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Training load and injuries in football- lessons from research and practise
- 1. Training load management in football - Lessons learned from research and practice Torstein Dalen-Lorentsen, PhD Research Manager SINTEF Digital Researcher Oslo Sports Trauma Research Center @torsteindalen Fotbollmedicinsk konferens SVFF Stockholm 2023
- 2. Hypothetical Fitness, n of Injuries and Performance Inadequate Low Medium High Excessive Low Moderate High Injuries Fitness Performance Hypothetical training load
- 3. Hypothetical Fitness, n of Injuries and Performance Inadequate Low Medium High Excessive Low Moderate High Performance Hypothetical training load Injuries Fitness Optimal Player A
- 4. Hypothetical Fitness, n of Injuries and Performance Inadequate Low Medium High Excessive Low Moderate High Performance Hypothetical training load Injuries Fitness Optimal Player B
- 5. Hypothetical Fitness, n of Injuries and Performance Inadequate Low Medium High Excessive Low Moderate High Performance Hypothetical training load Injuries Fitness Optimal Player C
- 6. Players A-Z
- 7. Find the optimal amount of training load for each player …For every MonthWeekDaySessionDrill
- 9. How
- 11. 1.Monitoring
- 16. Key variables Cover at least: ‒ External training load Physiological Mechanical ‒ Internal training load Subjective Objective
- 17. 2.Analysis
- 18. 5-10 seasons 25 players 500 min activity per week 20 different variables up to 1000HZ sampling =151 875 000 000 000 000
- 19. 2.Analysis
- 22. Week 1 2 3 4 5 Absolute load 200m sprint 400m sprint 500m sprint 300m sprint
- 23. Week 1 2 3 4 5 Absolute load 200m sprint 400m sprint 500m sprint 300m sprint Cumulative 200m sprint 600m sprint 1100m sprint 1400m sprint
- 24. Week 1 2 3 4 5 Absolute load 200m sprint 400m sprint 500m sprint 300m sprint Cumulative 200m sprint 600m sprint 1100m sprint 1400m sprint Rolling Avg 200m sprint 300m sprint 367m sprint 350m sprint
- 25. | | | | | | | | | | | | | | | | | | | | | | | | | | | | Load week 1 Load week 3 Load week 2 Load week 4 Days 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
- 26. | | | | | | | | | | | | | | | | | | | | | | | | | | | | Load week 1 Load week 3 Load week 2 Load week 4 Days 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Chronic load Acute load
- 27. Absolute load Context Relative load Load history Load constructs
- 28. Week 1 2 3 4 5 Absolute load 200m sprint 400m sprint 500m sprint 300m sprint Relative load Week to week change
- 29. Week 1 2 3 4 5 Absolute load 200m sprint 400m sprint 500m sprint 300m sprint Relative load Week to week change N/A 100% 25% -40%
- 30. | | | | | | | | | | | | | | | | | | | | | | | | | | | | Load week 1 Load week 3 Load week 2 Load week 4 Days 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Chronic load Acute load =Acute:chronic workload ratio (ACWR) Acute Chronic
- 31. Uke 1 2 3 4 5 Absolutt- load 200m sprint 400m sprint 500m sprint 300m sprint X ACWR 350
- 32. Uke 1 2 3 4 5 Absolutt- load 200m sprint 400m sprint 500m sprint 300m sprint 700 sprint X ACWR 350 700/350 = 2
- 34. Decision making
- 35. Wisdom Knowledge Information Data Data Information Knowledge Wisdom (DIKW)-Pyramid
- 38. Wisdom Knowledge Information Data Raw: Red Meaning: South facing traffic light on corner X/X turned red
- 39. Meaning: South facing traffic light on corner X/X turned red Wisdom Knowledge Information Data Raw: Red Context: The traffic ligth I am driving towards has turned red
- 40. Wisdom Knowledge Information Data Raw: Red Context: The traffic ligth I am driving towards has turned red Applied: I’d better stop the car Meaning: South facing traffic light on corner X/X turned red
- 41. Meaning: South facing traffic light on corner X/X turned red Wisdom Knowledge Information Data Raw: Red Context: The traffic ligth I am driving towards has turned red Applied: I’d better stop the car Raw: 500m sprinting Meaning: Player has sprinted 170% more than normal Context: Player has sprinted 200% of her game demand Applied: Player should train less than normal tomorrow
- 42. Wisdom Knowledge Information Data Applied: Player should train less than normal tomorrow
- 43. Applied: Player should train less than normal tomorrow 3.Decision making
- 44. Impellizzeri et al 2019
- 45. Impellizzeri et al 2019
- 47. Feedback In-session adjustment Day-to-day planning Season planning Long-term use Managing an athletes progression from youth team into the senior team Identify periods of increased load or stress that may impact upon injury or performance outcomes Based on the previous session, should tomorrows session be harder or easier for some athletes? Live feedback on player exposure and response Did this session meet our desired training targets, relative to the match demands? West S et al 2020
- 48. Find the optimal amount of training load for each player …For every MonthWeekDaySessionDrill But how?
