Toward Future-Centric Personal Informatics: Expecting Stressful Events and Preparing Personalized Interventions in Stress Management

Lee, Kwangyoung; Cho, Hyewon; Toshnazarov, Kobiljon; Narziev, Nematjon; Rhim, So Young; Han, Kyungsik; Noh, Youngmin; Hong, Hwajung

doi:10.1145/3313831.3376475

Cited by 17 publications

(9 citation statements)

References 45 publications

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“…However, no overall reduction in study-long stress was found in this study either. In a 30-day study with 47 participants [48], a calendar-mediated stressor anticipation application was deployed. It allowed participants to anticipate expected stressful events.…”

Section: Stress Interventionsmentioning

confidence: 99%

“…Many of these stressors may not be reported when asked later on. Our study was designed to help participants with the Collection stage by prompting them to easily log their stressors soon after their occurrence while limiting burden, as suggested in [48]. It helped them with the Integration stage by summarizing their data in visual forms.…”

Section: Design Rationale For the Moods Studymentioning

confidence: 99%

“…Aside from charging the smartwatch, participants were asked to rate their stress and annotate the stressor multiple times daily. Participants reported in [48] that typing an event repeatedly without auto-complete became burdensome.…”

Section: Burden and Utilitymentioning

confidence: 99%

“…Second, the passive detection of stress has been used to deliver interventions at stressful moments [31]. Although the reduction in self-reported stress has been observed before and after some of these momentary interventions, only a negligible study-long reduction in stress has been found thus far [31,48,66]. We, however, note this is still an active area of exploration given that the passive detection of stress has emerged only recently and efficacious interventions can take decades to emerge, accepted, and widely used.…”

Section: Introductionmentioning

confidence: 99%

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Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables

Neupane,

Saha,

Ali

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Commercial wearables from Fitbit, Garmin, and Whoop have recently introduced real-time notifications based on detecting changes in physiological responses indicating potential stress. In this paper, we investigate how these new capabilities can be leveraged to improve stress management. We developed a smartwatch app, a smartphone app, and a cloud service, and conducted a 100-day field study with 122 participants who received prompts triggered by physiological responses several times a day. They were asked whether they were stressed, and if so, to log the most likely stressor. Each week, participants received new visualizations of their data to self-reflect on patterns and trends. Participants reported better awareness of their stressors, and self-initiating fourteen kinds of behavioral changes to reduce stress in their daily lives. Repeated self-reports over 14 weeks showed reductions in both stress intensity (in 26,521 momentary ratings) and stress frequency (in 1,057 weekly surveys).CCS Concepts: • Human-centered computing → Field studies; Empirical studies in HCI.

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Section: Stress Interventionsmentioning

confidence: 99%

Section: Design Rationale For the Moods Studymentioning

confidence: 99%

Section: Burden and Utilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables

Neupane,

Saha,

Ali

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…There exist many trials for quantifying mood in the field of personal informatics, and these can lead to more effective reflection and coping. Furthermore, if a subject's future mood can be accurately predicted, more detailed coping behavior can be provided [6]. Previous studies have revealed many ways of predicting mood using machine learning techniques on physiological data collected by electrocardiogram, pulse wave, and electroencephalography [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Mood Prediction Based on Calendar Events Using Multitask Learning

2022

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Predicting future moods with machine learning can help plan appropriate strategies for mental health; this can be facilitated by collecting future event information from calendar applications. However, a model for mood prediction from calendar event information has not yet been developed. In addition, because the dataset of mood values is characterized by the tendency to collect values close to either the mean or the most recent value, a mood prediction model employing past moods as input is likely to overfit the distribution of the training dataset. This makes it difficult to capture changes in mood due to external factors such as events, especially on days when mood fluctuates greatly. In this study, we propose a mood prediction deep learning model with an event feature extractor. For introducing a multitask learning method, we add not only mood but also the effects of events on mood as objective variables, and we achieve higher prediction accuracy than existing machine learning models. Given the small initial weights in deep learning, the middle layer tends to output values near zero when learning is insufficient. With the introduction of multitask learning, the output values of the event feature extractor in the prediction model output non-zero values, and the prediction accuracy is improved on days featuring a large fluctuation in mood from the previous day. Our proposal is expected to be a more efficient learning method for deep learning models to predict human mental states from external factors.

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Patient Data Work with Consumer Self-tracking: Exploring Affective and Temporal Dimensions in Chronic Self-care

Andersen

Fritsch

Matthiesen

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Toward Future-Centric Personal Informatics: Expecting Stressful Events and Preparing Personalized Interventions in Stress Management

Cited by 17 publications

References 45 publications

Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables

Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables

Mood Prediction Based on Calendar Events Using Multitask Learning

Patient Data Work with Consumer Self-tracking: Exploring Affective and Temporal Dimensions in Chronic Self-care

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