The Relationships Between Internal and External Measures of Training Load and Intensity in Team Sports: A Meta-Analysis

BackgroundThe associations between internal and external measures of training load and intensity are important in understanding the training process and the validity of specific internal measures.ObjectivesWe aimed to provide meta-analytic estimates of the relationships, as determined by a correlation coefficient, between internal and external measures of load and intensity during team-sport training and competition. A further aim was to examine the moderating effects of training mode on these relationships.MethodsWe searched six electronic databases (Scopus, Web of Science, PubMed, MEDLINE, SPORTDiscus, CINAHL) for original research articles published up to September 2017. A Boolean search phrase was created to include search terms relevant to team-sport athletes (population; 37 keywords), internal load (dependent variable; 35 keywords), and external load (independent variable; 81 keywords). Articles were considered for meta-analysis when a correlation coefficient describing the association between at least one internal and one external measure of session load or intensity, measured in the time or frequency domain, was obtained from team-sport athletes during normal training or match-play (i.e., unstructured observational study). The final data sample included 122 estimates from 13 independent studies describing 15 unique relationships between three internal and nine external measures of load and intensity. This sample included 295 athletes and 10,418 individual session observations. Internal measures were session ratings of perceived exertion (sRPE), sRPE training load (sRPE-TL), and heart-rate-derived training impulse (TRIMP). External measures were total distance (TD), the distance covered at high and very high speeds (HSRD ≥ 13.1–15.0 km h−1 and VHSRD ≥ 16.9–19.8 km h−1, respectively), accelerometer load (AL), and the number of sustained impacts (Impacts > 2–5 G). Distinct training modes were identified as either mixed (reference condition), skills, metabolic, or neuromuscular. Separate random effects meta-analyses were conducted for each dataset (n = 15) to determine the pooled relationships between internal and external measures of load and intensity. The moderating effects of training mode were examined using random-effects meta-regression for datasets with at least ten estimates (n = 4). Magnitude-based inferences were used to interpret analyses outcomes.ResultsDuring all training modes combined, the external load relationships for sRPE-TL were possibly very large with TD [r = 0.79; 90% confidence interval (CI) 0.74 to 0.83], possibly large with AL (r = 0.63; 90% CI 0.54 to 0.70) and Impacts (r = 0.57; 90% CI 0.47 to 0.64), and likely moderate with HSRD (r = 0.47; 90% CI 0.32 to 0.59). The relationship between TRIMP and AL was possibly large (r = 0.54; 90% CI 0.40 to 0.66). All other relationships were unclear or not possible to infer (r range 0.17–0.74, n = 10 datasets). Between-estimate heterogeneity [standard deviations (SDs) representing unexplained variation; τ] in the pooled internal–external relationships were trivial to extremely large for sRPE (τ range = 0.00–0.47), small to large for sRPE-TL (τ range = 0.07–0.31), and trivial to moderate for TRIMP (τ range= 0.00–0.17). The internal–external load relationships during mixed training were possibly very large for sRPE-TL with TD (r = 0.82; 90% CI 0.75 to 0.87) and AL (r = 0.81; 90% CI 0.74 to 0.86), and TRIMP with AL (r = 0.72; 90% CI 0.55 to 0.84), and possibly large for sRPE-TL with HSRD (r = 0.65; 90% CI 0.44 to 0.80). A reduction in these correlation magnitudes was evident for all other training modes (range of the change in r when compared with mixed training − 0.08 to − 0.58), with these differences being unclear to possibly large. Training mode explained 24–100% of the between-estimate variance in the internal–external load relationships.ConclusionMeasures of internal load derived from perceived exertion and heart rate show consistently positive associations with running- and accelerometer-derived external loads and intensity during team-sport training and competition, but the magnitude and uncertainty of these relationships are measure and training mode dependent.

[1]  Will G. Hopkins,et al.  A spreadsheet for deriving a confidence interval, mechanistic inference and clinical inference from a P value , 2007 .

