Measuring Self-Esteem with Passive Sensing
Mehrab Bin Morshed1, Koustuv Saha1, Munmun De Choudhury1
1Georgia Institute of Technology, Atlanta, Georgia, U.S.
Summary
Smartphone sensors can now predict self-esteem. This study used phone data to accurately measure performance, social, and appearance self-esteem in college students, offering new insights for wellbeing.
Area of Science:
- Psychology
- Computer Science
- Digital Health
Background:
- Self-esteem, a crucial aspect of individual evaluation and success, is traditionally measured via surveys.
- Surveys are limited by recall and reporting biases, necessitating proactive measurement methods.
- The study addresses the need for objective, continuous self-esteem assessment.
Purpose of the Study:
- To predict self-esteem using passive smartphone sensor data.
- To investigate the relationship between digital behavior, physical activity, and self-esteem dimensions.
- To explore objective, sensor-based measurement of performance, social, and appearance self-esteem.
Main Methods:
- Utilized a multimodal sensing study with college students over five weeks.
- Extracted theory-driven features from smartphone sensors, including communication patterns and physical activity.
- Employed statistical modeling techniques like linear, ensemble, and neural network regression.
Main Results:
- Achieved high predictive accuracy for self-esteem with a correlation (r) of 0.60 and SMAPE of 7.26%.
- Identified key predictors: social interaction positively influences performance and appearance self-esteem.
- Physical activity emerged as the most significant predictor for social self-esteem.
Conclusions:
- Passive smartphone sensors effectively predict self-esteem dimensions.
- Findings support the use of objective sensor data for understanding and potentially improving psychological wellbeing.
- Results suggest potential for developing tailored digital interventions to enhance self-esteem.
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