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Naturalistic Observations

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...

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Related Experiment Video

Updated: Jun 6, 2025

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Published on: April 7, 2020

Understanding Morning Emotions by Analyzing Daily Wake-Up Alarm Usage: Longitudinal Observational Study.

Kyue Taek Oh¹, Jisu Ko², Nayoung Jin²

¹Department of Human-Computer Interaction, University of Hanyang, Ansan, Gyeonggi-do, Republic of Korea.

JMIR Human Factors

|November 29, 2024

Summary

This summary is machine-generated.

Morning alarm usage patterns influence daily emotions. Alarm set times and deactivation speed correlate with feelings like peacefulness, refreshment, nervousness, and happiness, offering new monitoring methods.

Keywords:

emotion monitoring longitudinal observational study morning context morning emotion wake-up alarm usage

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Last Updated: Jun 6, 2025

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Area of Science:

Psychology
Human-Computer Interaction
Digital Health

Background:

Morning emotions significantly impact daily well-being.
Traditional emotion monitoring relies on time-consuming surveys.
Novel methods are needed for efficient emotional assessment.

Purpose of the Study:

To investigate the relationship between daily alarm usage patterns and morning emotions.
To identify specific alarm behaviors associated with different emotional states.

Main Methods:

Recruited 373 Alarmy app users from the US and South Korea.
Collected demographics, usual behaviors, and 2-week morning emotion self-reports.
Analyzed alarm app logs and used generalized estimating equations (GEE) for statistical analysis.

Main Results:

Varied alarm usage correlates with morning emotions.
Earlier alarm set times linked to peacefulness and refreshment.
Task-based alarms associated with nervousness; longer deactivation times negatively correlated with happiness.

Conclusions:

Daily alarm usage patterns are demonstrably linked to morning emotions.
Alarm app logs offer a supplementary, less burdensome method for emotion monitoring.
Findings suggest leveraging digital behavioral data for mental wellness insights.