[2]  Matthew D Portas,et al.  Variability of physical performance and player match loads in professional rugby union. , 2016, Journal of science and medicine in sport.

[3]  T. Gabbett Influence of Ball-in-Play Time on the Activity Profiles of Rugby League Match-Play , 2015, Journal of strength and conditioning research.

[4]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[5]  Kevin Ball,et al.  Quantifying external load in Australian football matches and training using accelerometers. , 2013, International journal of sports physiology and performance.

[6]  T. Gabbett,et al.  Internal and External Match Loads of University-Level Soccer Players: A Comparison Between Methods , 2017, Journal of strength and conditioning research.

[7]  Dan Weaving,et al.  Combining internal- and external-training-load measures in professional rugby league. , 2014, International journal of sports physiology and performance.

[8]  Gershon Tenenbaum,et al.  Perceived effort — Can it be considered gestalt? , 2006 .

[9]  J M TANNER,et al.  Fallacy of per-weight and per-surface area standards, and their relation to spurious correlation. , 1949, Journal of applied physiology.

[10]  Shona L. Halson,et al.  Monitoring Training Load to Understand Fatigue in Athletes , 2014, Sports Medicine.

[11]  Samuele M. Marcora Perception of effort during exercise is independent of afferent feedback from skeletal muscles, heart, and lungs. , 2009, Journal of applied physiology.

[12]  T. Gabbett,et al.  Repeated-Sprint and Effort Ability in Rugby League Players , 2011, Journal of strength and conditioning research.

[13]  Aaron J Coutts,et al.  Validity and Reliability of the Session-RPE Method for Quantifying Training in Australian Football: A Comparison of the CR10 and CR100 Scales , 2013, Journal of strength and conditioning research.

[14]  A. Mahon,et al.  Perceived Exertion , 2006, Sports medicine.

[15]  W. Hopkins Individual responses made easy. , 2015, Journal of applied physiology.

[16]  A. Coutts,et al.  Monitoring Athlete Training Loads: Consensus Statement. , 2017, International journal of sports physiology and performance.

[17]  Grant Abt,et al.  Methods of monitoring the training and match load and their relationship to changes in fitness in professional youth soccer players , 2012, Journal of sports sciences.

[18]  Tim J Gabbett,et al.  Pre-training perceived wellness impacts training output in Australian football players , 2016, Journal of sports sciences.

[19]  A. Coutts,et al.  Intersubjective comparisons are possible with an accurate use of the Borg CR scales. , 2011, International journal of sports physiology and performance.

[20]  Andrew Smith,et al.  A detailed quantification of differential ratings of perceived exertion during team-sport training. , 2017, Journal of science and medicine in sport.

[21]  Michael Ian Lambert,et al.  Measuring training load in sports. , 2010, International journal of sports physiology and performance.

[22]  Stephen D. Mellalieu,et al.  Training Load and Fatigue Marker Associations with Injury and Illness: A Systematic Review of Longitudinal Studies , 2016, Sports Medicine.

[23]  A. Nevill,et al.  Selected issues in the design and analysis of sport performance research , 2001, Journal of sports sciences.

[24]  Aaron J Coutts,et al.  Between match variation in professional rugby league competition. , 2014, Journal of science and medicine in sport.

[25]  Aaron T. Scanlan,et al.  The Relationships Between Internal and External Training Load Models During Basketball Training , 2014, Journal of strength and conditioning research.

[26]  Matthew Weston,et al.  Difficulties in determining the dose-response nature of competitive soccer matches , 2013 .

[27]  A. Batterham,et al.  Evaluating Intervention Fidelity: An Example from a High-Intensity Interval Training Study , 2015, PloS one.

[28]  Will G Hopkins,et al.  Variability and predictability of finals times of elite rowers. , 2011, Medicine and science in sports and exercise.

