Athlete Monitoring Systems: Overview - SSISA · PDF fileDeveloping Athlete Monitoring Systems:...

2/20/2016

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DevelopingAthleteMonitoringSystems:Theoreticalbasisandpracticalapplications

ProfessorAaronCouttsPh.D.UniversityofTechnologySydney(UTS),Sydney,AUSTRALIA @aaronjcoutts

Overview

Theoretical basis monitoring training in athletes

Markers of fatigue and recovery

Model for monitoring training

Examine simple tools for monitoring: Training load

Fitness

Fatigue

Model for integrating into sport

Speed/Agility

CK

Strength/Power

DXA

Nutrition

Wizard Variables

Wizard Daily Training Report

Play / Squad Weekly Status

Player Daily Readiness

Session / Match Summary

Physio screening

Wizard Variables

Kicking coding

Recruiting

5‐5’





Medical Database

Performance

Loading

Drills Database

Medical

Custom Reports

SportScienceinDailyPractices:imbeddedevidence‐basedsystems

Rexy’s App

Speed/AgilitySpeed/Agility

CK

Strength/Power

DXA

Nutrition

Wizard Variables





Physio screening

Wizard Variables

Kicking coding

Recruiting

5‐5’





Medical Database

Performance

Data

Drills Database

Medical

Custom Reports

Rexy’s App

Speed/Agility

TRAINING&GAMELOAD FATIGUE&WELLBEING

FITNESS

PERFORMANCE&INJURY

TrainingTheory101

Time

Cap

acity

Training

Adaptation

Optimum time between bouts

TrainingOverload&Adaptation

Fatigue

Fitness

Time

Training Effect

TrainingDose‐Response:FundamentalsofFatigueandRecovery

PERFORMANCE

Performance = Fitness – Fatigue

[Banister et al., 1975, Busso et al., 2003]

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Training Period (Days)

Perform

ance Level

Fatigue Effect

Fitness Effect

Performance

Training Impulses

ModellingTrainingResponses

[Banister et al., 1975, Busso et al., 2003]

Performance = Fitness – Fatigue

Training–PerformanceRelationships

Performance is dependent upon the accumulatedfitness and the current fatigue levels.

Because of the non‐linear responses in fitness andfatigue and the individual characteristics it isdifficult to follow an athletes individual response.

Monitoring systems help us better understand anddirect this process – using various markers offatigue and recovery

MarkersofFatigue&Recovery:OverreachingandOvertrainingResearch

Poor performance Training incompetence

Physiological Markers ↓HRmax

HR as submaximal workload

Autonomic imbalance

Blood Markers ↑Muscle damage and catabolism

↑ Inflammation

Altered hormonal responses

Perceptual Markers ↑ Stress

↑ Fatigue

↑ Soreness

↓ Recovery

↓ Sleep

CommonSignsofOverreaching‐Overtraining

Urhausen and Kindermann (2002), Halson et al (2003)

Psycho‐BiologicalBasisofFatigue

Many biochemical / physiological / psychological ‘markers’

Most of limited practical value?.......

Time for analysis, cost, invasiveness etc…

The same relationships observed in research studies are notalways observed in the field…. The environment is differentto ‘training studies’.

Athlete fatigue in the field can be much more complicatedthan simplified models shown in research papers

Summary

Training elicits changes in fitness and fatigue

Performance is a function of the balance of fitness and fatigue

Monitoring these responses are important for understanding and guiding the control of the training process

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TheoreticalSystemforOptimisingTraining

Brink et al (2012) SJSMS

FOR NFOR OvertrainingAdapting

Monitoring Zone

Look for signs & symptoms Monitor training input (dose) Intended vs. actual dose Monitor training responses

Develop an iterative method for monitoring training

NeedaMonitoringModelforControllingTraining:

PracticalTools

IterativeModelforMonitoringTraining:ControltheTrainingProcess

RESPONSE

Feedback Loop

TRAINING PLAN DOSE

Fatigue

Fitness

PERFORMANCE

PERFORMANCE FITNESS FATIGUE

Training Load

= -

WhatareImportantMeasures?

INTERNAL TRAININGLOAD

Individual Characteristics PeriodisationQuality & Quantity

External Training Load

HOW DID PLAYERS RESPOND?

