Physical inactivity remains a major public health concern, having
associations with adverse health outcomes such as cardiovascular disease and
type-2 diabetes. Mobile health applications present a promising avenue for
low-cost, scalable physical activity promotion, yet often suffer from small
effect sizes and low adherence rates, particularly in comparison to human
coaching. Goal-setting is a critical component of health coaching that has been
underutilized in adaptive algorithms for mobile health interventions. This
paper introduces a modification to the Thompson sampling algorithm that places
emphasis on individualized goal-setting by optimizing personalized reward
functions. As a step towards supporting goal-setting, this paper offers a
balanced approach that can leverage shared structure while optimizing
individual preferences and goals. We prove that our modification incurs only a
constant penalty on the cumulative regret while preserving the sample
complexity benefits of data sharing. In a physical activity simulator, we
demonstrate that our algorithm achieves substantial improvements in cumulative
regret compared to baselines that do not share data or do not optimize for
individualized rewards.

通过改进的 Thompson 采样算法实现对个性化目标的优化和个性化奖励函数的最优化，从而在移动健康应用中达到降低累积遗憾和提高个性化回报的效果。

针对健康行为改变的自适应干预

Adaptive Interventions with User-Defined Goals for Health Behavior  Change

Malaria can be prevented, diagnosed, and treated; however, every year, there
are more than 200 million cases and 200.000 preventable deaths. Malaria remains
a pressing public health concern in low- and middle-income countries,
especially in sub-Saharan Africa. We describe how by means of mobile health
applications, machine-learning-based adaptive interventions can strengthen
malaria surveillance and treatment adherence, increase testing, measure
provider skills and quality of care, improve public health by supporting
front-line workers and patients (e.g., by capacity building and encouraging
behavioral changes, like using bed nets), reduce test stockouts in pharmacies
and clinics and informing public health for policy intervention.

通过移动健康应用，机器学习技术能够强化疟疾监测和治疗依从性，提高疟疾测试率，改善医护人员技能和医疗质量，通过能力建设和鼓励行为改变支持前线工作者和患者，减少药房和诊所的测试缺货并为政策干预提供当地的公共卫生信息。

全球健康的适应性干预：疟疾的案例研究

Adaptive Interventions for Global Health: A Case Study of Malaria

Mobile health applications, including those that track activities such as
exercise, sleep, and diet, are becoming widely used. Accurately predicting
human actions is essential for targeted recommendations that could improve our
health and for personalization of these applications. However, making such
predictions is extremely difficult due to the complexities of human behavior,
which consists of a large number of potential actions that vary over time,
depend on each other, and are periodic. Previous work has not jointly modeled
these dynamics and has largely focused on item consumption patterns instead of
broader types of behaviors such as eating, commuting or exercising. In this
work, we develop a novel statistical model for Time-varying, Interdependent,
and Periodic Action Sequences. Our approach is based on personalized,
multivariate temporal point processes that model time-varying action
propensities through a mixture of Gaussian intensities. Our model captures
short-term and long-term periodic interdependencies between actions through
Hawkes process-based self-excitations. We evaluate our approach on two activity
logging datasets comprising 12 million actions taken by 20 thousand users over
17 months. We demonstrate that our approach allows us to make successful
predictions of future user actions and their timing. Specifically, our model
improves predictions of actions, and their timing, over existing methods across
multiple datasets by up to 156%, and up to 37%, respectively. Performance
improvements are particularly large for relatively rare and periodic actions
such as walking and biking, improving over baselines by up to 256%. This
demonstrates that explicit modeling of dependencies and periodicities in
real-world behavior enables successful predictions of future actions, with
implications for modeling human behavior, app personalization, and targeting of
health interventions.

本文开发了一种基于个性化、多变量时间点过程的新型统计模型，用于对移动健康应用中的人类行为建模，以得出未来用户行为及其时机的准确预测。

建模相互依存和周期性的真实世界动作序列

Modeling Interdependent and Periodic Real-World Action Sequences

Mobile health applications that track activities, such as exercise, sleep,
and diet, are becoming widely used. While these activity tracking applications
have the potential to improve our health, user engagement and retention are
critical factors for their success. However, long-term user engagement patterns
in real-world activity tracking applications are not yet well understood. Here
we study user engagement patterns within a mobile physical activity tracking
application consisting of 115 million logged activities taken by over a million
users over 31 months. Specifically, we show that over 75% of users return and
re-engage with the application after prolonged periods of inactivity, no matter
the duration of the inactivity. We find a surprising result that the
re-engagement usage patterns resemble those of the start of the initial
engagement period, rather than being a simple continuation of the end of the
initial engagement period. This evidence points to a conceptual model of
multiple lives of user engagement, extending the prevalent single life view of
user activity. We demonstrate that these multiple lives occur because the users
have a variety of different primary intents or goals for using the app. We find
evidence for users being more likely to stop using the app once they achieved
their primary intent or goal (e.g., weight loss). However, these users might
return once their original intent resurfaces (e.g., wanting to lose newly
gained weight). Based on insights developed in this work, including a marker of
improved primary intent performance, our prediction models achieve 71% ROC AUC.
Overall, our research has implications for modeling user re-engagement in
health activity tracking applications and has consequences for how
notifications, recommendations as well as gamification can be used to increase
engagement.

本研究研究了移动健康应用程序的用户参与度和保留度，由于用户有多种不同的主要意图或目标使用应用程序，故他们有多种生命。在这项研究中，我们开发了一个改进的预测模型，可以达到 71％ROC AUC，并对如何使用通知，建议以及游戏化来增加用户参与度具有重要影响。