Understanding Training Load as Exposure and Dose.

Various terms used in sport and exercise science, and medicine, are derived from other fields such as epidemiology, pharmacology and causal inference. Conceptual and nomological frameworks have described training load as a multidimensional construct manifested by two causally related subdimensions: external and internal training load. In this article, we explain how the concepts of training load and its subdimensions can be aligned to classifications used in occupational medicine and epidemiology, where exposure can also be differentiated into external and internal dose. The meanings of terms used in epidemiology such as exposure, external dose, internal dose and dose-response are therefore explored from a causal perspective and their underlying concepts are contextualised to the physical training process. We also explain how these concepts can assist in the validation process of training load measures. Specifically, to optimise training (i.e. within a causal context), a measure of exposure should be reflective of the mediating mechanisms of the primary outcome. Additionally, understanding the difference between intermediate and surrogate outcomes allows for the correct investigation of the effects of exposure measures and their interpretation in research and applied settings. Finally, whilst the dose-response relationship can provide evidence of the validity of a measure, conceptual and computational differentiation between causal (explanatory) and non-causal (descriptive and predictive) dose-response relationships is needed. Regardless of how sophisticated or "advanced" a training load measure (and metric) appears, in a causal context, if it cannot be connected to a plausible mediator of a relevant response (outcome), it is likely of little use in practice to support and optimise the training process.

Development and Validation of Single Items for Fatigue and Recovery in Dancers.

PURPOSE: To examine the construct validity and reliability of 2 single items for fatigue and recovery in dancers. The construct validity was assessed using reference instruments: the fatigue items of the Brunel Mood Scale (BRUMS) and the Short Recovery and Stress Scale (SRSS). A secondary aim was to explore the respondent interpretation of these 2 items using a concept identification approach. METHODS: Two hundred forty-three (N = 243) dancers completed single-item fatigue and recovery (unipolar and bipolar), BRUMS, and SRSS once for construct validity. For reliability, 49 dancers completed the questionnaires twice, 1 week apart. Using a concept identification approach, 49 dancers were also asked comprehension and interpretation of fatigue and recovery. RESULTS: The fatigue item correlated with SRSS stress items (rs = .37-.51) and BRUMs fatigue items (rs = .63-.66). The recovery item was only partially confirmed in terms of construct validity, when using the SRSS recovery items as reference (rs = .39-.43). Reliability was confirmed for the single items of fatigue (κ = .77-.78) and recovery (κ = .71-.78). Main responses for the concept of fatigue were tiredness (34.7%), muscle soreness (17.3%), and energy (13.0%). Main responses for the concept of recovery were muscle soreness (43.0%), tiredness (27.9%), and fatigue (24.0%). CONCLUSION: We provide preliminary confirmation of the validity and reliability of the single item fatigue in dancers. The recovery item was only partially confirmed in terms of construct validity, when using the SRSS recovery items as reference, but did display acceptable reliability and agreement. Further research is warranted further exploring other measurement properties.

The 'training load' construct: Why it is appropriate and scientific.

A recent paper called for the abandonment of the term load (and training load) when used outside its mechanical meaning, claiming it is "unscientific" and "breaches scientific principles." In this article, we explain why its use does not breach any scientific principles and we clarify the process of labelling, conceptualising and operationalising a construct. Training load is simply a label attributed to a higher-order construct overarching other interrelated sub-dimensions. This multi-level structure provides a framework (nomological network) to support the research process and also practical applications. Load is a word, and therefore cannot be "unscientific". The "use" or "misuse" of words and terms entirely depends upon definitions that should be based on current understanding. Misuse occurs when a term is decontextualised or interpreted according to a unilateral perspective. The field of mechanics does not have a monopoly on the term load (or other common terms such as work, stress and fatigue), which are legitimately used in many scientific areas and with various meanings. The 'obligation' to rely on terms abiding by the Système International d'Unités (SI) when describing a construct is inappropriate. The SI relates to how we can measure, not describe training load; i.e., SI is relevant to its operational and not its constitutive (descriptive) definition. Discussions regarding shared and standardised descriptions and definitions are more relevant than discussions about discarding terms in sport and exercise science. Researchers (and practitioners) can continue to use the term training load as it does not breach any scientific principles.

Development of a Revised Conceptual Framework of Physical Training for Use in Research and Practice.

