.4%; P < .001). No differences were found at 3 wk (.06 < P < .99). No changes over time (.169 < P < .611) and no differences time × group interactions (.370 < P < .550) were found for stress, recovery, and the Stress Recovery Index. In conclusion players’ ability to sprint, jump, and perform repeated intense exercise was impaired when playing 2 competitive matches a week over 6 wk.

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Maurizio Fanchini, Ivan Ferraresi, Roberto Modena, Federico Schena, Aaron J. Coutts, and Franco M. Impellizzeri

Purpose:

To examine the construct validity of the session rating perceived exertion (s-RPE) assessed with the Borg CR100 scale to measure training loads in elite soccer and to examine if the CR100 is interchangeable and can provide more-accurate ratings than the CR10 scale.

Methods:

Two studies were conducted. The validity of the CR100 was determined in 19 elite soccer players (age 28 ± 6 y, height 180 ± 7 cm, body mass 77 ± 6 kg) during training sessions through correlations with the Edwards heart-rate method (study 1). The interchangeability with CR10 was assessed in 78 soccer players (age 19.3 ± 4.1 y, height 178 ± 5.9 cm, body mass 71.4 ± 6.1 kg) through the Bland–Altman method and correlations between change scores in different sessions. To examine whether the CR100 is more finely graded than the CR10, the proportions of responses corresponding to the verbal expressions were calculated (study 2).

Results:

Individual correlations between the Edwards method and s-RPE were large to very large (.52–.85). The mean difference between the 2 scales was –0.3 ± 0.33 AU (90% CI –0.41 to –0.29) with 95% limits of agreements (0.31 to –0.96 AU). Correlations between scales and between-changes scores were nearly perfect (.95 and .91–.98). Ratings corresponding to the verbal anchors were 49% in CR10 and 26% in CR100.

Conclusions:

The CR100 is valid for assessing the training load in elite soccer players. It can be used interchangeably with the CR10 and may provide more-precise measures of exercise intensity.

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Annie C. Jeffries, Lee Wallace, Aaron J. Coutts, Shaun J. McLaren, Alan McCall, and Franco M. Impellizzeri

Background: Athlete-reported outcome measures (AROMs) are frequently used in research and practice but no studies have examined their psychometric properties. Objectives: Part 1—identify the most commonly used AROMs in sport for monitoring training responses; part 2—assess risk of bias, measurement properties, and level of evidence, based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines. Study Appraisal and Synthesis Methods: Methodological quality of the studies, quality of measurement properties, and level of evidence were determined using the COSMIN checklist and criteria. Results: Part 1—from 9446 articles screened for title and abstract, 310 out of 334 full texts were included; 53.9% of the AROMs contained multiple items, while 46.1% contained single items. Part 2—from 1895 articles screened for title and abstract, 71 were selected. Most measurement properties of multiple-item AROMs were adequate, but content validity and measurement error were inadequate. With the exclusion of 2 studies examining reliability and responsiveness, no validity studies were found for single items. Conclusions: The measurement properties of multiple-item AROMs derived from psychometrics were acceptable (with the exclusion of content validity and measurement error). The single-item AROMs most frequently used in sport science have not been validated. Additionally, nonvalidated modified versions of the originally nonvalidated items are common. Until proper validation studies are completed, all conclusions based on these AROMs are questionable. Established reference methods, such as those of clinimetrics, should be used to develop and assess the validity of AROMs.

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Franco M. Impellizzeri, Matthew S. Tenan, Tom Kempton, Andrew Novak, and Aaron J. Coutts

The number of studies examining associations between training load and injury has increased exponentially. As a result, many new measures of exposure and training-load-based prognostic factors have been created. The acute:chronic workload ratio (ACWR) is the most popular. However, when recommending the manipulation of a prognostic factor in order to alter the likelihood of an event, one assumes a causal effect. This introduces a series of additional conceptual and methodological considerations that are problematic and should be considered. Because no studies have even tried to estimate causal effects properly, manipulating ACWR in practical settings in order to change injury rates remains a conjecture and an overinterpretation of the available data. Furthermore, there are known issues with the use of ratio data and unrecognized assumptions that negatively affect the ACWR metric for use as a causal prognostic factor. ACWR use in practical settings can lead to inappropriate recommendations, because its causal relation to injury has not been established, it is an inaccurate metric (failing to normalize the numerator by the denominator even when uncoupled), it has a lack of background rationale to support its causal role, it is an ambiguous metric, and it is not consistently and unidirectionally related to injury risk. Conclusion: There is no evidence supporting the use of ACWR in training-load-management systems or for training recommendations aimed at reducing injury risk. The statistical properties of the ratio make the ACWR an inaccurate metric and complicate its interpretation for practical applications. In addition, it adds noise and creates statistical artifacts.

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Matthew Weston, Warren Gregson, Carlo Castagna, Simon Breivik, Franco M. Impellizzeri, and Ric J. Lovell

Athlete case studies have often focused on the training outcome and not the training process. Consequently, there is a dearth of information detailing longitudinal training protocols, yet it is the combined assessment of both outcome and process that enhances the interpretation of physical test data. We were provided with a unique opportunity to assess the training load, physical match performance, and physiological fitness of an elite soccer referee from the referee’s final season before attaining full-time, professional status (2002) until the season when he refereed the 2010 UEFA Champions League and FIFA World Cup finals. An increased focus on on-field speed and gym-based strength training was observed toward the end of the study period and longitudinal match data showed a tendency for decreased total distances but an increased intensity of movements. Laboratory assessments demonstrated that VO2max remained stable (52.3 vs 50.8 mL-kg–1-min–1), whereas running speed at the lactate threshold (14.0 vs 12.0 km-h-1) and running economy (37.3 vs 43.4 mLkg–1min–1) both improved in 2010 compared with 2002.

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Paolo Menaspà, Franco M. Impellizzeri, Eric C. Haakonssen, David T. Martin, and Chris R. Abbiss

Purpose:

To determine the consistency of commercially available devices used for measuring elevation gain in outdoor activities and sports.

Methods:

Two separate observational validation studies were conducted. Garmin (Forerunner 310XT, Edge 500, Edge 750, and Edge 800; with and without elevation correction) and SRM (Power Control 7) devices were used to measure total elevation gain (TEG) over a 15.7-km mountain climb performed on 6 separate occasions (6 devices; study 1) and during a 138-km cycling event (164 devices; study 2).

Results:

TEG was significantly different between the Garmin and SRM devices (P < .05). The between-devices variability in TEG was lower when measured with the SRM than with the Garmin devices (study 1: 0.2% and 1.5%, respectively). The use of the Garmin elevation-correction option resulted in a 5–10% increase in the TEG.

Conclusions:

While measurements of TEG were relatively consistent within each brand, the measurements differed between the SRM and Garmin devices by as much as 3%. Caution should be taken when comparing elevation-gain data recorded with different settings or with devices of different brands.