Low Chronic Workload and the Acute:chronic Workload Ratio Are More Predictive of Injury Than Between-Match Recovery Time: A Two-Season Prospective Cohort Study in Elite Rugby League Players
Initiatives
-
Between-match recovery time, and acute and chronic workloads likely affect subsequent match injury risk in elite rugby league players.
Note: All published information has been collected from the article referenced in the Marker Paper box below. Therefore, there may be variations with more advanced versions of the study.
- Start Year
- 2016
- Funding
- Technical or equipment support for this study was not provided by any outside companies, manufacturers or organisations. BTH was funded by a postgraduate research scholarship supported by the University of Wollongong and the St. George Illawarra Dragons Rugby League Football Club.
Design
- Study design
- Population cohort
Marker Paper
Hulin BT, Gabbett TJ, Caputi P, Lawson DW, Sampson JA. Low chronic workload and the acute:chronic workload ratio are more predictive of injury than between-match recovery time: a two-season prospective cohort study in elite rugby league players. Br J Sports Med. 2016;50(16):1008‐1012. doi:10.1136/bjsports-2015-095364
PUBMED 26851288
Recruitment
- Sources of Recruitment
-
- Individuals
Number of participants
- Number of participants
- 28
- Number of participants with biosamples
Access
Availability of data and biosamples
Data | |
Biosamples | |
Other |
Timeline
rugby players
Workloads of 28 players throughout two seasons were calculated during short (<7 days), and long (≥7 days) between-match recovery times. ‘Acute’ workloads (1 week) greater than ‘chronic’ workloads (4-week rolling average acute workload) resulted in acute: chronic workload ratios above 1.
Selection Criteria
- Newborns
- Twins
- Countries
-
- Australia
- Ethnic Origin
-
- Health Status
-
Recruitment
- Sources of recruitment
-
- General population
Number of participants
- Number of participants
- 28
- Number of participants with biosamples
Data Collection Event
- Start Date
-
2016
- End Date
-
2016
- Data sources
-
-
Geospatial technology
- Global navigation satellite systems (GNSS) (e.g. GPS, GLONASS, Galileo, etc.)
-
Geospatial technology