A conceptual framework has a central role in the scientific process. Its purpose is to synthesize evidence, assist in understanding phenomena, inform future research and act as a reference operational guide in practical settings. We propose an updated conceptual framework intended to facilitate the validation and interpretation of physical training measures. This revised conceptual framework was constructed through a process of qualitative analysis involving a synthesis of the literature, analysis and integration with existing frameworks (Banister and PerPot models). We identified, expanded, and integrated four constructs that are important in the conceptualization of the process and outcomes of physical training. These are: (1) formal introduction of a new measurable component 'training effects', a higher-order construct resulting from the combined effect of four possible responses (acute and chronic, positive and negative); (2) explanation, clarification and examples of training effect measures such as performance, physiological, subjective and other measures (cognitive, biomechanical, etc.); (3) integration of the sport performance outcome continuum (from performance improvements to overtraining); (4) extension and definition of the network of linkages (uni and bidirectional) between individual and contextual factors and other constructs. Additionally, we provided constitutive and operational definitions, and examples of theoretical and practical applications of the framework. These include validation and conceptualization of constructs (e.g., performance readiness), and understanding of higher-order constructs, such as training tolerance, when monitoring training to adapt it to individual responses and effects. This proposed conceptual framework provides an overarching model that may help understand and guide the development, validation, implementation and interpretation of measures used for athlete monitoring.

Athlete-Reported Outcome Measures for Monitoring Training Responses: A Systematic Review of Risk of Bias and Measurement Property Quality According to the COSMIN Guidelines.

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.

Injury, Illness, and Training Load in a Professional Contemporary Dance Company: A Prospective Study.

CONTEXT: Professional dance is a demanding physical activity with high injury rates. Currently, no epidemiologic data exist regarding the incidence of injury and illness together with training load (TL) over a long period of time. OBJECTIVE: To provide a detailed description of injury, illness, and TL occurring in professional contemporary dancers. DESIGN: Descriptive epidemiology study. SETTING: A single professional contemporary dance company during a 1-year period. PATIENTS OR OTHER PARTICIPANTS: A total of 16 male and female professional contemporary dancers. MAIN OUTCOME MEASURE(S): Injury data consisted of medical-attention injury (Med-Inj) and time-loss injury (Time-Inj). Illness was measured using the Wisconsin Upper Respiratory Tract Infection Survey. Training load was collected for each dance session using the session rating of perceived exertion and classified into 3 groups based on individual and group percentiles: low, medium, or high. RESULTS: Reported injuries totaled 79 (86.1% new, 6.3% reinjury, and 7.6% exacerbation). The Med-Inj incidence rate was 4.6 per 1000 hours (95% confidence interval [CI] = 3.8, 5.8), and the Time-Inj rate was 1.4 per 1000 hours (95% CI = 0.8, 2.1). The median time until injury for Med-Inj and Time-Inj was 3 months. The number of days dancers experienced illness symptoms was 39.9 ± 26.9 (range = 1-96), with an incidence rate of 9.1 per 1000 hours (95% CI = 7.7, 10.7). Mean weekly TL was 6685 ± 1605 (4641-10 391; arbitrary units). Inconsistent results were found for the incidence of injury and illness based on individual and group categorizations of TL. CONCLUSIONS: Professional dancing is associated with high injury and illness rates. This is worrying from a health perspective and underlines the need for further studies to understand how to decrease the risk. The TL is higher than in other sport disciplines, but whether the high incidence of injuries and illnesses is related to high training demands needs additional investigation, possibly conducted as international, multicenter collaborative studies.

Quantifying Training Loads in Contemporary Dance.

PURPOSE: To describe the training demands of contemporary dance and determine the validity of using the session rating of perceived exertion (sRPE) to monitor exercise intensity and training load in this activity. In addition, the authors examined the contribution of training (ie, accelerometry and heart rate) and non-training-related factors (ie, sleep and wellness) to perceived exertion during dance training. METHODS: Training load and ActiGraphy for 16 elite amateur contemporary dancers were collected during a 49-d period, using heart-rate monitors, accelerometry, and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and several other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during dance training. RESULTS: Average weekly training load was 4283 ± 2442 arbitrary units (AU), monotony 2.13 ± 0.92 AU, strain 10677 ± 9438 AU, and average weekly vector magnitude load 1809,707 ± 1015,402 AU. There were large to very large within-individual correlations between training-load sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 49.7% of the adjusted variance in training-load sRPE was explained by peak heart rate, metabolic equivalents, soreness, motivation, and sleep quality (y = -4.637 + 13.817%HRpeak + 0.316 METS + 0.100 soreness + 0.116 motivation - 0.204 sleep quality). CONCLUSION: The current findings demonstrate the validity of the sRPE method for quantifying training load in dance, that dancers undertake very high training loads, and a combination of training and nontraining factors contribute to perceived exertion in dance training.