WAS TRAINING IMPLEMENTED AS

PLANNED?

Impellizzeri et al., 2005

‐ Fatigue

TRAININGOUTCOME

+ Fitness

MeasuringTrainingLoad:TheoreticalBasis

Impellizzeri et al., 2005

TrainingLoad=VolumexIntensity

Definition

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QuantifyingInternalLoad

136 bpm

5

4

3

2

1

90-100%

80-90%

70-80%

60-70%

50-60%

Training Load = Σ Scores for each zone

HeartRateTrainingLoad:Summedzones

Bannister et al, 1975, Lucia et al 2003, Edwards, 1993

HeartRateMeasures

Valid measures of ‘aerobic’ load

Monitor for aerobic adaptations

May not account for anaerobic or eccentric actions

Players don’t usually like wearing monitors!

Session‐RPE:Asimplemethod

Foster et al., 2001

Determining session RPE:

Ask athletes “How was your work out?”

30 min following bout

Calculate:

RPE = Global intensity

Training Load = duration x RPE

EXAMPLE:

RPE = 5 (HARD SESSION)

Duration = 40 min

Load = 5 x 40 = 200 AU

0

100

200

300

400

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Training days

RPE

-TR

IMP

(AU

) .

100150200250300350400450500550600

Edw

ard'

s TR

IMP

.

RPE-TRIMPEdward's TRIMP

95% confidence

100 200 300 400 500 600 700 800 900

RPE-TL

140

180

220

260

300

340

Edw

ards

' TL

Impellizzeri et al., (2004) MSSE

Validityofsession‐RPE

HR and Blood Lactate Correlates of RPE during Football:

851 sessions of soccer small‐sided games (4 x 4 min bouts)

Heart Rate and Blood Lactate Measures during bouts

RESULTS

43.1% of the adjusted variance in RPE could be explained by HR alone.

The addition of [BLa‐] data allowed for 57.8% of the adjusted variance in RPE to

be predicted

These results suggest session‐RPE a better indicator of global exercise intensity

Coutts et al., (2009) JSAMS.

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QuantifyingExternalLoads:Training&MatchPlay

Commercial Products Automated Camera Systems GPS Accelerometers Gyroscopes, magnetometers…. Metabolic power estimations Isoinertial movement analysis ……

QuantifyingExternalTrainingLoad

SummaryTrainingLoads

RPE, heart rate and microtechnology (GPS and accelerometers) used widely in practice

Understand noise in your load measurements

Ensure the method is valid and reliable!

Monitor training response using internal training load

MeasuringFitness&Fatigue

ModelforMonitoringTraining

RESPONSE

Feedback Loop

TRAINING PLAN DOSE

Fatigue

Fitness

PERFORMANCE

QuantifyingFitnessSubmaximalHeartRateResponses

4 min submaximal run @ 14 km/h

HRex

HRR

HRVresponse

HRex – Fitness changes

HRrecovery – Tolerance to training load

HRV – Fatigue

Buchheit et al (2011) EJAP

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MethodsforQuantifying‘Fatigue’

Talk to athletes: ‘Are you tired / fatigue / sore etc….?’

Psychometric questionnaires DALDA [Rushall, 1991]

RESTQ‐Sport [Kellmann & Kallus, 2001]

POMS [McNair et al., 1971]

BRUMS [Terry et al 1999]

Wellness questionnaires [Hooper et al., 1995]

Training distress score [Main et al., 2009]

Blood measures: endocrine, muscle damage etc…?

Borrensen & Lambert (2009) IJSSC

WellnessQuestionnaires

2

2

2.5

3

2

11.5

MuscleDamage

Time course in recovery from matches Relationships between measures

SeparatingtheSignalfromtheNoise

Need to understand “normal” variations in the measures

within‐athlete, day‐to‐day variability

Understanding Acute (recent) and Chronic Changes (long‐term) within Individuals & Team (spike risks)

Common Approaches:

Understand the Smallest Worthwhile Changes in each test

Use within individual Z‐scores analysis

InterpretingChangesinVariables

Biomarkers & Performance measures Assess clinical likelihoods of change

75% Chance of a ‘Real Change’: Week‐to‐Week variation + SWC [0.2 x test‐retest CV]

