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Related Concept Videos

The Influence of Cognition on Affect01:29

The Influence of Cognition on Affect

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Cognition plays a pivotal role in shaping emotional experiences, as demonstrated by Schachter and Singer’s two-factor theory of emotion. According to this model, emotion arises from a combination of physiological arousal and cognitive interpretation. The body’s physiological response to stimuli is ambiguous and only gains emotional significance through cognitive labeling. For instance, an increased heart rate and adrenaline surge while standing near an attractive person may be...
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Related Experiment Video

Updated: May 5, 2026

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
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Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing

Zachary D King1, Han Yu1, Thomas Vaessen2,3

  • 1Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.

JMIR Mhealth and Uhealth
|February 7, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning-driven ecological momentary assessment (EMA) delivery can influence participant emotional states. This study found that optimizing EMA timing based on receptivity reduced negative affect but may introduce bias by triggering during positive emotional states.

Keywords:
EMAJITAIsaffect inferenceecological momentary assessmentjust-in-time adaptive interventionsmHealthmobile healthmobile phonereceptivitystudy design

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Area of Science:

  • Mobile health (mHealth)
  • Wearable sensor technology
  • Machine learning (ML)
  • Ecological Momentary Assessment (EMA)

Background:

  • Participant receptivity is crucial for mHealth study success, especially when collecting subjective health data.
  • Low compliance rates in certain populations can impact data quality.
  • ML and sensor data offer innovative ways to optimize survey delivery timing.

Purpose of the Study:

  • To investigate the impact of an ML-based EMA delivery system on participants' reported emotional states.
  • To identify factors influencing receptivity to EMAs in a wearable sensor-based study.
  • To analyze physiological indicators of receptivity and affect, and their interaction.

Main Methods:

  • Collected data from 45 healthy participants using wearable sensors (electrodermal activity, accelerometer, ECG, skin temperature).
  • Administered 10 EMAs daily assessing perceived mood.
  • Employed unsupervised (k-means clustering) and supervised (random forest, neural networks) ML to infer affect and receptivity during non-responses.

Main Results:

  • Triggering EMAs via a receptivity model decreased self-reported negative affect by over 3 points (0.29 SDs).
  • Predicted affect during non-responses showed a bimodal distribution, indicating EMAs were more frequently initiated during positive emotional states.
  • A significant relationship was observed between affect and receptivity.

Conclusions:

  • The relationship between affect and receptivity can influence mHealth study efficacy, particularly with ML-triggered EMAs.
  • Future research should aim for smart triggers that enhance EMA receptivity without biasing emotional state reporting.