ArticleProceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference)2015
Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions.
Article in Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04554706 (Using Mobile Phones to Reduce Negative Rumination), which is not on this map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Using Mobile Phones to Reduce Negative Rumination
Who cites it
16 citing papers in PubMed.
- An Evaluation of Temporal and Categorical Uncertainty on Timelines: A Case Study in Human Activity Recall Visualizations.Visualization : proceedings of the ... IEEE Conference on Visualization. IEEE Conference on Visualization · 2025Article
- A Review of Sensor-Based Interventions for Supporting Patient Adherence to Inhalation Therapy.Patient preference and adherence · 2024Review
- Feedback based on experience sampling data: Examples of current approaches and considerations for future research.Heliyon · 2023Article
- Assessment and Disruption of Ruminative Episodes to Enhance Mobile Cognitive Behavioral Therapy Just-in-Time Adaptive Interventions in Clinical Depression: Pilot Randomized Controlled Trial.JMIR formative research · 2023Article
- Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis.Journal of medical Internet research · 2022Review
- Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study.Journal of medical Internet research · 2020Observational
- Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020Article
- Article
- Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches.Journal of medical Internet research · 2019Observational
- Automated sensing of daily activity: A new lens into development.Developmental psychobiology · 2019Review
- The Accuracy of Smart Devices for Measuring Physical Activity in Daily Life: Validation Study.JMIR mHealth and uHealth · 2018Article
- Chronodes: Interactive Multifocus Exploration of Event Sequences.ACM transactions on interactive intelligent systems · 2018Article
- How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.Journal of biomedical informatics · 2017Article
- Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity.Journal of medical Internet research · 2016Article
- Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2016Article
- cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment.Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.