Subjective markers Convert to Z‐scores (standard difference scores)

Individual acute response: (Current score‐baseline)/SD of individuals baseline

Individual chronic response: (4 week rolling average ‐ baseline)/SD of individual baseline

AnalysisofMonitoringData

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Fundamentals of a training monitoring system:

i. based on a theoretical framework;

ii. consistently & easily to implement;

iii. not easily manipulated;

iv. not too demanding for athletes;

v. affordable; and,

vi. analysed thoroughly

Summary

MAKINGAMODELWORK:

1.PlanningandImplementingLoads

PlanningLoads

1. Provide loading guidelines for week (intended)

a) Account for chronic loadings and acute spikes

2. Coach chooses drill according to tactical requirements / goals

a) Check if meets intended technical /tactical goals

b) Determine projected loading to meet periodisation goals/rules:• Training variation (monotony)

• Awareness of within‐week spikes (accelerations, high speeds and legs‐legs)

• Recovery after previous and before next match (inseason)

3. Modify individuals to meet fitness / medical / physiotherapy / wellness goalsa) Plan additional running or strength and conditioning

b) Modify drills deemed to be of risk (screening)

4. Compare actual loads to predicted loads

Tactical Goals

Physical LoadingLoad Constraints (GOAL ±2SD)

Running Loads Total distance High Speed Running Sprint load

Global Load sRPE

WEEK PLAN

G +1 G +2 G +1 G ‐2 G ‐1 G

Proportion planned load over the week

Adjust daily based on: Wellness (soreness,

fatigue) Medical screening

(squeeze, lunge) Collective feedback

(coach & fitness/medical)

PhysicalLoadingControlModel

ModelIntendedLoads

Coutts et al (2011) ECSS.

0.0

2.0

4.0

6.0

8.0

10.0

ses

sio

n-R

PE

(C

R-1

0)

Very Hard

Hard

Moderate

0.0

0.6

1.2

1.8

2.4

3.0

Kic

ks

(n

/min

)

15

18

21

24

27

30

Pe

ak

Sp

ee

d (

km

/h)

0

15

30

45

60

HS

R (

m/m

in)

0

50

100

150

200

Sp

ee

d (

m/m

in)

Ball Movement Game SenseGame Sense (Moderate)Line Specific Handball SSGs Kicking SSGs Skill Acquisition Tackling / Combat0.0

5.0

10.0

15.0

20.0

25.0

Drill Name

Tim

e (

min

)

sRPE

KickingRate

PeakSpeed

HSR

Speed

Time

AssessingTrainingDrillDemands

Stoppage

Motion

Crush

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SelectingTrainingDrillsToMeetTactical&LoadGoals

Stoppage

Motion

Crush

We have ball

Ball in dispute

Opponent has ball

DrillIntensity: LowModerate

High

TacticalGoal:

Live GPS Data Kicking Load Data

Cumulative Projected vs. ActualLoad Data

Cumulative Proj vs. Actual GPS & Kicking Data

Training Drills

Training Load Data

TrainingLoad– InjuryRelationships

Colby et al (2014) JSCR, Rogalski et al (2013) JSAMS, Blanch & Gabbett (2015) BJSM

UnderstandInjuryRiskLoadThresholds

Higher chronic loads associated with increased risk: 3‐wk cumulative distance and s‐RPE load associated with increased

injury risk

Lower chronic loads associated with increased risk

Load ‘spikes’ associated with injury risk. acute:chronic load ratio: = previous week last 4 week load

