Personalized behavior change interventions can be effective as they dynamically adapt to an individual’s context. Financial incentives, a commonly used intervention in commercial applications and policy-making, offer a mechanism for creating personalized micro-interventions that are both quantifiable and amenable to systematic evaluation. However, the effectiveness of such personalized micro-financial incentives in real-world settings remains largely unexplored. In this study, we propose a personalization strategy that dynamically adjusts the amount of micro-financial incentives to promote smartphone use regulation and explore its efficacy and user experience through a four-week, in-the-wild user study. The results demonstrate that the proposed method is highly cost-effective without compromising intervention effectiveness. Based on these findings, we discuss the role of micro-financial incentives in enhancing awareness, design considerations for personalized micro-financial incentive systems, and their potential benefits and limitations concerning motivation change.
https://dl.acm.org/doi/10.1145/3706598.3714208
Non-profits such as voluntary and community-based (VC) organisations are facing increasing pressures to engage in data work to sustain themselves. They face challenges with practices, information systems and tools associated with capturing data for supporting service provision. Most recently, researchers working with VC organisations have turned to Feminist and Care discourses to envision alternatives to current socio-technical systems whereby their values and purposes do not match with those of non-profits, consequently pulling the latter away from their socially driven mission. We report on a longitudinal, collaborative study with a UK-based mental health peer support organisation that created innovative tools as a means of navigating current pressures to practice data work for the quantification of mental health service provision. We present findings from interviews conducted with our community partner and share how recovery work has informed careful data practices, offering recommendations for supporting data work in mental health recovery.
https://dl.acm.org/doi/10.1145/3706598.3713537
Mental wellbeing has become a crucial aspect of overall health and has drawn increased attention to mental health concerns. Research in human-computer interaction (HCI) has explored how technologies can support mental wellbeing and address mental health issues. However, current research predominantly reflects Western cultural perspectives, leaving gaps in our understanding of mental wellbeing, coping strategies, and digital tools for mental wellbeing support from Eastern cultural viewpoints. To start to address this disparity, we interviewed 19 Taiwanese emerging adults aged between 18 and 29—a demographic uniquely susceptible to mental health challenges due to the transitional nature of this life phase. We explored their conceptualization of mental wellbeing, the challenges they encounter, the strategies they employ for managing mental wellbeing, and the role of digital tools in this process. The results highlight the intricate influence of cultural, political, social, and individual factors, and their interactions on mental wellbeing.
https://dl.acm.org/doi/10.1145/3706598.3713143
Personal informatics literature has examined reflection in tracking, but there are gaps in our understanding of how self-initiated reflection that one engages in shortly after data collection has taken place occurs in everyday life and how technology can best support it. We use baby tracking as a case study to explore `temporality,' the time over which reflection occurs relative to data collection, as caregivers track their baby's well-being over both short-term and long-term. We interviewed 20 parents in the U.S. who used baby-tracking technology. We find that parents ask different questions based on the time elapsed since data collection, such as checking alignment with medical guidance and prior patterns immediately after tracking or augmenting memory when reflecting hours later. We summarize these findings into a framework for short-term reflection in baby tracking that includes three windows: the immediate, in-between, and cumulative. We use these windows to identify helpful design patterns in baby-tracking technologies toward supporting temporally meaningful reflection and opportunities for further study in other self-tracking domains.
https://dl.acm.org/doi/10.1145/3706598.3713197
Most studies of Personal Informatics (PI) focus on the holistic experience of self-tracking or how users relate to self-tracking goals. Recently, new tracker metrics became available in commercial systems, e.g. stress scores or body battery. Hence, more attention should be devoted to what users track and how they understand metrics produced by their trackers. Charting the evolution of metrics in PI can enable building systems that better support well-being. To this end, we interviewed n=25 fitness tracker users to discover what metrics are most important to them, how they understand the metrics, and how they formulate their goals with respect to the metrics. We found that users created a metric ecology which they adjusted to their life circumstances, reformulating their goals. We identified key issues in understanding metrics which bear the risk of misuse. We contribute recommendations for future PI systems as self-tracking metrics increase in complexity.
https://dl.acm.org/doi/10.1145/3706598.3713650
Over the past decade, mobile apps have been widely adopted as a digital intervention method for mental health support, offering scalable and accessible solutions to address the growing global mental health challenges. However, sustaining user engagement in real-world settings remains a major challenge in the development of these applications. This study systematically examines factors that hinder user engagement in existing mobile mental health support systems through a scoping review of the literature. After an initial identification of 1,267 papers, we conducted a final analysis of 111 empirical studies using mobile app-based mental health support systems. The study investigates the main factors that negatively affect user engagement from user and system perspectives. Based on these findings, we propose guidelines for enhancing user engagement and structuring personalized emotional interaction design along three dimensions: adaptive, continuous, and multimodal interactions. Furthermore, we discuss the potential for integration with advanced AI methods (e.g., LLM-based AI agents) as a way to achieve these design implications and suggestions. Our results provide critical insights for enhancing long-term user engagement in the development of future mental health support systems.
https://dl.acm.org/doi/10.1145/3706598.3713732
Health information technologies are transforming how mental healthcare is paid for through value-based care programs, which tie payment to data quantifying care outcomes. But, it is unclear what outcomes data these technologies should store, how to engage users in data collection, and how outcomes data can improve care. Given these challenges, we conducted interviews with 30 U.S.-based mental health clinicians to explore the design space of health information technologies that support outcomes data specification, collection, and use in value-based mental healthcare. Our findings center clinicians’ perspectives on aligning outcomes data for payment programs and care; opportunities for health technologies and personal devices to improve data collection; and considerations for using outcomes data to hold stakeholders including clinicians, health insurers, and social services financially accountable in value-based mental healthcare. We conclude with implications for future research designing and developing technologies supporting value-based care across stakeholders involved with mental health service delivery.
https://dl.acm.org/doi/10.1145/3706598.3713481