university’s men’s and women’s rugby and ultimate frisbee teams, as well as the women’s lacrosse team. Athlete experience ranged from novice to experienced and their average age was approximately 20 years old.
Injury surveillance was conducted by weekly online questionnaires that were emailed to athletes to identify if an injury occurred during the previous week and to determine the participation level. Injury was defined for the athletes as “a physical complaint (pain, discomfort, etc.) that resulted from a team’s practice or competition, whether or not medical treatment was sought”. Questions were developed based on previous studies to gather information on injury mechanism, classification, types, and treatment. A geo-referenced alphanumeric grid (3 square meter [32.3 sq. ft,] cells) was created over each field using ArcMap and provided in the questionnaires to report where in the field an injury occurred. Corresponding letter and number signs were placed along a fence in the fields to aid the athletes in identifying each grid. Upon the study’s completion, triangular consensus validation was conducted between three investigators to discuss the validity of the self-reported injuries and to decide which injuries to keep for analysis. Only ground- derived injuries were considered, excluding such injuries caused by contact with another athlete or other objects. We included acute and overuse injuries since contact with the ground was either acute or repetitive over time.
Soil moisture (volumetric water content) and turfgrass quality (normalized difference vegetation index; NDVI) were measured either weekly or bi-weekly with the Toro Precision Sense 6000. Te PS6000 is a mobile, multi-sensor data acquisition unit that is towed behind a utility vehicle. It simultaneously measured both field properties while traversing the fields. Surface hardness and turfgrass shear strength (i.e. rotational traction) of the fields were measured bi-weekly during the study with handheld data acquisitions.
Hot Spot maps were created for all field properties each
Te illustration of the geo-referenced alphanumeric grid (3 square meter cells) that was provided to athletes in questionnaires to report where injuries occurred on the field (corresponding letter and number signs hung along a fence in the fields).
month using ArcMap to identify within-field variability of each field property by first interpolating the geo-referenced data points. Tis analysis takes the local sum of a cell and all its neighbors and compares it proportionally to the sum of all cells on the field. Te result is a Gi* statistic for each individual cell that can be used to assess significance. A positive and significant Gi* indicates a “hot” spot (i.e. a cell with a high value relative to all cells) and a negative and significant Gi* indicates a “cold” spot (i.e. a cell with a low value relative to all cells). Te monthly hot and/or cold proportions from both fields were averaged in order to have an overall expected proportion for each field property. Only months when an injury occurred were considered
Te Toro Precision Sense 6000 is used for measuring soil moisture (volumet- ric water content) and turfgrass quality (normalized difference vegetation index), as well as geo-referencing all data.
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Twenty-three ground-derived injuries were reported during this two-year study. Several occurred in practice (16/23; 70 percent), rather than games (6/23; 26 percent), and one was reported as “other.” Tey were mainly to the
TPI Turf News July/August 2018
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