2000.10

0.150.20

0.250.30

0.350.40

0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00

1900.11

0.160.21

0.260.32

0.370.42

0.47 0.53 0.58 0.63 0.68 0.74 0.79 0.84 0.89 0.95 1.00 1.05

1800.11

0.170.22

0.280.33

0.390.44

0.50 0.56 0.61 0.67 0.72 0.78 0.83 0.89 0.94 1.00 1.06 1.11

1700.12

0.180.24

0.290.35

0.410.47

0.53 0.59 0.65 0.71 0.76 0.82 0.88 0.94 1.00 1.06 1.12 1.18

1600.13

0.190.25

0.310.38

0.440.50

0.56 0.63 0.69 0.75 0.81 0.88 0.94 1.00 1.06 1.13 1.19 1.25

1500.13

0.200.27

0.330.40

0.470.53

0.60 0.67 0.73 0.80 0.87 0.93 1.00 1.07 1.13 1.20 1.27 1.33

1400.14

0.210.29

0.360.43

0.500.57

0.64 0.71 0.79 0.86 0.93 1.00 1.07 1.14 1.21 1.29 1.36 1.43

1300.15

0.230.31

0.380.46

0.540.62

0.69 0.77 0.85 0.92 1.00 1.08 1.15 1.23 1.31 1.38 1.46 1.54

1200.17

0.250.33

0.420.50

0.580.67

0.75 0.83 0.92 1.00 1.08 1.17 1.25 1.33 1.42 1.50 1.58 1.67

1100.18

0.270.36

0.450.55

0.640.73

0.82 0.91 1.00 1.09 1.18 1.27 1.36 1.45 1.55 1.64 1.73 1.82

1000.20

0.300.40

0.500.60

0.700.80

0.90 1.00 1.10 1.20 1.30 1.40 1.50 1.60 1.70 1.80 1.90 2.00

900.22

0.330.44

0.560.67

0.780.89

1.00 1.11 1.22 1.33 1.44 1.56 1.67 1.78 1.89 2.00 2.11 2.22

800.25

0.380.50

0.630.75

0.881.00

1.13 1.25 1.38 1.50 1.63 1.75 1.88 2.00 2.13 2.25 2.38 2.50

700.29

0.430.57

0.710.86

1.001.14

1.29 1.43 1.57 1.71 1.86 2.00 2.14 2.29 2.43 2.57 2.71 2.86

600.33

0.500.67

0.831.00

1.171.33

1.50 1.67 1.83 2.00 2.17 2.33 2.50 2.67 2.83 3.00 3.17 3.33

500.40

0.600.80

1.001.20

1.401.60

1.80 2.00 2.20 2.40 2.60 2.80 3.00 3.20 3.40 3.60 3.80 4.00

400.50

0.751.00

1.251.50

1.752.00

2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00

300.67

1.001.33

1.672.00

2.332.67

3.00 3.33 3.67 4.00 4.33 4.67 5.00 5.33 5.67 6.00 6.33 6.67

201.00

1.502.00

2.503.00

3.504.00

4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.00

2030

4050

6070

8090 100 110 120 130 140 150 160 170 180 190 200

Acute:Chronic Workload Ratio

Based on % of Normal Average

Chronic Workload (% of

Normal Average)

Acute Workload (% of Normal Average)

OptimiseLoadswithinTolerableLimits

IdentifyRisks

1. Did we plan training properly?

2. Was technical load as planned?

3. Did players meet external load goals?

4. What are our impending risks?

CheckListforLoadMonitoring

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2.AssessResponses:Fitness&Fatigue

MonitoringResponse:Wellbeing+Fatigue

Daily, 2 mins

Same time of day

Compared to individual norms

Look for the RED and GREEN traffic lights

MonitoringResponse:Wellbeing+Fatigue MonitoringFitness:4’‐1’Test

4 min submaximal run @ 14 km/h

HRex

HRR

HRex – Fitness changes

HRrecovery – Tolerance to training load

Buchheit et al (2013) JSAMS

Look for the RED and GREEN traffic lights

Season 2013

30th August Test #1 Test #2 Test #3 Test #4 Test #5

End of Season 2012 Start of Pre Season 2013 3/12/2012 21/12/2012 16/01/2013 30/01/2013