- 49. Can we calculate the exact amount? Why is load important?
- 50. Why
- 51. Rationale load management Time to adapt = Less fatigue Performance Injuries VS OK - But what is the evidence?
- 52. Blanch & Gabbett 2016
- 53. Training load causes health problems Training load management prevents health problems
- 55. 1 Systematic review of randomised controlled trials (RCT) or high-quality individual RCT 2 Systematic review of cohort studies or high-quality individual cohort study and low quality RCT 3 Systematic review of case-control studies or high-quality individual case-control study 4 Case-series and poor quality cohort and case-control studies 5 Expert opinion Level of evidence ACWR (Oxford centre for evidence-based medicine, 2009)
- 56. 1 Systematic review of randomised controlled trials (RCT) or high-quality individual RCT 2 Systematic review of cohort studies or high-quality individual cohort study and low quality RCT 3 Systematic review of case-control studies or high-quality individual case-control study 4 Case-series and poor quality cohort and case-control studies 5 Expert opinion (n = ) ∞ (Oxford centre for evidence-based medicine, 2009) Level of evidence ACWR
- 57. 1 Systematic review of randomised controlled trials (RCT) or high-quality individual RCT 2 Systematic review of cohort studies or high-quality individual cohort study and low quality RCT 3 Systematic review of case-control studies or high-quality individual case-control study 4 Case-series and poor quality cohort and case-control studies 5 Expert opinion (Oxford centre for evidence-based medicine, 2009) (n = >150) Level of evidence ACWR
- 58. Conceptual problems “As in biology, anatomy dictates physiology. The anatomy (design) of a study dictates what it can and cannot do” Grimes & Schultz, 2002 4 Poor quality cohort studies Impellizzeri et al 2020, Dalen-Lorentsen 2021 Methodological problems
- 59. Mufano et al 2017 Impellizzeri et al 2020 Dalen-Lorentsen et al 2021 No conceptual framework Median n of Incidents = 72 N of analyses N of combinations Biased conclusions >90 % are positive findings Low compliance Missing data Six threats to reproducible science Mufano et al, 2017
- 60. 1 Systematic review of randomised controlled trials (RCT) or high-quality individual RCT 2 Systematic review of cohort studies or high-quality individual cohort study and low quality RCT 3 Systematic review of case-control studies or high-quality individual case-control study 4 Case-series and poor quality cohort and case-control studies 5 Expert opinion Level of evidence ACWR (Oxford centre for evidence-based medicine, 2009) (n = ) ∞ (n = >150)
- 61. 1 Systematic review of randomised controlled trials (RCT) or high-quality individual RCT 2 Systematic review of cohort studies or high-quality individual cohort study and low quality RCT 3 Systematic review of case-control studies or high-quality individual case-control study 4 Case-series and poor quality cohort and case-control studies 5 Expert opinion Level of evidence ACWR (n = 1) (Oxford centre for evidence-based medicine, 2009)
- 62. Training load causes health problems
- 63. 1 Systematic review of randomised controlled trials (RCT) or high-quality individual RCT 2 Systematic review of cohort studies or high-quality individual cohort study and low quality RCT 3 Systematic review of case-control studies or high-quality individual case-control study 4 Case-series and poor quality cohort and case-control studies 5 Expert opinion (Oxford centre for evidence-based medicine, 2009) Status
- 64. Why?
- 65. Training load causes health problems No evidence…. But Bittencourt et al 2016 sRPE Total distance Sprint distance Accelerations High intensity actions Playing style Previous injury Age Muscle strength Mental state Fitness
- 66. Training load causes health problems Training load management prevents health problem
- 67. One-size-fits-all-approach with ACWR No evidence….BUT
- 69. Can load management prevent health problems?
