Digital Behavior Change Interventions (DBCIs) are supporting development of
new health behaviors. Evaluating their effectiveness is crucial for their
improvement and understanding of success factors. However, comprehensive
guidance for developers, particularly in small-scale studies with ethical
constraints, is limited. Building on the CAPABLE project, this study aims to
define effective engagement with DBCIs for supporting cancer patients in
enhancing their quality of life. We identify metrics for measuring engagement,
explore the interest of both patients and clinicians in DBCIs, and propose
hypotheses for assessing the impact of DBCIs in such contexts. Our findings
suggest that clinician prescriptions significantly increase sustained
engagement with mobile DBCIs. In addition, while one weekly engagement with a
DBCI is sufficient to maintain well-being, transitioning from extrinsic to
intrinsic motivation may require a higher level of engagement.

数字行为变化干预（DBCIs）在支持新的健康行为方面发挥作用。本研究旨在通过识别衡量参与度的度量标准、探索患者和临床医生对 DBCIs 的兴趣，并提出在此类情境中评估 DBCIs 影响的假设，为支持癌症患者提高生活质量定义有效的 DBCIs 参与方式。研究结果表明，临床医生的指示显著增加了与移动 DBCIs 的持续参与。此外，尽管每周一次的 DBCIs 参与足以维持幸福感，但从外部动机转变为内在动机可能需要更高水平的参与。

通过移动数字行为改变干预提高癌症患者幸福感的有效参与定义

Defining Effective Engagement For Enhancing Cancer Patients' Well-being  with Mobile Digital Behavior Change Interventions

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.

研究分析了孕期和产后进行足够的体育活动对生活质量的影响，结果显示孕期和产后进行足够的体育活动可使身体健康和心理健康领域的生活质量分别提高 7.3 和 3.4 个单位，通过结合因果发现和因果推断方法，我们开发了一种因果机器学习方法，并采用四种辨伪分析技术验证了我们的估计结果。

孕期身体活动对生活质量的影响：一种因果机器学习方法研究

Impact of Physical Activity on Quality of Life During Pregnancy: A  Causal ML Approach

The latest cancer statistics indicate a decrease in cancer-related mortality.
However, due to the growing and ageing population, the absolute number of
people living with cancer is set to keep increasing. This paper presents
ASCAPE, an open AI infrastructure that takes advantage of the recent advances
in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer
patients quality of life (QoL). With ASCAPE health stakeholders (e.g.
hospitals) can locally process their private medical data and then share the
produced knowledge (ML models) through the open AI infrastructure.

癌症相关死亡率下降，但由于人口增长和老龄化，罹患癌症的人数不断增加。本文介绍了 ASCAPE，一种利用人工智能和机器学习的最新进展来支持癌症患者生活质量的开放式 AI 基础设施。通过 ASCAPE，医疗健康利益相关者（如医院）可以在本地处理私人医疗数据，然后通过开放式 AI 基础设施分享产生的知识（机器学习模型）。