A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring

• URL: http://arxiv.org/abs/2605.14360v1
• Date: Thu, 14 May 2026 04:36:29 GMT
• Title: A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring
• Abstract summary: We examined whether ultra-brief naturalistic concern text could serve as a scalable complement to passive sensing.<n>Weeks dominated by academic concern framing were associated with lower physical activity.<n>Emotional exhaustion language was associated with poorer sleep quality and lower heart rate variability.
• Score: 1.343264019207053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wearable devices capture physiological and behavioral data with increasing fidelity, but the psychological context shaping these outcomes is difficult to recover from sensor data alone, limiting passive sensing utility for digital health. We examined whether ultra-brief naturalistic concern text could serve as a scalable complement to passive sensing. In a year-long study of 458 university students (3,610 person-waves) tracked with Oura rings, participants responded bimonthly to an open-ended prompt about what concerned them most; responses had a median length of three words. We compared dictionary-based, general pretrained, and domain-adapted NLP approaches using within-person mixed-effects models across nine sleep and physical activity outcomes. Weeks dominated by academic concern framing were associated with lower physical activity; weeks characterized by emotional exhaustion language were associated with poorer sleep quality and lower heart rate variability. General pretrained embeddings outperformed domain-adapted models for most outcomes, with domain adaptation showing relative advantage for autonomic outcomes. Zero-shot classification of concern topics produced no significant associations, while affective dimensions across all three methods were consistently associated with outcomes, indicating emotional register rather than topical content carries the signal. These findings offer design guidance: ultra-brief affective prompts enrich the psychological interpretability of passive physiological data at minimal burden.

Related papers

• From Wearable Data to Personalized and Actionable Health Insights [12.6174140673741]
  We propose a framework that combines user annotations with wearable data to support better stress management.<n>Social interaction reduced average heart rate by 4.35 to 5.0 beats per minute, deliberate rest reduced average Garmin stress scores by 10.03 to 13.83 points, and mindfulness activities decreased average HRV by 6.61 to 13.22 milliseconds.
  arXiv  Detail & Related papers  (2026-08-04T07:23:46Z)
• Depression Markers in Speech: An Approach based on Tract Variables Dynamics [52.72895760734978]
  This study identifies new depression biomarkers based on the dynamical properties of tract variables.<n>The proposed biomarkers effectively discriminate between the depressed and control speakers, as evidenced by the high Cliffs delta values across both read and spontaneous speech.
  arXiv  Detail & Related papers  (2026-07-28T15:48:07Z)
• The Association of Transformer-based Sentiment Analysis with Symptom Distress and Deterioration in Routine Psychotherapy Care [39.146761527401424]
  Transformer deep learning architecture has produced text-based sentiment analysis models that are highly accurate and context-aware.<n>We use proposed utterance-level and session-level sentiment features derived from a fine-grained sentiment model on a large corpus of psychotherapy sessions.<n>There are statistically significant differences between the sentiment distributions for patients flagged as at risk of deterioration or dropping out of care via either the OQ Rational or Empirical outcome models.
  arXiv  Detail & Related papers  (2026-05-11T00:41:47Z)
• CAST-Phys: Contactless Affective States Through Physiological signals Database [74.28082880875368]
  The lack of affective multi-modal datasets remains a major bottleneck in developing accurate emotion recognition systems.<n>We present the Contactless Affective States Through Physiological Signals Database (CAST-Phys), a novel high-quality dataset capable of remote physiological emotion recognition.<n>Our analysis highlights the crucial role of physiological signals in realistic scenarios where facial expressions alone may not provide sufficient emotional information.
  arXiv  Detail & Related papers  (2025-07-08T15:20:24Z)
• Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning [47.02027575768659]
  We introduce continuous valence and arousal labels for an existing dataset of children's stories originally annotated with discrete emotion categories. For predicting the thus obtained emotionality signals, we fine-tune a DeBERTa model and improve upon this baseline via a weakly supervised learning approach. A detailed analysis shows the extent to which the results vary depending on factors such as the author, the individual story, or the section within the story.
  arXiv  Detail & Related papers  (2024-06-04T12:17:16Z)
• Exploring the dynamic interplay of cognitive load and emotional arousal by using multimodal measurements: Correlation of pupil diameter and emotional arousal in emotionally engaging tasks [0.0]
  The study aims to investigate the correlation between two continuous sensor streams, pupil diameter as an indicator of cognitive workload and FACTs with deep learning as an indicator of emotional arousal. 28 participants worked on three cognitively demanding and emotionally engaging everyday moral dilemmas while eye-tracking and emotion recognition data were collected. The results show negative and statistically significant correlations between the data streams for emotional arousal and pupil diameter.
  arXiv  Detail & Related papers  (2024-03-01T08:49:17Z)
• Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings? [0.210674772139335]
  This study aims to bridge the gap between laboratory-based studies and real-life settings by leveraging consumer-grade wearables and self-report measures. We present the initial analysis of the collected data, focusing primarily on the results of valence classification. This research opens up avenues for future research in the field of mobile mental health interventions.
  arXiv  Detail & Related papers  (2023-12-21T13:57:34Z)
• A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing [0.4818210066519976]
  Emotions are an essential part of human behavior that can impact thinking, decision-making, and communication skills. The ability to accurately monitor and identify emotions can be useful in many human-centered applications such as behavioral training, tracking emotional well-being, and development of human-computer interfaces.
  arXiv  Detail & Related papers  (2023-08-17T01:15:26Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling [51.316408685035526]
  Learning new tasks and skills in succession without losing prior learning is a computational challenge for both artificial and biological neural networks. Here, we investigate how modeling three distinct components of mammalian sleep together affects continual learning in artificial neural networks.
  arXiv  Detail & Related papers  (2022-09-09T13:45:27Z)
• Insights on Modelling Physiological, Appraisal, and Affective Indicators of Stress using Audio Features [10.093374748790037]
  Utilising speech samples collected while the subject is undergoing an induced stress episode has recently shown promising results for the automatic characterisation of individual stress responses. We introduce new findings that shed light onto whether speech signals are suited to model physiological biomarkers.
  arXiv  Detail & Related papers  (2022-05-09T14:32:38Z)
• Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load [17.394964035035866]
  We introduce a set of five datasets for task load detection in speech. The voice recordings were collected as either cognitive or physical stress was induced in the cohort of volunteers. We used the datasets to design and evaluate a novel self-supervised audio representation.
  arXiv  Detail & Related papers  (2022-03-30T19:43:21Z)
• Voxel-level Importance Maps for Interpretable Brain Age Estimation [70.5330922395729]
  We focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature.
  arXiv  Detail & Related papers  (2021-08-11T18:08:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.