research-article

Measuring Self-Esteem with Passive Sensing

Authors:

Mehrab Bin Morshed,

Munmun De Choudhury,

Gregory D. Abowd,

Thomas PlötzAuthors Info & Claims

PervasiveHealth '20: Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

Pages 363 - 366

https://doi.org/10.1145/3421937.3421952

Published: 02 February 2021 Publication History

Abstract

Self-esteem encompasses how individuals evaluate themselves and is an important contributor to their success. Self-esteem has been traditionally measured using survey-based methodologies. However, surveys suffer from limitations such as retrospective recall and reporting biases, leading to a need for proactive measurement approaches. Our work uses smartphone sensors to predict self-esteem and is situated in a multimodal sensing study on college students for five weeks. We use theory-driven features, such as phone communications and physical activity to predict three dimensions, performance, social, and appearance self-esteem. We conduct statistical modeling including linear, ensemble, and neural network regression to measure self-esteem. Our best model predicts self-esteem with a high correlation (r) of 0.60 and low SMAPE of 7.26% indicating high predictive accuracy. We inspect the top features finding theoretical alignment; for example, social interaction significantly contributes to performance and appearance-based self-esteem, whereas, and physical activity is the most significant contributor towards social self-esteem. Our work reveals the efficacy of passive sensors for predicting self-esteem, and we situate our observations with literature and discuss the implications of our work for tailored interventions and improving wellbeing.

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Cited By

Thukral MHaresamudram HPlötz T(2025)Cross-Domain HAR: Few-Shot Transfer Learning for Human Activity RecognitionACM Transactions on Intelligent Systems and Technology10.1145/370492116:1(1-35)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3704921
Zhang HWang LSheng YXu XMankoff JDey A(2023)A Framework for Designing Fair Ubiquitous Computing SystemsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610677(366-373)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610677

Index Terms

Measuring Self-Esteem with Passive Sensing
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

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cover image ACM Other conferences

PervasiveHealth '20: Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

May 2020

446 pages

ISBN:9781450375320

DOI:10.1145/3421937

Editors:
Sean A. Munson
Human Centered Design & Engineering, University of Washington
,
Stephen M. Schueller
University of California, Irvine
,
General Chair:
Rosa Arriaga
Georgia Institute of Technology, USA

Copyright © 2020 ACM.

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Published: 02 February 2021

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NIH
Siemens FutureMaker Fellowship

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PervasiveHealth '20

PervasiveHealth '20: 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

May 18 - 20, 2020

GA, Atlanta, USA

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PervasiveHealth '20 Paper Acceptance Rate 55 of 116 submissions, 47%;

Overall Acceptance Rate 55 of 116 submissions, 47%

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Cited By

Thukral MHaresamudram HPlötz T(2025)Cross-Domain HAR: Few-Shot Transfer Learning for Human Activity RecognitionACM Transactions on Intelligent Systems and Technology10.1145/370492116:1(1-35)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3704921
Zhang HWang LSheng YXu XMankoff JDey A(2023)A Framework for Designing Fair Ubiquitous Computing SystemsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610677(366-373)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610677

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