Intention-use gaps and displacement of valued activities predict social media regret more strongly than duration, with pre-session context generalizing across users and physiological signals adding person-specific predictive power.
HUCKINS, COURTNEY ROGERS, MEGHAN L
6 Pith papers cite this work, alongside 38 external citations. Polarity classification is still indexing.
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LLMs routinely produce unsupported causal stories for personal sensing anomalies, and richer evidence or constrained prompts do not reliably eliminate this epistemic overreach.
TimeSRL uses semantic abstractions from time-series data optimized via reinforcement learning to achieve better cross-dataset generalization than standard ML or LLM baselines in mental health prediction.
PULSE demonstrates that agentic LLM-based investigation of passive smartphone sensing data achieves balanced accuracies of 0.743 (with diary) and 0.713 (sensing-only) for predicting emotion regulation desire and intervention availability in 50 cancer survivors.
A privacy-aware semantic encoding framework for GPS data in mobile stress recognition maintains performance comparable to non-private baselines while improving privacy by 2-3 times on the GeoLife dataset via LOSO validation.
Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.
citing papers explorer
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Before You Scroll Again: Predicting Regretful Social Media Sessions from In-the-Wild Contextual and Wearable Sensing
Intention-use gaps and displacement of valued activities predict social media regret more strongly than duration, with pre-session context generalizing across users and physiological signals adding person-specific predictive power.
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Causal Stories from Sensor Traces: Auditing Epistemic Overreach in LLM-Generated Personal Sensing Explanations
LLMs routinely produce unsupported causal stories for personal sensing anomalies, and richer evidence or constrained prompts do not reliably eliminate this epistemic overreach.
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TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health
TimeSRL uses semantic abstractions from time-series data optimized via reinforcement learning to achieve better cross-dataset generalization than standard ML or LLM baselines in mental health prediction.
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PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship
PULSE demonstrates that agentic LLM-based investigation of passive smartphone sensing data achieves balanced accuracies of 0.743 (with diary) and 0.713 (sensing-only) for predicting emotion regulation desire and intervention availability in 50 cancer survivors.
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From Coordinates to Context: An LLM-Bootstrapped Semantic Encoding Framework for Privacy-Preserving Mobile Sensing Stress Recognition
A privacy-aware semantic encoding framework for GPS data in mobile stress recognition maintains performance comparable to non-private baselines while improving privacy by 2-3 times on the GeoLife dataset via LOSO validation.
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A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring
Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.