Training Load Management for Injury Risk Flashcards
state what it is meant by the key term - ‘training load’
can be described as a higher-order construct reflecting the amount of PA that is actually done and experienced by the athletes and not what was planned, which is training prescription
what is the ‘Theoretical Basis for Movement’ stated by (Banister et al., 1975)
the basis of training monitoring us based on the ‘dose-response relationship’ - the response of an athlete to one ‘dose’ of training
(Banister et al., 1975)
what is the ‘theoretical basis for monitoring’ stated by (Soligard et al., 2016)
- we want to look at the green line
- we want to avoid the red line - if you load an athlete before they fully recover, then you could overtrain the athlete, and their capacity to perform will decrease
what is the overall ‘theoretical basis for monitoring’ by (Winds & Gabbett., 2016)
- monitoring is essential to see adaptation, illness, injury, etc…
- modifiable factors change due to fitness and fatigue factors
- take point: the likelihood of injury changes day-to-day which is why coaches do daily training monitoring
what 3 fats are highlighted in the ‘Theoretical Framework of the Training Process’ (Jeffries et al., 2021)
- functional overreaching
- non-functional overreaching
- overtraining
state what it is meant by the key term - functional overreaching (Jeffries et al., 2021)
short term; after rest and recovery, the athlete is then at a higher performance level
state what it is meant by the key term - no-functional overreaching (Jeffries et al., 2021)
where the athlete doesn’t get a performance re-bound after a huge training load - the training stimulus was too great
state what it is meant by the key term - overtraining (Jeffries et al., 2021)
weeks/months/years of a performance deficit due to a workload being applied which is greater than the athlete’s capacity to deal with that load
state what it is meant by the key term - ‘external load measures’
the work completed by the athlete; measured independently of their internal characteristics
state 4 external work load measures
- frequency/time
- power, speed, acceleration
- Global Positioning System (GPS)
- micro-sensor technology (e.g. - accelerometers)
state what it is meant by the key term - ‘internal load measures’
the relative psychophysiological stress elicited by an external workload
state an issue with GPS monitoring
the reliability is influenced by factors such as sample rate, velocity and duration, and type of task. increased velocity = reduced reliability
state 4 internal work load measures
- session RPE
- blood lactate
- biochemical/hormonal/immunological assessments
- HR measures (more weight given to higher HR zones)
what is the formula for sRPE/tRIMP (session RPE)
sRPE load = Duration x sRPE
e.g. - 45 mins x RPE 7 = 315 AU
what is recommended when measuring RPE?
recommended you wait 30 minutes (10 mins at least) post session before you fill it out
state 4 benefits of using RPE
1) can be used for different training styles
2) is valid (HR r = 0.89 and lactate 0.96) and reliable (ICC = 0.99, TE = 4%) - Gabbett., 2007
3) easy and cheap to collect
4) captures both physiological and psychological stress
state 3 issues with the use of RPE
1) compliance/collection issues (e.g. - less likely to be honest if your mates say different scores)
2) doesn’t measure diff components of training stress (e.g. - breathlessness, leg muscle exhaustion, etc…)
3) intensity and duration are merged (10 mins at RPE 10 does not equal 100 mins at RPE 2)
what tools do practitioners use?
- shift to examining external rather than internal load
- caution should be taken (internal adaptation ultimately determines external capacity)
- advantage of external load measuring is that is can allow for better prescription of external load
what did Carl Foster come up with ?
he came up with a ‘Load Injury Relationship’ graph which looked at the loads which athletes performed when/just before they got injured
explain Carl Foster’s graph (2 points)
- he drew a threshold line where, if you went above it, your likelihood of injury increased drastically
- did not predict injury every time, people were still getting injured far below the threshold
what was Tim Gabbet’s graph on injuries in different parts of the season ? (2 points)
- he saw that different phases in the season changed the likelihood of getting injured
- this changed due to the cumulative effect of performing every single week in rugby
what was Tim Gabbett’s graph on loads and injury (3 points)
- saw injury can be due to changes in loads
- saw low load increases have lower chances of injury (10% rule)
- as you increase the change in load, the risk of getting injured increases
explain the difference between ‘acute’ and ‘chronic’ work loads
acute: recent loads (e.g. - one week), analogous to state of ‘fatigue’
chronic: average loads over last 3-6 weeks, analogous to state of ‘fitness’ - 4 weeks used in most literature
how do you work out the ACWR?
