Personal informatics (PI) systems are widely used in various domains such as mental health to provide insights from self-tracking data for behavior change. Users are highly interested in examining relationships from the self-tracking data, but identifying causality is still considered challenging. In this study, we design DeepStress, a PI system that helps users analyze contextual factors causally related to stress. DeepStress leverages a quasi-experimental approach to address potential biases related to confounding factors. To explore the user experience of DeepStress, we conducted a user study and a follow-up diary study using participants’ own self-tracking data collected for 6 weeks. Our results show that DeepStress helps users consider multiple contexts when investigating causalities and use the results to manage their stress in everyday life. We discuss design implications for causality support in PI systems.

DeepStress: Supporting Stressful Context Sensemaking in Personal Informatics Systems Using a Quasi-experimental Approach

AI-powered Digital Therapeutics (DTx) hold potential for enhancing stress prevention by promoting the scalability of P5 Medicine, which may offer users coping skills and improved self-management of mental wellbeing. However, adoption rates remain low, often due to insufficient user and stakeholder involvement during the design phases. 

Human centered design of AI-powered Digital Therapeutics for stress prevention: Perspectives from multi-stakeholders' workshops about the SHIVA solution

Recent efforts to predict stress in the wild using mobile technology have increased; however, the field lacks a common pipeline for assessing the impact of factors such as label encoding and feature selection on prediction performance. This gap hinders replication, especially because of a lack of common guidelines for reporting results or privacy concerns that limit access to open codes and datasets. Our study introduces a common pipeline based on a comprehensive literature review and offers comprehensive evaluations of key pipeline factors, promoting independent reproducibility. Our systematic evaluation aimed to validate the findings of previous studies. We identified overfitting and distribution shifts across users as the major reasons for performance limitations. We used K-EmoPhone, a public dataset, for experimentation and a new public dataset---DeepStress---to validate the findings. Furthermore, our results suggest that researchers should carefully consider temporal order in cross-validation settings. Additionally, self-report labels for target users are key to enhancing performance in user-independent scenarios. 

A Reproducible Stress Prediction Pipeline with Mobile Sensor Data

Counterfactual Explanations in Personal Informatics for Personalized Mental Health Management

Unraveling Temporal Dependencies in Stress: Insights from Self-Reported Stress Data

Smartphones can unobtrusively capture human behavior and contextual data such as user interaction and mobility. Thus far, smartphone sensor data have primarily been used to gain behavioral insights through correlation analysis. This paper provides a tutorial on the causal analysis of human behavior using smartphone sensor data by reviewing well-known matching methods. The key steps of the causal inference pipeline employing matching methods are illustrated using a concrete scenario involving the identification of a causal relationship between phone usage and physical activity. Several practical considerations for conducting causal inferences about human behaviors using smartphone sensor data are also discussed.

A Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data

Digital therapeutics (DTx), in contrast to traditional treatments such as pills, use software installed in smartphones or wearable devices as a medical device to cure diseases and improve health conditions, which represents a significant departure from existing wellness products such as Fitbits. DTx requires clinical validation of efficacy through systematic clinical trials, as do conventional therapeutics. Mobile DTx apps transform conventional treatment approaches such as counseling, self-help, and self-tracking into app-based micro-interventions that can be delivered via notifications, short videos, and chatbots. This article presents a data-driven DTx analytics framework for analyzing and optimizing DTx delivery processes in everyday life contexts by leveraging passive sensor data analysis and human-in-the-loop interaction support.

Data-driven Digital Therapeutics Analytics

Researchers spend lots of time for reading scientific papers as they need to stay updated with recent trends. However, navigating citations, which are indispensable elements of research papers, can act as a barrier for junior researchers as they do not have enough background knowledge and experience. We conduct a formative user study to identify challenges in navigating cited papers. We then prototype QuickRef, an interactive reader that provides additional information about cited papers on the side panel. A preliminary user study documents the usability of QuickRef. Further, we present practical design implications for citation navigation support.

QuickRef: Should I Read Cited Papers for Understanding This Paper?

This paper proposes a process of causal inference using data from mobile devices to better understand the relationships between human behaviors and contexts. A simple case study is presented based on the proposed method and shows the existence of causality in a given sample scenario.

Causal Analytic Process for Mobile Health Data

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