Drawing Test Form for Depression: The Development of Drawing Tests for Predicting Depression Among Breast Cancer Patients

Kim, Jiyeon; Chung, Seockhoon

doi:10.30773/pi.2021.0044

Cited by 4 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously, some researchers studied the discrimination of projective tests based on ROC curve analysis. For example, Kim and Chung (2021) developed the drawing tests for predicting depression among breast cancer patients, they reported an AUC of 0.82, a sensitivity of 0.85, and a specificity of 0.64. They evaluated ROC with summed scores of 23 formative elements, including the number of colors used, degree of integration, closure status, etc.…”

Section: Discussionmentioning

confidence: 99%

The Application of Human Figure Drawing as a Supplementary Tool for Depression Screening

Deng

Wang

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

ObjectiveDepression is one of the most prevalent mental disorder in college students. The traditional screening method for is psychological measurements or scales, but social desirability can cause students to mask their thoughts, and an auxiliary projective test may be needed. This study was designed to measure the validity of applying human figure drawing (HFD) test as an auxiliary tool for depression screening in this population.MethodsThe HFD test was administered to 113 clinical participants diagnosed with major depressive disorder and 97 healthy college students with self-rating depression scale scores <50. Correlation analysis, chi-square tests, and logistic regression were conducted to identify specific drawing features that associated with depression and could differentiate between the clinical and control subjects. ROC curve was also implemented to evaluate the diagnostic accuracy.ResultsEleven drawing features were significantly related to depression based on the chi-square test results and seven drawing features were associated with depression based on correlation analysis. After logistic regression by controlling gender and age, three drawing features were associated with depression: shaded eyes, drawing clothes in detail, and drawing other personal belongings. Further, drawing clothes in detail and drawing other personal belongings were two significant variables in ROC curve analysis.ConclusionLogistic regression showed that shaded eyes, drawing clothes in detail and drawing other personal belongings were significant drawing features. Individuals with depression will have less energy to put extra effort into drawing and are less likely to have detailed drawings. And the shading of eyes may represent that depressive individuals have a low willingness to communicate and tend to isolate themselves. The results indicated that Human Figure Drawing could be used as an auxiliary tool in college students’ depression screening. Further, the ROC curve analysis showed low discrimination of single drawing features, suggesting that the application of Human Figure Drawing should be considered as a whole instead of focusing on the single drawing feature.

show abstract

Section: Discussionmentioning

confidence: 99%

The Application of Human Figure Drawing as a Supplementary Tool for Depression Screening

Deng

Wang

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

show abstract

Tree Imagery in Drawing Tests for Screening Mental Disorders: A Systematic Review and Meta-analysis

Guo,

Feng,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Tree imagery in drawing tests has demonstrated effectiveness in predicting mental disorders; however, there remains a lack of uniformity in the selection and interpretation of predictors. This study aimed to integrate various tree imagery characteristics in mental disorders through a systematic review and meta-analysis and to further identify valid indicators for predicting mental disorders. Methods A search of the following electronic databases was performed in May 2023: PubMed, Web of Science, Embase, EBSCO, CNKI, VIP, and Wanfang. Screening and checking of the literature were performed independently by two authors. A total of 42 studies were included in the meta-analysis. The strength of the association between drawing characteristics and mental disorders was measured by the ratio (OR) with a 95% CI. Publication bias was assessed using a funnel plot, Rosenthal’s fail-safe number (Nfs), and the trim and fill method. Results The analysis demonstrated a total of 45 drawing characteristics that appeared at least three times in previous studies, 24 of which were found to significantly predict mental disorders. The effective predictors could be categorized into five categories: blackened out, scribbled lines, oddly shaped, no vitality, and overly simple. Subgroup analyses indicated that “blackened tree”, “no motion”, and “excessive separation” were specific indicators of affective disorders, whereas “roots” was an indicator of thought disorders. Common indicators for mental disorders included “weak or intermittent tree lines”, “no additional decoration”, “simplified drawing”, “small drawing size” and “very small tree”. Conclusion This study confirms the value of drawing tests in screening for mental disorders, and provides reference for the selection and interpretation of drawing indicators.

show abstract

Digital Phenotypes for Early Detection of Internet Gaming Disorder in Adolescent Students: Explorative Data-Driven Study

Cho,

Kim,

Cho

et al. 2024

JMIR Ment Health

View full text Add to dashboard Cite

Background Limited awareness, social stigma, and access to mental health professionals hinder early detection and intervention of internet gaming disorder (IGD), which has emerged as a significant concern among young individuals. Prevalence estimates vary between 0.7% and 15.6%, and its recognition in the International Classification of Diseases, 11th Revision and Diagnostic and Statistical Manual of Mental Disorders, 5th Edition underscores its impact on academic functioning, social isolation, and mental health challenges. Objective This study aimed to uncover digital phenotypes for the early detection of IGD among adolescents in learning settings. By leveraging sensor data collected from student tablets, the overarching objective is to incorporate these digital indicators into daily school activities to establish these markers as a mental health screening tool, facilitating the early identification and intervention for IGD cases. Methods A total of 168 voluntary participants were engaged, consisting of 85 students with IGD and 83 students without IGD. There were 53% (89/168) female and 47% (79/168) male individuals, all within the age range of 13-14 years. The individual students learned their Korean literature and mathematics lessons on their personal tablets, with sensor data being automatically collected. Multiple regression with bootstrapping and multivariate ANOVA were used, prioritizing interpretability over predictability, for cross-validation purposes. Results A negative correlation between IGD Scale (IGDS) scores and learning outcomes emerged (r166=–0.15; P=.047), suggesting that higher IGDS scores were associated with lower learning outcomes. Multiple regression identified 5 key indicators linked to IGD, explaining 23% of the IGDS score variance: stroke acceleration (β=.33; P<.001), time interval between keys (β=–0.26; P=.01), word spacing (β=–0.25; P<.001), deletion (β=–0.24; P<.001), and horizontal length of strokes (β=–0.21; P=.02). Multivariate ANOVA cross-validated these findings, revealing significant differences in digital phenotypes between potential IGD and non-IGD groups. The average effect size, measured by Cohen d, across the indicators was 0.40, indicating a moderate effect. Notable distinctions included faster stroke acceleration (Cohen d=0.68; P=<.001), reduced word spacing (Cohen d=.57; P=<.001), decreased deletion behavior (Cohen d=0.33; P=.04), and longer horizontal strokes (Cohen d=0.34; P=.03) in students with potential IGD compared to their counterparts without IGD. Conclusions The aggregated findings show a negative correlation between IGD and learning performance, highlighting the effectiveness of digital markers in detecting IGD. This underscores the importance of digital phenotyping in advancing mental health care within educational settings. As schools adopt a 1-device-per-student framework, digital phenotyping emerges as a promising early detection method for IGD. This shift could transform clinical approaches from reactive to proactive measures.

show abstract

Drawing Test Form for Depression: The Development of Drawing Tests for Predicting Depression Among Breast Cancer Patients

Cited by 4 publications

References 17 publications

The Application of Human Figure Drawing as a Supplementary Tool for Depression Screening

The Application of Human Figure Drawing as a Supplementary Tool for Depression Screening

Tree Imagery in Drawing Tests for Screening Mental Disorders: A Systematic Review and Meta-analysis

Digital Phenotypes for Early Detection of Internet Gaming Disorder in Adolescent Students: Explorative Data-Driven Study

Contact Info

Product

Resources

About