Using machine learning to explore core risk factors associated with the risk of eating disorders among non-clinical young women in China: A decision-tree classification analysis

Ren, Yaoxiang; Lü, C.; Yang, Harrison Hao; Ma, Qianyue; Barnhart, Wesley R.; Zhou, Jianjun; He, Jinbo

doi:10.1186/s40337-022-00545-6

Cited by 18 publications

(13 citation statements)

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“…The EDE‐QS showed good psychometric properties in a sample of Chinese young adults (e.g., high internal consistency reliability and good convergent validity; He, Sun, & Fan, 2021). According to Prnjak et al (2020), a total score

\geq

15 indicates probable ED cases, which has also been used in a sample of Chinese young adult women for screening EDs (Ren et al, 2022). In this study, the EDE‐QS had a Cronbach's α value of .89.…”

Section: Methodsmentioning

confidence: 99%

Transgender congruence, body appreciation, body dissatisfaction, and disordered eating in Chinese transgender adults

Barnhart

Cui

et al. 2023

Intl J Eating Disorders

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Objective Ample evidence shows that transgender congruence is negatively associated with body dissatisfaction and disordered eating in the Western context; however, limited research has explored these relationships in non‐Western populations (e.g., Chinese transgender adults). Moreover, to our knowledge, there has been no research describing disordered eating in Chinese transgender adults. Thus, this study aimed to explore group differences in and relationships between transgender congruence, body appreciation, body dissatisfaction, and disordered eating in Chinese transgender adults. Methods This study examined transgender congruence, body appreciation, body dissatisfaction, and disordered eating in a sample of Chinese transgender adults (N = 200). Chi‐square, F tests, and correlation analyses were conducted to examine group differences and relationships between study variables. Results Of the transgender adults, 31.0% screened positive as probable eating disorder (ED) cases. There were no gender identity differences in the study variables. Transgender congruence was positively associated body appreciation (r = .40, p < .001) and negatively associated with body dissatisfaction (r = −.26, p < .001) and disordered eating (r = −.15, p = .031). Discussion Chinese transgender adults may be at high risk of EDs. Transgender congruence and body appreciation may serve a protective role in the context of eating pathology in Chinese transgender adults. Future research is needed to validate the observed relationships between transgender congruence, body appreciation, body dissatisfaction, and disordered eating in Chinese transgender adults. Public Significance Research on disordered eating and body image in Chinese transgender adults is limited. This study describes disordered eating and its relations with transgender congruence, body appreciation, and body dissatisfaction in Chinese transgender adults. Findings highlight the need for and implications of ED intervention in Chinese transgender adults.

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Section: Methodsmentioning

confidence: 99%

Transgender congruence, body appreciation, body dissatisfaction, and disordered eating in Chinese transgender adults

Barnhart

Cui

et al. 2023

Intl J Eating Disorders

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“…In the field of eating disorders, supervised ML is primarily being used to predict people’s eating disorder status and to identify the most relevant predictors for eating disorders. Recent research has used ML to predict recurrent binge-eating behaviour [ 30 ] and eating disorder status [ 31 – 34 ] using cross-sectional surveys with validated measures of known eating disorder risk factors (see Table 1 for a summary of current research using survey predictors). Further, a longitudinal study used ML to predict future eating disorder outcomes among a sample of female patients over a two-year period from 33 self-report measures with 78% accuracy [ 26 ].…”

Section: Implications For Detectionmentioning

confidence: 99%

Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions

2022

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Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using machine learning in the field of eating disorders is just beginning to emerge. This paper provides a narrative review of existing research and explores potential benefits, limitations, and ethical considerations of using machine learning to aid in the detection, prevention, and treatment of eating disorders. Current research primarily uses machine learning to predict eating disorder status from females’ responses to validated surveys, social media posts, or neuroimaging data often with relatively high levels of accuracy. This early work provides evidence for the potential of machine learning to improve current eating disorder screening methods. However, the ability of these algorithms to generalise to other samples or be used on a mass scale is only beginning to be explored. One key benefit of machine learning over traditional statistical methods is the ability of machine learning to simultaneously examine large numbers (100s to 1000s) of multimodal predictors and their complex non-linear interactions, but few studies have explored this potential in the field of eating disorders. Machine learning is also being used to develop chatbots to provide psychoeducation and coping skills training around body image and eating disorders, with implications for early intervention. The use of machine learning to personalise treatment options, provide ecological momentary interventions, and aid the work of clinicians is also discussed. Machine learning provides vast potential for the accurate, rapid, and cost-effective detection, prevention, and treatment of eating disorders. More research is needed with large samples of diverse participants to ensure that machine learning models are accurate, unbiased, and generalisable to all people with eating disorders. There are important limitations and ethical considerations with utilising machine learning methods in practice. Thus, rather than a magical solution, machine learning should be seen as an important tool to aid the work of researchers, and eventually clinicians, in the early identification, prevention, and treatment of eating disorders.

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“…It emphasizes follow-up and monitoring to track progress and prevent relapse, while also incorporating crisis management protocols for acute situations. By implementing this algorithm, colleges can provide comprehensive support, promoting student well-being and academic success [8].…”

Section: Introductionmentioning

confidence: 99%

Intervention Algorithm for College Students' Psychological Problems Based on Decision Tree

2024

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College students often face a variety of psychological problems due to the transition to higher education, academic pressure, social challenges, and lifestyle changes. Common psychological issues among college students include stress, anxiety, depression, loneliness, and adjustment difficulties. These problems can significantly impact students' academic performance, overall well-being, and quality of life. Factors such as academic workload, financial stress, relationship issues, and homesickness can exacerbate psychological distress among college students. This paper presents an innovative approach to addressing college student mental health challenges through the integration of a Recommender System with Psychometric Data Analytics (RC-PDA). With rising concerns about the psychological well-being of college students, there is a growing need for personalized and targeted interventions to support their mental health needs. RC-PDA offers a novel solution by harnessing psychometric data and decision tree algorithms to provide personalized assessments and interventions tailored to individual student profiles. Through the analysis of psychometric data, RC-PDA accurately predicts student well-being levels and guides the implementation of interventions aimed at alleviating stress and promoting mental health. This paper discusses the methodology and findings of a study that evaluates the effectiveness of RC-PDA in supporting college student mental health. Through the analysis of psychometric data, RC-PDA accurately predicts student well-being levels with an average accuracy of 85% and guides the implementation of interventions aimed at alleviating stress and promoting mental health. This paper discusses the methodology and findings of a study that evaluates the effectiveness of RC-PDA in supporting college student mental health. Results demonstrate the utility of RC-PDA in predicting student well-being levels and reducing stress levels by an average of 20% through targeted interventions.

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Using machine learning to explore core risk factors associated with the risk of eating disorders among non-clinical young women in China: A decision-tree classification analysis

Cited by 18 publications

References 83 publications

Transgender congruence, body appreciation, body dissatisfaction, and disordered eating in Chinese transgender adults

Transgender congruence, body appreciation, body dissatisfaction, and disordered eating in Chinese transgender adults

Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions

Intervention Algorithm for College Students' Psychological Problems Based on Decision Tree

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