Literature DB >> 30827495

"Development and preliminary validation of an image-based instrument to assess depressive symptoms".

Davide Marengo1, Michele Settanni2, Fabrizia Giannotta3.   

Abstract

Depression has high social and economic costs, making the reducing of potential barriers to screening of utmost importance. The use of non-verbal, image-based items might help to widen accessibility to depression screenings due to their potentially increased ease of interpretation and language-free nature. In this view, the paper presents two studies exploring the feasibility of assessing depressive symptoms using a set of image-based items consisting of 36 emoji. In study 1, 430 online-recruited young adults participated to investigate whether they ever felt in the way depicted by each emoji during the last week. Results showed that 33 emoji had significant, theoretically coherent correlations with the 10-item version of the Center for Epidemiologic Studies Depression Scale. Next, a subset of 10 emoji were selected for potential inclusion in a brief depression assessment. In study 2, using a sample of 482 young adults, the 10-item emoji-based assessment showed acceptable internal consistency, and theoretically consistent convergent and divergent validity with depressive symptoms, and big-5 personality traits. Further, the emoji-based screening instrument showed remarkable accuracy in identifying individuals showing depression symptoms. Overall, results indicate that the selected emoji represent a promising alternative to text-based items when assessing depressive symptoms among young adults.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Depression; Image-based instrument; Measurement; Psychopathology; Young adults

Mesh:

Year:  2019        PMID: 30827495     DOI: 10.1016/j.psychres.2019.02.059

Source DB:  PubMed          Journal:  Psychiatry Res        ISSN: 0165-1781            Impact factor:   3.222


  2 in total

1.  A Systematic Review of Emoji: Current Research and Future Perspectives.

Authors:  Qiyu Bai; Qi Dan; Zhe Mu; Maokun Yang
Journal:  Front Psychol       Date:  2019-10-15

2.  Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub.

Authors:  Xuan Lu; Wei Ai; Zhenpeng Chen; Yanbin Cao; Qiaozhu Mei
Journal:  PLoS One       Date:  2022-01-26       Impact factor: 3.240

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.