ArticleJournal of healthcare informatics research2020
Affect Estimation with Wearable Sensors.
Article in Journal of healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Integrative research on technology-assisted physical activity and biological aging: a review of wearable sensors, tele-exercise platforms, and aging biomarkers.Frontiers in medicine · 2026Review
- Predicting Multiple Outcomes Associated with Frailty based on Imbalanced Multi-label Classification.Journal of healthcare informatics research · 2024Article
- Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.Sensors (Basel, Switzerland) · 2024Review
- Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review.JMIR mental health · 2023Article
- Understanding People's Use of and Perspectives on Mood-Tracking Apps: Interview Study.JMIR mental health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Affective states are associated with people's mental health status and have profound impact on daily life, thus unobtrusively understanding and estimating affects have been brought to the public attention. The pervasiveness of wearable sensors makes it possible to build automatic systems for affect tracking. However, constructing such systems is a challenging task due to the complexity of human behaviors. In this work, we focus on the problem of estimating daily self-reported affects from sensor-generated data. We first analyze the intra- and inter-subject differences of self-reported affect labels. Second, we explore different machine learning models as well as label transformation techniques to overcome the individual differences in self-reported responses estimation. We conceptualize three experimental settings including long-term and short-term estimation scenarios. Our experimental results show that the mixed effects model and label transformation can yield better estimation of individual daily affect. This work poses the basis for future sensor-based individualized and real-time affective digital and/or clinical interventions.
Indexed as
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What OpenQuestion holds
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.