Evidence map›Paper›PMID 34350057›Full record

ArticleInternational Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare2020

Measuring Self-Esteem with Passive Sensing.

Mehrab Bin Morshed, Koustuv Saha, Munmun De Choudhury, Gregory D Abowd, Thomas Plötz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mehrab Bin MorshedGeorgia Institute of Technology, Atlanta, Georgia, U.S.
Koustuv SahaGeorgia Institute of Technology, Atlanta, Georgia, U.S.
Munmun De ChoudhuryGeorgia Institute of Technology, Atlanta, Georgia, U.S.
Gregory D AbowdGeorgia Institute of Technology, Atlanta, Georgia, U.S.
Thomas PlötzGeorgia Institute of Technology, Atlanta, Georgia, U.S.

Funding

Leveraging Social Media Data and Machine Learning to Optimize Treatment Paradigms for Youth with SchizophreniaR01MH117172 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2019 to 2022
$2.6M
NIMH NIH HHS R01 MH117172
6 · The paper itself

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 (

Indexed as

campuslifecollege studentspassive sensingself-esteemwellbeing

Identifiers

PMID34350057
PMCPMC8329846

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.