[29]  D. Altman,et al.  Statistics notes: Calculating correlation coefficients with repeated observations: Part 1—correlation within subjects , 1995 .

[30]  Matthew C Varley,et al.  Wearable Training-Monitoring Technology: Applications, Challenges, and Opportunities. , 2017, International journal of sports physiology and performance.

[31]  Stanley E Lazic,et al.  The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? , 2010, BMC Neuroscience.

[32]  K. Pearson Mathematical contributions to the theory of evolution.—On a form of spurious correlation which may arise when indices are used in the measurement of organs , 1897, Proceedings of the Royal Society of London.

[33]  D. Burgess The Research Doesn't Always Apply: Practical Solutions to Evidence-Based Training-Load Monitoring in Elite Team Sports. , 2017, International journal of sports physiology and performance.

[34]  Martin Buchheit,et al.  High-Intensity Interval Training, Solutions to the Programming Puzzle , 2013, Sports Medicine.

[35]  Stephen J Kelly,et al.  Validity and Interunit Reliability of 10 Hz and 15 Hz GPS Units for Assessing Athlete Movement Demands , 2014, Journal of strength and conditioning research.

[36]  Tim J Gabbett,et al.  The training—injury prevention paradox: should athletes be training smarter and harder? , 2016, British Journal of Sports Medicine.

[37]  Daniel Castillo,et al.  Relationships Between Internal and External Match-Load Indicators in Soccer Match Officials. , 2017, International journal of sports physiology and performance.

[38]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement , 2009, BMJ.

[39]  Andrew D White,et al.  Time-on-pitch or full-game GPS analysis procedures for elite field hockey? , 2013, International journal of sports physiology and performance.

[40]  S. Robertson,et al.  Title : Relationships Between Internal and External Training Load in Team Sport Athletes : Evidence for an Individualised Approach , 2016 .

[41]  Carlo Castagna,et al.  Relation between individualized training impulses and performance in distance runners. , 2009, Medicine and science in sports and exercise.

[42]  Monitoring training loads in professional rugby league , 2009 .

[43]  Paul B Gastin,et al.  Red, Amber, or Green? Athlete Monitoring in Team Sport: The Need for Decision-Support Systems. , 2017, International journal of sports physiology and performance.

[44]  Franco M Impellizzeri,et al.  Factors affecting perception of effort (session rating of perceived exertion) during rugby league training. , 2013, International journal of sports physiology and performance.

[45]  E. Cerin,et al.  Consensus on measurement properties and feasibility of performance tests for the exercise and sport sciences: a Delphi study , 2017, Sports Medicine - Open.

[46]  Will G Hopkins,et al.  Monitoring acute effects on athletic performance with mixed linear modeling. , 2010, Medicine and science in sports and exercise.

[47]  W. Kraemer,et al.  The Physiological Basis of Wrestling: Implications for Conditioning Programs , 2004 .

[48]  T. Gabbett,et al.  Cost-benefit analysis underlies training decisions in elite sport , 2016, British Journal of Sports Medicine.

[49]  S. Marshall,et al.  Progressive statistics for studies in sports medicine and exercise science. , 2009, Medicine and science in sports and exercise.

[50]  Casamichana David,et al.  The Relationship Between Intensity Indicators in Small-Sided Soccer Games , 2015, Journal of human kinetics.

[51]  W. Helsen,et al.  Relationships Between Training Load Indicators and Training Outcomes in Professional Soccer , 2017, Sports Medicine.

[52]  Tim Meyer,et al.  How Valid are They , 2009 .

[53]  Therese D. Pigott,et al.  An alternative to R2 for assessing linear models of effect size , 2010, Research synthesis methods.

[54]  A. Batterham,et al.  The Use of Ratios and Percentage Changes in Sports Medicine: Time for a Rethink?· , 2012, International Journal of Sports Medicine.

