Hurwitz Eric, Butzin-Dozier Zachary, Master Hiral, O'Neil Shawn T, Walden Anita, Holko Michelle, Patel Rena C, Haendel Melissa A
Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Wright Center for Clinical and Translational Research, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA.
medRxiv. 2023 Oct 14:2023.10.13.23296965. doi: 10.1101/2023.10.13.23296965.
Postpartum depression (PPD), afflicting one in seven women, poses a major challenge in maternal health. Existing approaches to detect PPD heavily depend on in-person postpartum visits, leading to cases of the condition being overlooked and untreated. We explored the potential of consumer wearable-derived digital biomarkers for PPD recognition to address this gap. Our study demonstrated that intra-individual machine learning (ML) models developed using these digital biomarkers can discern between pre-pregnancy, pregnancy, postpartum without depression, and postpartum with depression time periods (i.e., PPD diagnosis). When evaluating variable importance, calories burned from the basal metabolic rate (calories BMR) emerged as the digital biomarker most predictive of PPD. To confirm the specificity of our method, we demonstrated that models developed in women without PPD could not accurately classify the PPD-equivalent phase. Prior depression history did not alter model efficacy for PPD recognition. Furthermore, the individualized models demonstrated superior performance compared to a conventional cohort-based model for the detection of PPD, underscoring the effectiveness of our individualized ML approach. This work establishes consumer wearables as a promising avenue for PPD identification. More importantly, it also emphasizes the utility of individualized ML model methodology, potentially transforming early disease detection strategies.
产后抑郁症（PPD）困扰着七分之一的女性，是孕产妇健康领域的一项重大挑战。现有的产后抑郁症检测方法严重依赖产后的当面问诊，导致该病症的一些病例被忽视且未得到治疗。我们探索了利用消费者可穿戴设备获取的数字生物标志物来识别产后抑郁症的潜力，以填补这一空白。我们的研究表明，使用这些数字生物标志物开发的个体内机器学习（ML）模型能够区分孕前、孕期、无抑郁症的产后以及患有抑郁症的产后时期（即产后抑郁症诊断）。在评估变量重要性时，基础代谢率消耗的卡路里（基础代谢率卡路里）成为最能预测产后抑郁症的数字生物标志物。为了确认我们方法的特异性，我们证明了在没有产后抑郁症的女性中开发的模型无法准确分类与产后抑郁症等效的阶段。既往抑郁症病史并未改变模型对产后抑郁症识别的效果。此外，与传统的基于队列的模型相比，个体模型在检测产后抑郁症方面表现更优，突出了我们个体机器学习方法的有效性。这项工作确立了消费者可穿戴设备作为产后抑郁症识别的一个有前景的途径。更重要的是，它还强调了个体机器学习模型方法的实用性，可能会改变早期疾病检测策略。
