Utilizing a Novel 2D Image Processing System for Relating Body Composition Metrics to Performance in Collegiate Female Rowers

Abstract

The purpose of this study was to determine if rowing performance was associated with fat mass (FM) or fat-free mass (FFM) measured using a novel 2D digital image analysis system. Nineteen female rowers (ages = 20.3 ± 1.0 years, weight = 73.8 ± 8.3 kg, height = 172.7 ± 4.7 cm) participated in this study. FM and FFM were estimated with a smartphone application that uses an automated 2D image analysis program. Rowing performance was measured using a 2 km (2k) timed trial on an indoor ergometer. The average speed of the timed trial was recorded in raw units (m·s−1) and adjusted for body weight (m·s−1·kg−1). FFM was significantly correlated to unadjusted 2k speed (r = 0.67, p < 0.05), but not for FM (r = 0.44, p > 0.05). When 2k speed was adjusted to account for body weight, significant correlations were found with FM (r = −0.56, p < 0.05), but not FFM (r = −0.34, p > 0.05). These data indicate that both FM and FFM are related to rowing performance in female athletes, but the significance of the relationships is dependent on overall body mass. In addition, the novel 2D imaging system appears to be a suitable field technique when relating body composition to rowing performance.

Body composition estimates using a 2D image analysis system across different environmental conditions: An agreement study

“…Participants wore tight-fitting athletic clothing that allows for the automated IMAGE analysis program to identify the necessary anatomical points of interest [7,9,14]. Participants with long hair were instructed to pull their hair "back" and "up" to allow the digital image to capture the width of the neck.…”

“…Participants stood with their feet flat in front of a white photography background facing away from the digital camera, with weight evenly distributed on both feet. The heels were placed together with the feet pointed slightly outward at a 60-degree angle [7,9,14]. Participants were instructed to remain still with arms abducted at a 45-degree angle away from the torso and aligned within the coronal plane, with palms facing away from the camera.…”

“…Standardizing body composition assessment protocols is important to ensure accuracy and agreement between measurements [4][5][6]. However, most of the research is directed toward studying their validity under tightly controlled conditions within a laboratory setting [7][8][9][10]. For instance, participants are often required to undergo 12-hour food fasts, wear specific clothing, and refrain from exercise prior to testing [4][5][6].…”

“…Recently various smartphone applications have also been developed to provide an estimate of body composition from a single two-dimensional (2D) image (IMAGE) based on a 2D rendering of a person in a standing position wearing form-fitting clothing utilizing embedded algorithms [12,13]. Once the image is processed by the application, estimates of body fat percentage (%Fat), fat mass (FM), and fat-free mass (FFM) are presented on the smartphone screen [7,9,14]. While the built-in algorithms are proprietary and not available to the public, according to previous validation research, the conversion of body composition metrics may first involve prediction of body volume from linear diameter measurements [7,9,14].…”

“…Once the image is processed by the application, estimates of body fat percentage (%Fat), fat mass (FM), and fat-free mass (FFM) are presented on the smartphone screen [7,9,14]. While the built-in algorithms are proprietary and not available to the public, according to previous validation research, the conversion of body composition metrics may first involve prediction of body volume from linear diameter measurements [7,9,14]. For example, a previous study demonstrated that the body volume estimates from the IMAGE were strongly correlated with hydrostatic weighing (r = 0.99, p < 0.001) [9].…”