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SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks.

Maryam Khalid1, Elizabeth B Klerman2, Andrew W McHill3

  • 1Department of Electrical & Computer Engineering, Rice University, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|March 12, 2026
PubMed
Summary
This summary is machine-generated.

SleepNet predicts sleep duration by analyzing physiological, phone, and social network data. Incorporating social contagion significantly improves prediction accuracy, even with noisy data.

Keywords:
Graph convolutioncontagiongraph neural networksmobile computingmultimodal sensingsleepsocial networkwearable sensingwell-being prediction

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Area of Science:

  • Computational health
  • Network science
  • Behavioral informatics

Background:

  • Sleep behavior is crucial for physical and mental health.
  • Ubiquitous sensors can monitor sleep, aiding health management.
  • Sleep is influenced by physiology, digital media, social networks, and weather.

Purpose of the Study:

  • To develop SleepNet, a system for predicting next-day sleep duration.
  • To integrate physiological, phone, and social network data using graph networks.
  • To leverage social contagion effects for improved sleep behavior prediction.

Main Methods:

  • Proposed SleepNet architecture utilizing graph networks and an attention mechanism.
  • Integration of physiological and phone data from mobile and wearable devices.
  • Exploitation of social contagion in sleep behavior via network analysis.

Main Results:

  • Incorporating social networks significantly enhances sleep duration prediction accuracy.
  • SleepNet demonstrates robustness against perturbations in input data.
  • Network topology, specifically eigenvalue centrality, impacts prediction stability.

Conclusions:

  • SleepNet effectively predicts sleep duration by integrating diverse data sources.
  • Social contagion is a significant factor in sleep behavior prediction.
  • Understanding network topology is key to robust sleep behavior monitoring systems.