What Does All This Data Mean for My Future Mood? Actionable Analytics and Targeted Reflection for Emotional Well-Being

Hollis, Victoria; Konrad, Artie; Springer, Aaron; Antoun, Matthew; Antoun, Christopher; Martin, Rob; Whittaker, Steve

doi:10.1080/07370024.2016.1277724

Cited by 82 publications

(64 citation statements)

References 83 publications

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“…Such work aims to support increased self-awareness by providing interactive feedback mirroring physiological signals. Previous work on affective designs has focused on time-series graphs [33,103,108] and abstract visualizations [22,26,41,78,99,102] of biosignals on traditional mobile and desktop interfaces.…”

Section: Introductionmentioning

confidence: 99%

Towards Affective Chronometry

Umair

Sas

Latif

2019

Proceedings of the 2019 on Designing Interactive Systems Conference

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Increasing HCI work on affective interfaces aimed to capture and communicate users' emotions in order to support self-understanding. While most such interfaces employ traditional screen-based displays, more novel approaches have started to investigate smart materials and actuators-based prototypes. In this paper, we describe our exploration of smart materials and actuators leveraging their temporal qualities as well as common metaphors for real-time representation of changes in arousal through visual and haptic modalities. This exploration provided rationale for the design and implementation of six novel wrist-worn prototypes evaluated with 12 users who wore them over 2 days. Our findings describe how people use them in daily life, and how their material-driven qualities such as responsiveness, duration, rhythm, inertia, aliveness and range shape people's emotion identification, attribution, and regulation. Our findings led to four design implications including support for affective chronometry for both raise and decay time of emotional response, design for slowness, and for expressiveness.

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Section: Introductionmentioning

confidence: 99%

Towards Affective Chronometry

Umair

Sas

Latif

2019

Proceedings of the 2019 on Designing Interactive Systems Conference

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“…The above interventions point out a design space for supporting reflection on mood journal. As previous HCI studies about mood journals revealed (Konrad et al, 2016 andHollis et al, 2017), simply reviewing previously written memories is not enough for mood regulation. Designers of such systems also need to consider the mood dependent context and human memory weakness (Sellen and Whittaker, 2010).…”

Section: Supporting Reflection On Negative Memories (Challenge 3)mentioning

confidence: 94%

“…For example, findings from mood journal studies (Konrad et al, 2016) have shown that reflecting on positive memories in sad moods could potentially taint the happy memories and induce joy-killing thoughts. Findings also indicated that revisiting negative memories from digital affective diaries (Hollis et al, 2017) contributes little to both momentary and long-term mood improvement, although it has proven effective in pencil and paper based studies (Pennebaker et al, 2017) for inducing re-appraisal, self-distancing and wellbeing.…”

Section: Supporting Reflection On Negative Memories (Challenge 3)mentioning

confidence: 99%

Exploring Memory Interventions in Depression through Lifelogging Lens

Sas

2018

Electronic Workshops in Computing

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Depression is a major affective disorder with significant socioeconomic cost. Distinctive autobiographical memory impairments in depression include overgeneralization, negative-bias, and repetitive negative thinking. A few psychotherapeutic interventions have been purposefully designed to address these impairments, albeit they benefit from limited technological support. This paper reports an analysis of four memory-based interventions proven effective in the therapeutic practice targeting depression. We also explored the memory impairments addressed by these interventions. Our findings led to three design implications for digital tools in this space. We suggest the value of supporting enriched positive memory recall, negative memory reappraisal, and future episodic imagination.

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“…For the reasons mentioned above, we set the subject of prediction to tomorrow's mood instead of upcoming in a few hours. Upcoming mood prediction plays an important role in diverse topics such as emotion variety impacts on behavior and decision making [9], bipolar depression disorder in psychology [8], and recommender systems in artificial intelligence [10]. Early detection could contribute to appropriate interventions by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent critical situations [8].…”

Section: Introductionmentioning

confidence: 99%

Upcoming Mood Prediction Using Public Online Social Networks Data: Analysis over Cyber-Social-Physical Dimension

Dhahri

Matsumoto

Hoashi

2019

IEICE Trans. Inf. & Syst.

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Upcoming mood prediction plays an important role in different topics such as bipolar depression disorder in psychology and qualityof-life and recommendations on health-related quality of life research. The mood in this study is defined as the general emotional state of a user. In contrast to emotions which is more specific and varying within a day, the mood is described as having either a positive or negative valence [1]. We propose an autonomous system that predicts the upcoming user mood based on their online activities over cyber, social and physical spaces without using extradevices and sensors. Recently, many researchers have relied on online social networks (OSNs) to detect user mood. However, all the existing works focused on inferring the current mood and only few works have focused on predicting the upcoming mood. For this reason, we define a new goal of predicting the upcoming mood. We, first, collected ground truth data during two months from 383 subjects. Then, we studied the correlation between extracted features and user's mood. Finally, we used these features to train two predictive systems: generalized and personalized. The results suggest a statistically significant correlation between tomorrow's mood and today's activities on OSNs, which can be used to develop a decent predictive system with an average accuracy of 70% and a recall of 75% for the correlated users. This performance was increased to an average accuracy of 79% and a recall of 80% for active users who have more than 30 days of history data. Moreover, we showed that, for non-active users, referring to a generalized system can be a solution to compensate the lack of data at the early stage of the system, but when enough data for each user is available, a personalized system is used to individually predict the upcoming mood.

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What Does All This Data Mean for My Future Mood? Actionable Analytics and Targeted Reflection for Emotional Well-Being

Cited by 82 publications

References 83 publications

Towards Affective Chronometry

Towards Affective Chronometry

Exploring Memory Interventions in Depression through Lifelogging Lens

Upcoming Mood Prediction Using Public Online Social Networks Data: Analysis over Cyber-Social-Physical Dimension

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