The concept of Quality of Life (QoL) refers to a holistic measurement of an individual's well-being, incorporating psychological and social aspects. Pregnant women, especially those with obesity and stress, often experience lower QoL. Physical activity (PA) has shown the potential to enhance the QoL. However, pregnant women who are overweight and obese rarely meet the recommended level of PA. Studies have investigated the relationship between PA and QoL during pregnancy using correlation-based approaches. These methods aim to discover spurious correlations between variables rather than causal relationships. Besides, the existing methods mainly rely on physical activity parameters and neglect the use of different factors such as maternal (medical) history and context data, leading to biased estimates. Furthermore, the estimations lack an understanding of mediators and counterfactual scenarios that might affect them. In this paper, we investigate the causal relationship between being physically active (treatment variable) and the QoL (outcome) during pregnancy and postpartum. To estimate the causal effect, we develop a Causal Machine Learning method, integrating causal discovery and causal inference components. The data for our investigation is derived from a long-term wearable-based health monitoring study focusing on overweight and obese pregnant women. The machine learning (meta-learner) estimation technique is used to estimate the causal effect. Our result shows that performing adequate physical activity during pregnancy and postpartum improves the QoL by units of 7.3 and 3.4 on average in physical health and psychological domains, respectively. In the final step, four refutation analysis techniques are employed to validate our estimation.

翻译：生活质量（QoL）概念是指对个体全面福祉的综合测量，涵盖心理和社会维度。肥胖与压力较大的孕妇群体常报告较低的生活质量。身体活动（PA）已被证实具有改善生活质量的潜力，但超重和肥胖孕妇普遍未能达到推荐的身体活动水平。现有研究多采用基于相关性的方法探究孕期PA与QoL的关联，这类方法更易发现变量间的虚假关联而难以揭示因果关系。此外，现有方法主要依赖身体活动参数，忽视孕产妇病史与情境数据等因素，导致估计偏差。更关键的是，这些估计缺乏对中介变量及反事实情景的解析。本文研究了孕期及产后身体活动（处理变量）与生活质量（结果变量）间的因果关系。为估计因果效应，我们提出一种整合因果发现与因果推断组件的因果机器学习方法。研究数据来自针对超重和肥胖孕妇的长期可穿戴健康监测项目，采用机器学习元学习技术进行因果效应估计。结果表明：孕期及产后保持充足身体活动可使生理健康领域和生活质量评分平均提高7.3个单位，心理领域提高3.4个单位。最终，我们采用四项反驳分析技术验证了估计结果的稳健性。