Armfield Injured Maintenance Worsening Away Bad Data Injured

Bell NA Injured Injured Bad Data First Test Worsening

Betts NA Worsening Improving Away Improving Worsening

Bootsma Improving Worsening NA Improving Bad Data Maintenance

Buckley Improving Improving NA Bad Data Worsening Off Legs

Cachia First test Injured Improving

Carrazzo Away Injured Maintenance Injured Injured Bad data

Casboult Injured Injured First Test Improving Improving Improving

Collins Improving Improving Worsening Improving Improving Bad data

Curnow NA Improving Worsening Improving Bad Data Improving

Dale Worsening Improving Improving Bad Data NA Off Legs

Davies NA Injured Injured Away Bad Data Bad data

Duigan Worsening Improving Worsening Improving Bad Data Worsening

Ellard Injured Worsening Improving Improving Improving Bad data

Garlett NA Worsening Worsening Improving Worsening Bad data

Gibbs NA Improving Maintenance Improving Worsening Improving

Graham Injured Improving Improving

Hampson Injured Injured First Test Improving Injured Improving

Henderson Injured Improving Worsening Away Injured Injured

Jamison NA Worsening Bad Data Bad Data Injured Injured

Joseph Improving Improving Worsening Improving Bad Data Improving

Judd NA Worsening Injured Worsening Improving Bad data

Kreuzer NA Injured Injured Bad Data Injured 1st test

Laidler Injured Worsening Worsening Bad Data Improving Improving

Lucas Improving Worsening Injured Away Bad Data Bad data

McCarthy NA Improving NA Injured Bad Data Bad data

McInnes Injured Injured Injured Injured Injured Injured

McLean Bad Data Away Maintenance Improving Bad Data Worsening

Menzel Injured Injured Injured

Mitchell Worsening Improving Worsening Injured Injured Improving

Murphy Worsening Injured Injured Injured First Test Bad data

O'Keefe Improving Improving NA Improving Worsening Sick

Robinson Worsening Improving NA Bad Data Worsening Improving

Rowe Worsening Improving Maintenance Improving Bad Data Bad data

Scotland Worsening Worsening Improving Bad Data Off Legs Injured

Simpson NA Improving Worsening Improving Worsening Bad data

Temay Bad Data Improving Worsening

Tuohy Improving Improving Improving Bad Data Bad Data Improving

Waite Improving NA First Test Improving Injured Injured

Walker Maintenance Improving Worsening Injured Injured Improving

Warnock Injured Improving Worsening Improving Worsening Bad data

Watson Injured Improving Bad Data Worsening Improving Improving

White Maintenance Improving Bad Data Bad Data Worsening Improving

Yarran Improving Improving Injured Worsening Worsening Improving

TEAM Improving Improving Improving Improving Maintenance Improving

HR exercise

MonitoringFitness:HRsubmax

Look for the RED& GREEN traffic

lights

PhysiotherapyScreening

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Wellness response Did it compare to yesterday?

Did it compare to same time in recovery cycle?

Did it compare to past month?

Fitness & Load Tolerance Maintaining fitness

Tolerating load

Look for non‐typical responses…

CheckListforResponseMonitoring

3.UsetheFeedbacktoAssessandInformPractice

Intervention

UseTrafficLight Alerts: Talk with players Alert relevant staff

Developactionplan: Modify individual load accordingly Set a recovery & nutritional

intervention Medical/Physiotherapy treatment Further investigation…. (medical /

physio etc.) Recommendations to coaches......

ModelforMonitoringTraining

RESPONSE

Feedback Loop

TRAINING PLAN DOSE

Fatigue

Fitness

PERFORMANCE

INFORMTHETRAININGCYCLE

PERFORM

OBSERVE

ANALYSEPLAN

INTERPRET&

INTERVENE

PREPARE

THE TRAINING CYCLE

TakeHomeMessage

Get the basics of data collection right

Separating the signal and noise in all of the data

Focussed on the fundamentals before getting carried away withforecasting and prediction [interesting vs. important]

Scientists develop expertise in both coaching and science [& viceversa]

Get the meaningful data into the hands [and minds] of the peoplewho are in a position to make use of it!

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TakeHomeMessage

SELECT MONITORING TOOLS TO SUIT TEAM ENIVRONMENT1. Use simple tools and good science consistently2. Collect data properly3. Analyse data thoroughly

BEST TOOLS:1. Talk to athletes2. Work with coaches3. ‘Wellness’ measures4. Training load (session‐RPE)5. External load (GPS)

William of Ockham1288 1348

We should not use more than necessary!

Coutts (2014) IJSPP

Thankyou

Athlete Monitoring Systems: Overview - SSISA · PDF fileDeveloping Athlete Monitoring Systems:...

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