Can we predict the neutral breast position using the gravity-loaded breast position, age, anthropometrics and breast composition data?. (October 2022)

• Record Type: Journal Article
• Authors: Norris, Michelle; O'Neill, Aoife; Blackmore, Tim; Mills, Chris; Sanchez, Amy; Brown, Nicola; Wakefield-Scurr, Joanna
• Is Part Of: Clinical biomechanics. Volume 99(2022)
• Publication Date: 2022-10
• Subjects: Breast composition -- Skin strain -- Breast position -- Garment development; Biomechanics -- Periodicals; Osteopathic medicine -- Periodicals; Biomechanics -- Periodicals; Osteopathic Medicine -- Periodicals; 612.76
• DOI: 10.1016/j.clinbiomech.2022.105760
• Languages: English
• ISSNs: 0268-0033

Abstract

Abstract: Background: This study aimed to identify the predictor variables which account for neutral breast position variance using a full independent variable dataset (the gravity-loaded breast position, age and anthropometrics, and magnetic resonance imaging breast composition data), and a simplified independent variable dataset (magnetic resonance imaging breast composition data excluded). Methods: Breast position (three-dimensional neutral and static gravity-loaded), age, anthropometrics and magnetic resonance imaging breast composition data were collected for 80 females (bra size 32A to 38D). Correlations between the neutral breast position and the gravity-loaded breast position, age, anthropometrics, and magnetic resonance imaging breast composition data were assessed. Multiple linear and multivariate multiple regression models were utilised to predict neutral breast positions, with mean absolute differences and root mean square error comparing observed and predicted neutral breast positions. Findings: Breast volume was the only breast composition variable to contribute as a predictor of the neutral breast position. While ≥69% of the variance in the anteroposterior and mediolateral neutral breast positions were accounted for utilising the gravity-loaded breast position, multivariate multiple regression modelling resulted in mean absolute differences >5 mm. Interpretation: Due to the marginal contribution of breast composition data, a full independent variable dataset may be unnecessary for this application. Additionally, the gravity-loaded breast position, age, anthropometrics, and breast composition data do not successfully predict the neutral breast position. Incorporation of the neutral breast position into breast support garments may enhance bra development. However, further identification of variables which predict the neutral breast position is required. Highlights: Neutral breast position prediction may help limit excessive breast skin strain. Breast composition marginally contributes to neutral breast position prediction. A full independent variable dataset may be unnecessary for this application. The identified variables do not successfully predict the neutral breast position.