Multifaceted risk analysis made simple
This dashboard provides a simple and multifaceted analysis of wellness, screening, testing, training, competition metrics and health risk factors to monitor individual response to training. This dashboard simplify the monitoring of the Training Stress Balance (TSB) and immediately identify athletes who are at risk of injury (red), those who aren’t coping well with their workload (yellow), those who are not training at their full potential (blue), and those adapting well to the workload and ready to perform (green).
The dashboard is updated in real-time.
The athlete background color indicates the overal level of risk/problems. To open the individual dashboard and access individual alerts and recommendations, click on the athlete name.
To prevent the opening of the individual dashboard, set the 'Enable invidual dashboard' to 'No'
Customizing the Dashboard
The dashboard can be customized at any time to match your needs
Step 1 – Select the dashboard settings, colors and tabs
Team settings->Readiness Dashboard
Default colors
To change colors, click on the small colored square and pick a different color, or click on the color code and enter another one.
Step 2- Select the metrics to display on the dashboard
Scroll down the Training, Wellness, Health, Menstrual Cycle Tracking, Health, Injury Surveillance (OSTRC), Assessments and Questionnaires sections, and set the metrics you'd like to display to Show.
Step 3 – Select how markers are interpreted
Go to Team settings->Training forms->Custom fields
To change the ACWR formula used with the Internal Load (sRPE), select an option in the ACWR formula (sRPE) list.
To change the ACWR formula used with each custom field, select an option in the ACRW list displayed in the field section.
Calculation formulas
1) Daily z-score: Calculates daily z-score and compare it to the athlete's 4-weeks daily average. Ideal when you need to identify fluctuations on a daily basis.
2) 3-7d:14-42d ratio: Divides the last 3-7 days cumulative load to the last 14-42 day rolling average.
2) 3-7d:14-42d ratio: Divides the last 3-7 days cumulative load to the last 14-42 day rolling average.
3) 3-7d:14-42d ratio EWMA: Divides the last 3-7 days exponentially weighted moving average to the last 14-42
exponentially weighted moving average.
How to pick the best option
In most situations, using a 7d:28d ACWR will provide optimum risk analysis. Using shorter periods can be useful with very busy competition schedules and/or when athletes are returning from injuries and need a shorter risk assessment.
Wellness score
The daily wellness score is calculated by adding and converting in percentage the points associated with each answer. A total percentage score is given for each category of questions, and the average percentage score of all categories is used to calculate the overall wellness score.
Health
Red color indicates that the athlete is currently injured or has an ongoing health issue
Scientific background
The dashboard combines athletes’ individual daily wellness score, health status and evidence-based workload metrics linked to the apparition of injuries (acute:chronic workload ratios -ACWR- and week-to-week load increase) to provide an immediate global picture of athletes’ level of risk and level of readiness (represented by the color of the first column).
How does it work?
The dashboard is updated in real-time using both data planned by coaches and self-reported by athletes.
Dashboard Items
The dashboard is pre-configured with internal load, wellness and health metrics proven to be effective markers of athlete readiness. These items are presented below. The dashboard can also be customized in Team settings - Readiness Dashboard to include the metrics and calculations of your choice.
Default dashboard options
sRPE Load = Internal load calculated by multiplying Session RPE x duration measured during training and competition
The dashboard displays ACWR for the past 7 days and the next 7 days (use measured and predicted loads), as well as the cumulative load calculated for the same period (displayed between parenthesis)
How it's calculated
Method 1: When using Rolling average or EWMA options
Acute load =3-7 day cumulative load (uses reported load for the previous 7 days, including today, and planned load for the next 7 days)
Chronic load= 14-48 days (2-6 weeks) workload rolling average or exponentially weighted moving average
Acute:chronic workload ratio (ACWR) = acute load /chronic load
Method 2: When using Curr week / Next week option
Acute load =1 week cumulative load (based on reported and planned load for the week)
Chronic load =4 weeks rolling workload average
Acute:chronic workload ratio (ACWR) = acute load /chronic load
Note: Acute, chronic period duration and calculation methods can be selected for each field in Team settings-Training Forms
Example:
Last 4 weeks average WL=3500
Current week WL =4500
ACWR = 4500/3500=1.28
Note: When you add custom fields to the dashboard, ACWR is calculated using the same approach.
Week to week load increase (W-T-W Load Inc.)
Workload changes from last week to the current one (in percentage)
% of WL change = ((curr week - last week) / last week)*100
Example:
Last week WL=1500
Current week WL =3500
% of WL change = ((1500 - 3500) / 3500)*100= -57%
The dashboard displays W-T-W LC for the current week and the next
Interpreting Dashboard Data
Note: colors and terminology may change based on your Team settings-Readiness Dashboard configuration
ACWR | Risk of injury | Meaning | What to do | |
1.5 | Very high | This week WL is excessively high compared to the last 4 weeks. Very high level of fatigue is expected. Risk of injury is increased. | Reduce WL as soon as possible and aim to bring load back to the green zone (optimal zone). Also reduce next week’s planned WL a large proportion of injuries occurred 1 week after a spike in acute workload | |
1.31-1.49 | High | This week WL is high compared to the last 4 weeks. High level of fatigue expected | Acute workload is high in comparison to previous 4 weeks: DO NOT increase this week WL and keep an eye on wellness markers to optimize load based on individual response | |
0.1-0.79 | Undertraining/medium risk | Acute workload is substantially lower than previous 4 weeks. Very high level of freshness is expected | This week is good if you are deliberately attempting to freshen up your players. However note this “light” week will lower your 4 week chronic load from next week Keep an eye on wellness markers to optimize load based on individual response | |
0.8-1.3 | Optimal load | Risk of injury is minimised. | Keep an eye on wellness and week to week load changes |
Week-to-Week Load Changes | Risk level | Meaning | What to do | |
≥15% | Very high load increase | Increase in WL may be excessive and may overwhelm the athlete’s ability to adapt | Reduce this week WL so it is increasing at a rate less than 10% per week | |
≥10% | High load increase | Increase in WL is greater than recommended | Reduce this week WL so it doesn’t increase more than 10% from last week | |
<10% | Optimal load change | WL change is in recommended zone | Keep an eye on wellness and week to week load changes |
Wellness Score | Risk level | Meaning | What to do | |
≥80% | High | Today’s wellness score reflects major issues. | Verify the causes of wellness issues (stress? Sleep?, etc) and reduce this week WL until wellness score returns to optimal zone. | |
≥60% | Medium | Today’s wellness score reflects some issues. | Verify the causes of wellness issues (stress? Sleep?, etc) and reduce this week WL until wellness score returns to optimal zone. | |
<60% | Optimal wellness response | Wellness is in recommended zone | Keep an eye on wellness and week to week load changes |
Further readings
- Hulin B et al.: The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players Br J Sports Med doi:10.1136/bjsports-2015-094817, 2015
- Hulin BT, Gabbett TJ, Blanch P, et al: Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers, Br J Sports Med;48:708-712, 2014.
- Gabbett TJ.The training injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med; 50:273–280, 2016.
- Anna E Saw et al: Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review, Br J Sports Med ;50:281-291, 2016