- acute = most recent week load AU)
- chronic = average over last 4 weeks (AU)
- acute / chronic = ACWR
e. g. - 200 AU / 125 AU = 1.6 ACWR
what did Gabbett do with regards to the ACWR
did a summary of studies that looked at ACWR. he found a ‘sweet spot’ between 0.8 and 1.3 where injury risk is low. when ratio gets to 1.5 and above (50% or more increase in one week), then the chance of injury increases
this theory has been used by a lot of coaches because it is easy to do and makes a lot of sense
what did (Windt & Gabbett., 2016) do ?
did a study to look at - ‘does fatigue negatively affect risk factors of the lower extremity injury risk profile? a systematic and critical review’
what were the findings to the following study:
‘does fatigue negatively affect risk factors of the lower extremity injury risk profile? a systematic and critical review’ (Windt & Gabbett., 2016)
- reviewed study of fitness in Gaelic football and found that the fitter the athlete, the better they can deal with larger increases in load
- found the stronger the athlete, the better they can deal with larger increases in load
who came up with the following study:
‘Has the athlete trained enough to return to play safely’
‘Has the athlete trained enough to return to play safely’ (Blanch & Gabbett., 2016)
state 4 facts from the following study:
‘Has the athlete trained enough to return to play safely’ (Blanch & Gabbett., 2016)
- a measure of ACWR in high-speed runners
- ever time their injury ent above 1.5, they’d get injured
- when injured, the workload must go down to zero
- if they go back to their previous workload, their ratio goes a lot higher, and therefore will become injured again
what is more reliable, and why:
1) exponentially weighted moving average
2) rolling average
1) exponentially weigher moving average
2) gives more weighting to the more recent training day
3) rolling average takes past 7 days and divides by 7. this means that it doesn’t give more weighting to most recent session, and some days they may not train so their ACWR is 0 - does not mean that they are fully recovered to perform again
state 3 issues with using ACWR research
- mathematical coupling/scaling issues (Lolli et al., 2017)
- optimal time-constraints (Carey et al., 2017)
- categorisation (Carey et al., 2017)
- inappropriate statistical methods (Windt et al., 2018)
- multiple comparisons (Tostein Dalen-Lorenstel et al., 2021)
why is the following an issue with using ACWR research:
mathematical coupling/scaling issues (Lolli et al., 2017)
need to check that it is appropriate to divide numbers by each other which it may not be appropriate to do so with ACWR
why is the following an issue with using ACWR research:
optimal time-constraints (Carey et al., 2017)
commonly use 7 days and 28 days - no reason why you cant be using 5 days and x days etc… you ca tweak that as much as you want until you get a significant effect
why is the following an issue with using ACWR research:
categorisation (Carey et al., 2017)
lots of different was to split into low, moderate, and high data sets. this causes comparison problems
why is the following an issue with using ACWR research:
inappropriate statistical methods (Windt et al., 2018)
some used in research are not appropriate for modelling injury risk
explain the study design to:
‘does load management using the ACWR prevent health problems?’
- randomised cluster trial of 482 elite youth footballers of both sexes
- intervention group had all sessions planned by ACWR
- control group trained as normal
- outcome determined via ‘Also Sports Trauma Research Centre Questionnaire on `health Problems’
what were the findings to:
‘does load management using the ACWR prevent health problems?’
- ACWR works and doesn’t work
- no - results how’s no difference in injuries between the 2 groups
- if you look at serious injuries, the chances are lower in the intervention group (PR = 0.99. p = 0.17) - p value is signifiant so there is potentially something going on
explain the problems of the following study:
‘does load management using the ACWR prevent health problems?’
- control didn’t measure loads at all - may not have had any spikes above 1.5 ACWR
- compliance issues: 10 month study with compliance around 62%
Planning optimal workloads:
- we want to get an athlete from point A to point B
- must do in a safe way (ACWR can be a useful guide)
- adjust loads based on subjective wellness measures
- can use heart rate variability (HRV)
what’s considered a healthy HRV, why?
- variation relents current state of autonomic NS
- greater aviation = parasympathetic NS in control - relaxed state - more able to adjust to environmental changes
- structured = sympathetic NS in control - ridged and less able to respond to changes in the environment
‘Mediators in workload-injury investigations’ (Williams et al., 2017) - explain findings
- injury risk was elevated when HRV was low and workloads were high (unable to cope with increases in work loads)
- people with high HRV and high work loads are able to cope with those higher work loads
who’s study on HRV training did we look at?
(Manresa-Rocamora et al., 2021)
what was the study design to:
‘HRV Guided Training’ (Manresa-Rocamora et al., 2021)
measure HRV every morning and if in normal range = normal training
surpassed HRV - rest day or low intensity/volume day
study did a meta-analysis of multiple athletes training when using HRV
what are the future directions of Training Load Management for Injury management ?
tech improving to measure many more biological factors that are wearable
this will give us a key insight to the integration of both training and match loads as its difficult to measure together right now
tech will also aid out analysis s we don’t have to deal with so many numbers - machine learning methods