- 71. West et al., 2020
- 72. West et al., 2020
- 73. >
- 74. Balance Training load Contextual factors
- 75. Inform decision making Who is starting the next match? Is this player ready to return from injury? How should we train this week?
- 76. ?
- 79. What load management should be used for • Should be used for: ‒ Planning and control of training Inform training process decisions together with many other factors • Can not be used for: ‒ Accurate injury prediction ‒ Holy grail of injury prevention
- 80. Det är fysioterapeuter och läkare som jobbar med, eller är intresserade av, fotbollsmedicin. Det är lite mixad kompetens, vissa har jobbat länge inom elitverksamhet fotboll, andra är nyexaminerade och har intresse mot idrotts/fotbollsmedicin. Du kan hålla en ’hög nivå’ på presentationen. Sikta på max 30 min presentation så att vi har tid för diskussion – jag är säker på att det kommer bli många frågor från auditoriet. Praktiska erfarenheter och evidens gällande belastningsstyrning i fotboll (vi kan bolla en exakt titel). Teknologi for et bedre samfunn
- 81. • Intro • How • Why • Where to now? 1. Why? ‒ Underlying theory, principles and rationale for load measurement/management 2. How? ‒ Long- and short-term planning ‒ Longitudinal analysis 3. Where to now? ‒ Limitations & discussion
- 83. 2018-season February November Control group Intervention group Health problem registration
- 84. Participation Performance Training volume Symptoms Clarsen et al., 2013 No health problem Health problem Substantial health problem Vs Intervention Control 25%
- 86. Intervention - Coaches 0.8-1.3 «sweet spot concept» Blanch & Gabbett 2016
- 87. Intervention - Players 75 min * 6 RPE = 450
- 90. Invited (N=63 teams) Intervention group (N=11 teams) Control group (N=14 teams) Full analysis set N=394 Participants 25 Difference in health problems prevalence and substantial health problems Generalised estimating equation
- 91. Participants n = 230 17.6 (1) Years old n = 164 17 (1.3) Years old
- 92. 2 475 69% (10-100) 15 253 74% (0-100) Compliance
- 93. Compliance - intervention • Yes, every week 62.5% (n=5) • No, every other week 12.5% (n=1) • No, once per month 25% (n=2)
- 94. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 3 4 5 6 7 8 9 10 Prevalence (%) Month Control Intervention Alle health problems Control: 64.2% (95% CI 60.4% to 67.7%) Intervention: 65.8% (95% CI 61.4% to 70.2%) No effect Dalen-Lorentsen et al 2021
- 95. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 3 4 5 6 7 8 9 10 Prevalence (%) Month Control Intervention Substantial health problems Control: 35.7% (95% CI 32.1% to 39.4%) Intervention: 31.1% (95% CI 27.4% to 36.1%) No effect Dalen-Lorentsen et al 2021
- 96. Methodological considerations • Compliance to the intervention measure Health problem registration
Editor's Notes
- Probably wondering why i chose this picture as the backgroun.d and in addition to being a very nice picture from norway, it is also very relevant to training load management. I will get back to exacly why towards the end. But first - lets dive into how we work with training load management
- How does injuries, fitness and performance relate to training load
- How?
- The actual work that is performed Physiological – locomotive actions, everything related to metabolism Mechanical – start/stop/cutting action, everything related to ground reaction forces Internal – response to the external
- Raw data into tangible insights we can use
- When we have control of these data. Next step is to include context and turn them into wisdom. This must be done before decision making is performed.
- DIKW pyramid
- Lets say that a self driving tesla approaching an intersection What does the car need to know?
- Not only on a day to day basis
- How?
- ACWR as an example. Extremely popular topic and were hyped by major journals and on social media. From this period, there were two major claims.
- Training load causes health problems And that by changing the training load, you could prevent health problems. In this talk, I will look at the evidence behind both of these
- So what’s the evidence.
- When assessing evidence - the level of evidence pyramid is a good way to start
- By using this, we find that most articles are actually in the bottom level, often as editorials without any data
- There are also a large number of prospective cohort-studies. But, as our group and many others have pointed out, these have major limitations. These weaknesses can broadly be divided into two groups, conceptual and methodological problems
- To quote a brilliant paper from grimes and Shultz. “As in biology, anatomy dictates physiology. The anatomy (design) of a study dictates what it can and cannot do” and as these studies are mostly observational without any conceptual framework , they cannot assess causality. There is also a lack of both theoretical and conceptual model, which leaves the researcher with endless degrees of freedom in their design and analysis
- In a paper called a manif
- To summarise, the evidence behind the claim training load causes health problems is more or less non-existing
- The only way to examine preventive effect is to use an experimental design. And as there were no RCTs in this field of research, we aimed to test the preventive effect of load management using ACWR, using a cluster randomised design
- This is where we are at, a lot of positives from the editorials. Positive associations reported from cohort studies that lack both the methodological quality and the study design for assessing causality, and a negative answer from one RCT.
