Research Motivation
Since starting university, I have been deeply interested in how theories and technologies can assist in regulating emotions and behaviors, thereby promoting achievement and positive emotions conducive to well-being. In my graduation thesis, I focused on two typical aspects of self-regulation: anxiety and procrastination. Using the Experience Sampling Method (ESM) and multilevel logistic regression analysis, I discovered that procrastination and anxiety are influenced by moderating variables, with a positive predictive relationship particularly when the expectation of success is low.
My thesis work highlighted the need for information technologies to capture dynamic relationships and develop practical interventions for self-regulation. Information technologies offer the potential to acquire meaningful data to understand an individual’s cognitive, emotional, behavioral, and situational states, including environmental factors like natural surroundings and social contexts. Therefore they aid in identification, prediction, and intervention to support decision-making and appropriate behaviors, with enhanced accuracy from incorporating environmental data such as air quality, noise levels, and social interactions.
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Building on these possibilities, my current interest focuses on utilizing information technology to enhance self-regulation and promote well-being. By integrating environmental factors into technological solutions, we can develop effective interventions to improve mental health and quality of life, acknowledging that both technology and environment significantly influence well-being.
Study and Research Themes
I hope to study the following themes after enrollment to better use information technology to aid self-regulation and promote well-being:
Enhanced Data Sources. I aim to use mobile and wearable devices for passive data collection and employ spatial big data and spatiotemporal analysis for more ecological and instant data. Wearable and mobile devices, through digital sensor technology, can monitor individuals’ daily activities in real-time, such as steps taken, heart rate, and sleep quality. This technology provides multi-modal data collection, enabling analysis and understanding of individual health status from multiple dimensions. Compared to traditional methods, wearable devices can continuously collect data in natural environments, enhancing the ecological validity of research. They also offer significant cost-efficiency, making large-scale deployment feasible. Using spatial big data and spatiotemporal analysis can further enrich data sources, providing comprehensive insights into the interactions between individuals and their environments.
Understanding Environment and Context. To better understand user behavior and states, I hope to study context-aware technologies to identify current contexts from data, with a focus on environmental impacts, particularly how urban spaces affect individual mental health and behavior. Context-aware technology can analyze and respond to environmental changes in real-time, providing intelligent and personalized services, thus enhancing user experience. Unlike traditional methods, context-aware systems can continuously collect and process data, predict future scenarios, and take preemptive measures. By analyzing current environmental data and user behavior, context-aware technology offers smarter, safer, and more comfortable user experiences, meeting personalized needs, and improving well-being and self-regulation capabilities. Additionally, studying the impact of urban spaces can shed light on how different environments influence mental health and behavior.
Broadening Research Perspectives: From Individual to Group. I plan to expand my research focus from individual self-regulation to group behavior and collective health, considering both internal dynamics and the influence of the environment on groups. Researching from a team perspective can fill the gaps left by individual-focused studies, providing insights into collective benefits such as overall health improvements. Interactions and social support within teams can enhance motivation and participation, helping individuals overcome obstacles and maintain healthy behaviors. Compared to individual perspectives, team perspectives highlight the importance of social connections and can help design interventions that meet diverse needs. By also considering the impact of the environment on group dynamics, we can better understand how spatial interactions influence collective well-being.
Preliminary Research Plan
Here is a preliminary research plan, which will be refined after enrolling and studying the themes above. I plan to utilize momentary self-assessment data combined with usage logs and passive data collected from various electronic devices to identify, predict, and ultimately intervene in anxiety and procrastination.
Phase 1: Developing the Relationship Model and Identification Algorithms
Data collection will involve software on devices to record self-assessments, usage logs, and heart rate data. As for model and algorithms, Dynamic Structural Equation Modeling will filter indicators and establish a model between anxiety and procrastination. Meanwhile, association rule mining will link scenarios, usage, and heart rate data to identify anxiety and procrastination.
Phase 2: Establishing Intervention Mechanisms and Designing Intervention Software
Establishing Intervention Mechanisms: Critical predictive points for procrastination will be identified. If anxiety is detected, intervention measures such as alerts or reminders will be applied to manage anxiety and reduce procrastination risks.
Designing Intervention Software: User interviews will gather intervention requirements, and software functionalities will be designed accordingly, adhering to ethical and privacy standards.
Phase 3: Deploying and Testing the Intervention Software
Its effectiveness will be tested through tracking users’ anxiety and procrastination levels, semi-structured interviews, and satisfaction scales.
Limitations and Future Aspiration
The previous section has the following issues:
Technical and Thematic Limitations: There is a need to learn more about various passive mobile and wearable sensing platforms to acquire multimodal data effectively. Moreover, well-being encompasses a wide range of topics beyond anxiety, procrastination, or self-regulation. I am open to exploring other themes involving interactions between individuals and their environments that promote well-being.
Perspective Limitations: Currently, my perspective is limited to the individual. By enrolling, I aim to engage with diverse groups to understand their dynamics and specific needs, thereby broadening my research scope. Additionally, the preliminary plan lacks sufficient focus on spatial and environmental factors. Future research will incorporate environmental data, such as urban space impacts, to understand how different environments influence anxiety, procrastination, and overall well-being.