It is difficult to design systems that honor the complex and often contradictory emotions that can be surfaced by sensitive encounters with recommender systems. To explore the design and ethical considerations in this space, we interviewed 20 people who had recently seen sensitive content through Facebook’s Memories feature. Interviewees typically described how (1) expectedness, (2) context of viewing, and (3) what we describe as “affective sense-making” were important factors for how they perceived “bittersweet” content, a sensitizing concept from our interviews that we expand upon. To address these user needs, we pose provocations to support critical work in this area and we suggest that researchers and designers: (1) draw inspiration from no/low-technology artifacts, (2) use empirical research to identify contextual features that have negative impacts on users, and (3) conduct user studies on affective sense-making.

We present a series of mHealth applications and studies pursued as part of the Fittle+ project. This program of research has the dual aims of (1) bringing scalable evidence-based behavior-change interventions to mHealth and evaluating them and (2) developing theoretically based predictive models to better understand the dynamics of the impact of these interventions on achieving behavior-change goals. Our approach in the Fittle+ systems rests on the idea that to master the complex fabric of a new healthy lifestyle, one must weave together a new set of healthy habits that over-ride the old unhealthy habits. To achieve these aims, we have developed a series of mHealth platforms that provide scaffolding interventions: Behavior-change techniques and associated mHealth interactions (e.g., SMS reminders; chatbot dialogs; user interface functionality; etc.) that provide additional support to the acquisition and maintenance of healthy habits. We present experimental evidence collected so far for statistically significant improvements in behavior change in eating, exercise, and physical activity for the following scaffolding interventions: guided mastery, teaming, self-affirmation, and implementation intentions. We also present predictive computational ACT-R models of daily individual behavior goal success for data collected in guided mastery and implementation intention studies that address goal-striving and habit formation mechanisms.

We explore the Examined Life, informing the design of reflective systems to promote emotional well-being, a critical health issue. People now have increasingly rich, digital records of highly personal data about what they said, did, and felt in the past. But social science research shows that people have difficulty in tracking and regulating their emotions. New reflective technologies that promote constructive analysis of rich personal data potentially offer transformative ways that individuals might better understand themselves and improve well-being. However, there are important system design challenges in supporting effective reflection about personal data. We explore fidelity in recording and representing past personal mood data, and forecasting future actions, feelings, and thoughts. Much prior personal informatics work has been dedicated to past-centric tools for recording and capture. In contrast, forecasting examines how we might use such past data to inform and motivate our future selves, providing recommendations about remedial actions to improve future well-being. Fidelity addresses both how and what reflective systems should show people about their pasts, in particular whether we should filter negative past experiences. To inform reflective system design, we examine forecasting and fidelity in controlled field trial interventions that explore two novel system designs for presenting and reflecting on mood data. We detail findings from 165 participants, 4,693 participant logfiles, 65 surveys, and 15 user interviews. Our novel forecasting system, EmotiCal, uses past mood data to model and visualize future user moods with the goal of encouraging participants to adopt remedial new behaviors to regulate negative moods before they occur. Such forecasting both improved mood and subsequent emotional self-awareness compared with controls who simply monitored their past. Consistent with system goals, interview responses also indicated that participants generated important insights into behaviors that affect their moods. Our second intervention examined filtering; it assessed the impact on well-being of recording and revisiting past experiences containing negative emotions. We compared participants who were encouraged to record and reflect on positive versus negative experiences. Long-term measures of happiness and ruminative behaviors improved by recording and reflecting on positive but not negative experiences, although this depended on the intensity of the negative experience. We discuss general design and theory implications for future systems that support monitoring, reflection, and forecasting to facilitate productive examination of our emotional lives.

Technology increasingly allows us to capture and revisit rich digital records of our lives, processes which we call Technology-Mediated Memory (TMM). We explore whether TMM alters unmediated remembering and also whether such changes affect psychological well-being. Human memory biases promote well-being by adaptively editing our memories, making them more positive. In contrast, TMM often provides rich records of what people actually did and felt, which could disrupt adaptive edits. To explore this, we developed a smartphone-based personal TMM application, Echo, that allows participants to record and later reflect on everyday events. In a month-long deployment, 64 users made over 3200 recordings and reflections. We found that although Echo TMM alters how we remember, these changes remain adaptive. Instead of compromising adaptive biases, Echo TMM helps well-being and benefits are sustained long-term. Logfile analysis shows that participants use Echo strategically to prospectively edit by initially reporting events positively to anticipate future viewing. Participants also distance themselves from past negative events by reflecting more positively than at recording. We discuss design and theoretical implications.

Preventable behaviors contribute to many life threatening health problems. Behavior-change technologies have been deployed to modify these, but such systems typically draw on traditional behavioral theories that overlook affect. We examine the importance of emotion tracking for behavior change. First, we conducted interviews to explore how emotions influence unwanted behaviors. Next, we deployed a system intervention, in which 35 participants logged information for a self-selected, unwanted behavior (e.g., smoking or overeating) over 21 days. 16 participants engaged in standard behavior tracking using a Fact-Focused system to record objective information about goals. 19 participants used an Emotion-Focused system to record emotional consequences of behaviors. Emotion-Focused logging promoted more successful behavior change and analysis of logfiles revealed mechanisms for success: greater engagement of negative affect for unsuccessful days and increased insight were key to motivating change. We present design implications to improve behavior-change technologies with emotion tracking.

Automated coaching systems offer a convenient, cost-effective way to reduce stress, which can be a serious health issue. However, one concern with such systems is compliance; users fail to achieve daily stress reduction goals because goals are too easy or too difficult. To address this, we built DStress (Design for Stress), a theoretically grounded system that sets adaptive goals in three coaching dimensions: Exercise, Meditation and Accessibility. DStress modifies goal-difficulty based on the individual's immediately previous performance. In a 28-day deployment with 65 users, DStress reduced scores on one direct measure of stress almost in half, significantly more than two other non-adaptive coaching strategies. However, on a second direct stress measure, no improvement was found. There were also no improvements on other indirect stress measures. Analysis of 2842 user-generated reports suggests our findings were the result of DStress balancing compliance against the degree of challenge of the goals it would set.

As people document more of their lives online, some recent systems are encouraging people to later revisit those recordings, a practice we're calling technology-mediated reflection (TMR). Since we know that unmediated reflection benefits psychological well-being, we explored whether and how TMR affects well-being. We built Echo, a smartphone application for recording everyday experiences and reflecting on them later. We conducted three system deployments with 44 users who generated over 12,000 recordings and reflections. We found that TMR improves well-being as assessed by four psychological metrics. By analyzing the content of these entries we discovered two mechanisms that explain this improvement. We also report benefits of very long-term TMR.