[55]  Pedro Silva,et al.  Validity of Heart Rate-Based Indices to Measure Training Load and Intensity in Elite Football Players , 2017, Journal of strength and conditioning research.

[56]  N. Laird,et al.  Meta-analysis in clinical trials. , 1986, Controlled clinical trials.

[57]  Michael K Drew,et al.  The Relationship Between Training Load and Injury, Illness and Soreness: A Systematic and Literature Review , 2016, Sports Medicine.

[58]  J. Hawley,et al.  The Molecular Bases of Training Adaptation , 2007, Sports medicine.

[59]  J P Archie,et al.  Mathematic Coupling of Data: A Common Source of Error , 1981, Annals of surgery.

[60]  B. Jones,et al.  The Use of Accelerometers to Quantify Collisions and Running Demands of Rugby Union Match-Play , 2016 .

[61]  G. Abt,et al.  Integrating the internal and external training loads in soccer. , 2014, International journal of sports physiology and performance.

[62]  Tim J Gabbett,et al.  Relationship Between Accelerometer Load, Collisions, and Repeated High-Intensity Effort Activity in Rugby League Players , 2015, Journal of strength and conditioning research.

[63]  P D Milburn The kinetics of rugby union scrummaging. , 1990, Journal of sports sciences.

[64]  A. Coutts,et al.  Use of CR100 Scale for Session Rating of Perceived Exertion in Soccer and Its Interchangeability With the CR10. , 2016, International journal of sports physiology and performance.

[65]  R. Persaud Correlation, regression, and repeated data , 1994, BMJ.

[66]  Douglas G. Altman,et al.  Statistics Notes: Correlation, regression, and repeated data , 1994 .

[67]  J. Higgins,et al.  Cochrane Handbook for Systematic Reviews of Interventions , 2010, International Coaching Psychology Review.

[68]  R. Robertson,et al.  15 Perception of Physical Exertion: Methods, Mediators, and Applications , 1997, Exercise and sport sciences reviews.

[69]  Julian P T Higgins,et al.  Commentary: Heterogeneity in meta-analysis should be expected and appropriately quantified. , 2008, International journal of epidemiology.

[70]  Robert G Lockie,et al.  A comparison of methods to quantify the in-season training load of professional soccer players. , 2013, International journal of sports physiology and performance.

[71]  E. Rampinini,et al.  Physiological assessment of aerobic training in soccer , 2005, Journal of sports sciences.

[72]  D. Doran,et al.  The Integration of Internal and External Training Load Metrics in Hurling , 2016, Journal of human kinetics.

[73]  M. Weston,et al.  The Sensitivity of Differential Ratings of Perceived Exertion as Measures of Internal Load. , 2016, International journal of sports physiology and performance.

[74]  Grant Abt,et al.  The use of individualized speed and intensity thresholds for determining the distance run at high-intensity in professional soccer , 2009, Journal of sports sciences.

[75]  Carlo Castagna,et al.  Relationship Between Indicators of Training Load in Soccer Players , 2013, Journal of strength and conditioning research.

[76]  Tim J Gabbett,et al.  The Relationship Between Workloads, Physical Performance, Injury and Illness in Adolescent Male Football Players , 2014, Sports Medicine.

[77]  A. Coutts,et al.  Relationship Between External and Internal Loads of Professional Soccer Players During Full Matches in Official Games Using Global Positioning Systems and Heart-Rate Technology. , 2016, International journal of sports physiology and performance.

[78]  Ermanno Rampinini,et al.  High-intensity training in football. , 2009, International journal of sports physiology and performance.

[79]  Grant Trewartha,et al.  Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures. , 2017, International journal of sports physiology and performance.

[80]  C. Foster,et al.  A New Approach to Monitoring Exercise Training , 2001, Journal of strength and conditioning research.

[81]  L. Engebretsen,et al.  How much is too much? (Part 2) International Olympic Committee consensus statement on load in sport and risk of illness , 2016, British Journal of Sports Medicine.