- But it all makes so much sense. Why cant we seem to find a causal link?
- As demonstrated by Bittencourt, health problems are a complex and dynamic outcome that is influenced by a multitude of factors. There is no doubt that training load plays a part in this complex puzzle of factors, but how, and by which magnitude is currently not answered in the litterature
- Over to the next claim. Based on the only paper who has investigated this, using a one size fits all approach and ACWR, then NO
- One of the reasons migth be the complexity of the training load an health problem relationship
- Load small piece of the puzle. MANY contextual factors
- In this paper led by Stephen West, we aimed to illustrate the complexity of contextual factors that inform player management
- These are grouped into team-level factors. Such as the content of training session or the context of match. Environmental factors like temperature and surface. And factors on a player-level.
- Training load is highlighted in a yellow box to demonstrate it is only a small part of the overall picture. With all these factors, many of which is not possible to measure, nevermind trying to use them in an RCT, I think its fair to say
- So, altogether, with all the individual contextual factors and the balance of risk versus reward, I think its fair to say that training load management still is more an art than a science
- There is no exact calculation to know this
- Other non-measurable factors
- Other non-measurable factors
- Remember one thing from this presentation, its this picture. Training load management can be seen in the same way we build roads up mountains .it might be tempting to go straigth up from the ferry dock at the bottom, but that would be too steep and probably not safe. In stead, we follow a steady progression all the way to the top. And by going this way, we give the players time to adapt along we go and get everyone with us to the mountain top.
- The contextual factors are numerous, but as Martin Buchheit pointed out in this brilliant blog, when making decisions on player management, content is king, but contex is god So we really need to take them into consideration
- Both the intervention group and the control group registered their weekly health problem prevalence once per month, using the oslo sports trauma research center questionnaire on health problems
- This questionnaire consist of four questions surrounding participation, training volume, performance and symptoms. Based on the answers, the players were catergorised into three groups, no health problem, health problem and substantial health problems. We calculated the prevalence of both health problems and substantial health problems by dividing the number of players in each of these categories to the total amount of players. So, if you have sixteen players and four of them reports a health problem. The health problem prevalence would be 25% for that week. To investigate the effect of the intervention, we compared the prevalence between the groups
- The intervention group also received the intervention that consisted of load management using ACWR
- The load management programme was based on this editorial that outlined this figure including the sweet spot concept. Meaning training should be planned to follow an ratio between these thresholds
- The players registered their duration and rating of perceived exertion for all footballing activity, which we calculated to a combined session rpe score
- This score was then automatically updated in the coaches dashboard in the athlete management system, as we can see here. The coaches could then use the training load to make a training plan within the thresholds. If a player were planned to have a higher ACWR than the upper threshold, he was then marked in red, as player 20 here
- And a suggestion appeared to the coach to decrease the load accoringly
- We invited 63 teams of which 25 agreed to be randomised into the two groups. When excluding all players who did not respond to any health questionnaires, we ended up with a full analysis set of almost 400 players. We used a Generalised estimating equations panel data models to analyse the between-group difference in prevalence of health problems and substantial health problems
- The players were the best under 19- players of both sexes, with an average age of 17 years
- We recorded two and a half thousand health reports which gave us a compliance of 69 % We received more than 15 000 training session which were 74% of all planned activity
- In a post study questionnaire, we asked the coaches if they had used the programme as intended every week of the season, 63 % answered yes and the remaining 30 percent said they had used it less frequently
- Over to the main results. On the Y-axis you will see the health problem prevalence, the proportion of players in each group that reported a health problem at each time point. The solid line is the intervention group and the grey is the control group As we can see, these lines followed each other quite closely and we concluded that the intervention had no effect
- The same for substantial health problems. Also here, the groups followed each other throughout the season and we observed no effect of the intervention
- The main limitation of this study is the lack of an quantitative assessment of compliance to the intervention Whereas one of the strengths was the broad health problem registration that enabled us to capture overuse injuries, which probably is the most likely health problem group to be prevented by load management