[82]  Jos Vanrenterghem,et al.  Training Load Monitoring in Team Sports: A Novel Framework Separating Physiological and Biomechanical Load-Adaptation Pathways , 2017, Sports Medicine.

[83]  Romain Meeusen,et al.  How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury , 2016, British Journal of Sports Medicine.

[84]  E. Sáez De Villarreal,et al.  Match-play activity profile in professional soccer players during official games and the relationship between external and internal load. , 2015, The Journal of sports medicine and physical fitness.

[85]  W. Hopkins,et al.  Quantification of Training Load During Return to Play After Upper- and Lower-Body Injury in Australian Rules Football. , 2017, International journal of sports physiology and performance.

[86]  Alan M Batterham,et al.  Making meaningful inferences about magnitudes. , 2006, International journal of sports physiology and performance.

[87]  Ric Lovell,et al.  The application of differential ratings of perceived exertion to Australian Football League matches. , 2015, Journal of science and medicine in sport.

[88]  George P Nassis,et al.  Training Load and Player Monitoring in High-Level Football: Current Practice and Perceptions. , 2016, International journal of sports physiology and performance.

[89]  G. Abt,et al.  Multiple Measures are Needed to Quantify Training Loads in Professional Rugby League , 2017, International Journal of Sports Medicine.

[90]  Guido Knapp,et al.  Improved tests for a random effects meta‐regression with a single covariate , 2003, Statistics in medicine.

[91]  The Influence of Exercise-to-Rest Ratios on Physical and Physiological Performance During Hurling-Specific Small-Sided Games , 2017, Journal of strength and conditioning research.

[92]  Tim J. Gabbett,et al.  Is workload associated with injuries and performance in elite football? A call for action , 2016, British Journal of Sports Medicine.

[93]  R. Fisher 014: On the "Probable Error" of a Coefficient of Correlation Deduced from a Small Sample. , 1921 .

[94]  Ashley A. Kavanaugh,et al.  Establishing a duration standard for the calculation of session rating of perceived exertion in NCAA division I men’s soccer , 2017 .

[95]  Stuart J. Cormack,et al.  Characteristics impacting on session rating of perceived exertion training load in Australian footballers , 2015, Journal of sports sciences.

[96]  Aaron J Coutts,et al.  Match-to-match variation in physical activity and technical skill measures in professional Australian Football. , 2015, Journal of science and medicine in sport.

[97]  A. Batterham,et al.  True and false interindividual differences in the physiological response to an intervention , 2015, Experimental physiology.

[98]  G. Atkinson,et al.  Match-to-Match Variability of High-Speed Activities in Premier League Soccer , 2010, International journal of sports medicine.

[99]  Greg Atkinson,et al.  Factors influencing perception of effort (session rating of perceived exertion) during elite soccer training. , 2015, International journal of sports physiology and performance.

[100]  Stephen J Senn,et al.  Overstating the evidence – double counting in meta-analysis and related problems , 2009, BMC Medical Research Methodology.

[101]  K. Quarrie,et al.  Managing player load in professional rugby union: a review of current knowledge and practices , 2016, British Journal of Sports Medicine.

[102]  D. Altman,et al.  Calculating correlation coefficients with repeated observations: Part 2--Correlation between subjects. , 1995, BMJ.

[103]  J. Cordy Quantification of training load during return to play following upper and lower body injury in Australian Rules Football , 2016 .

[104]  A. Coutts,et al.  Unpacking the Black Box: Applications and Considerations for Using GPS Devices in Sport. , 2017, International journal of sports physiology and performance.

[105]  D G Altman,et al.  Correlation, regression, and repeated data. , 1994, BMJ.

[106]  E. Rampinini,et al.  Accuracy of GPS Devices for Measuring High-intensity Running in Field-based Team Sports , 2014, International Journal of Sports Medicine.