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Han J, Li H, Lin H, Wu P, Wang S, Tu J, Lu J. Depression prediction based on LassoNet-RNN model: A longitudinal study. Heliyon 2023; 9:e20684. [PMID: 37842633 PMCID: PMC10570602 DOI: 10.1016/j.heliyon.2023.e20684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/21/2023] [Accepted: 10/04/2023] [Indexed: 10/17/2023] Open
Abstract
Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health.
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Affiliation(s)
- Jiatong Han
- School of Computer Science, Nanjing Audit University, China
| | - Hao Li
- School of Computer Science, Nanjing Audit University, China
| | - Han Lin
- Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit, Nanjing Audit University, China
| | - Pingping Wu
- Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit, Nanjing Audit University, China
| | - Shidan Wang
- School of Computer Science, Nanjing Audit University, China
| | - Juan Tu
- Key Laboratory of Modern Acoustics (MOE), School of Physics, Nanjing University, China
| | - Jing Lu
- Key Laboratory of Modern Acoustics (MOE), School of Physics, Nanjing University, China
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2
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Eysenbach G, Li S, Huang F, Wen Y, Wang X, Liu X, Li L, Zhu T. Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study. J Med Internet Res 2023; 25:e41823. [PMID: 36719723 PMCID: PMC9929724 DOI: 10.2196/41823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/25/2022] [Accepted: 12/19/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users' PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB. OBJECTIVE This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. METHODS We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability. RESULTS The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model's structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001). CONCLUSIONS By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study.
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Affiliation(s)
| | - Sijia Li
- Department of Social Work and Social Administration, The Unversity of Hong Kong, Hong Kong SAR, Hong Kong
| | - Feng Huang
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yeye Wen
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyang Wang
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Xiaoqian Liu
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Linyan Li
- School of Data Science, City University of Hong Kong, Hong Kong SAR, Hong Kong.,Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, Hong Kong
| | - Tingshao Zhu
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Deng H, Zhao N, Wang Y. Identifying Chinese social media users' need for affect from their online behaviors. Front Public Health 2023; 10:1045279. [PMID: 36703844 PMCID: PMC9871915 DOI: 10.3389/fpubh.2022.1045279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
The need for affect (NFA), which refers to the motivation to approach or avoid emotion-inducing situations, is a valuable indicator of mental health monitoring and intervention, as well as many other applications. Traditionally, NFA has been measured using self-reports, which is not applicable in today's online scenarios due to its shortcomings in fast, large-scale assessments. This study proposed an automatic and non-invasive method for recognizing NFA based on social media behavioral data. The NFA questionnaire scores of 934 participants and their social media data were acquired. Then we run machine learning algorithms to train predictive models, which can be used to automatically identify NFA degrees of online users. The results showed that Extreme Gradient Boosting (XGB) performed best among several algorithms. The Pearson correlation coefficients between predicted scores and NFA questionnaire scores achieved 0.25 (NFA avoidance), 0.31 (NFA approach) and 0.34 (NFA total), and the split-half reliabilities were 0.66-0.70. Our research demonstrated that adolescents' NFA can be identified based on their social media behaviors, and opened a novel way of non-intrusively perceiving users' NFA which can be used for mental health monitoring and other situations that require large-scale NFA measurements.
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Affiliation(s)
- Hong Deng
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Nan Zhao
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,*Correspondence: Nan Zhao ✉
| | - Yilin Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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4
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Li Y, Li C, Zhang T, Wu L, Lin X, Li Y, Wang L, Yang H, Lu D, Miao D, Fang P. Questionnaires based on natural language processing elicit immersive ruminative thinking in ruminators: Evidence from behavioral responses and EEG data. Front Neurosci 2023; 17:1118650. [PMID: 36950128 PMCID: PMC10025410 DOI: 10.3389/fnins.2023.1118650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/06/2023] [Indexed: 03/08/2023] Open
Abstract
Rumination is closely related to mental disorders and can thus be used as a marker of their presence or a predictor of their development. The presence of masking and fabrication in psychological selection can lead to inaccurate detection of psychological disorders. Human language is considered crucial in eliciting specific conscious activities, and the use of natural language processing (NLP) in the development of questionnaires for psychological tests has the potential to elicit immersive ruminative thinking, leading to changes in neural activity. Electroencephalography (EEG) is commonly used to detect and record neural activity in the human brain and is sensitive to changes in brain activity. In this study, we used NLP to develop a questionnaire to induce ruminative thinking and then recorded the EEG signals in response to the questionnaire. The behavioral results revealed that ruminators exhibited higher arousal rates and longer reaction times, specifically in response to the ruminative items of the questionnaire. The EEG results showed no significant difference between the ruminators and the control group during the resting state; however, a significant alteration in the coherence of the entire brain of the ruminators existed while they were answering the ruminative items. No differences were found in the control participants while answering the two items. These behavioral and EEG results indicate that the questionnaire elicited immersive ruminative thinking, specifically in the ruminators. Therefore, the questionnaire designed using NLP is capable of eliciting ruminative thinking in ruminators, offering a promising approach for the early detection of mental disorders in psychological selection.
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Affiliation(s)
- Yulong Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Tian Zhang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Lin Wu
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Xinxin Lin
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Yijun Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Lingling Wang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Huilin Yang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Diyan Lu
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Danmin Miao
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
- Key Laboratory of Military Medical Psychology and Stress Support of PLA, Xi'an, China
- *Correspondence: Danmin Miao
| | - Peng Fang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
- Key Laboratory of Military Medical Psychology and Stress Support of PLA, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Peng Fang
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Yu Y, Li Q, Liu X. Automatic anxiety recognition method based on microblog text analysis. Front Public Health 2023; 11:1080013. [PMID: 37020823 PMCID: PMC10067666 DOI: 10.3389/fpubh.2023.1080013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/02/2023] [Indexed: 04/07/2023] Open
Abstract
Mental health has traditionally been assessed using a self-report questionnaire. Although this approach produces accurate results, it has the disadvantage of being labor-intense and time-consuming. This study aimed to extract original text information published by users on the social media platform (Sina Weibo). A machine learning method was used to train the model and predict the anxiety state of the user automatically. Data of 1,039 users were collected. First, Weibo users were invited to fill the anxiety self-assessment scale. All original text data ever published by the users were collected. Second, the Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) were extracted for feature selection and model training. We found that the model achieved the best performance when the XGBoostRegressor algorithm was used. The Pearson correlation coefficient between the model predicted scores and self-reported scores was moderate (r = 0.322). In addition, we tested the reliability of the model, and found that the model had high reliability (r = 0.72). The experimental results further showed that the model was feasible and effective and could use the digital footprints to predict psychological characteristics.
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Affiliation(s)
- Yang Yu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Learning and Cognition Key Laboratory of Beijing, School of Psychology, Capital Normal University, Beijing, China
| | - Qi Li
- Learning and Cognition Key Laboratory of Beijing, School of Psychology, Capital Normal University, Beijing, China
| | - Xiaoqian Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Xiaoqian Liu,
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Han N, Li S, Huang F, Wen Y, Su Y, Li L, Liu X, Zhu T. How social media expression can reveal personality. Front Psychiatry 2023; 14:1052844. [PMID: 36937737 PMCID: PMC10017531 DOI: 10.3389/fpsyt.2023.1052844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Background Personality psychology studies personality and its variation among individuals and is an essential branch of psychology. In recent years, machine learning research related to personality assessment has started to focus on the online environment and showed outstanding performance in personality assessment. However, the aspects of the personality of these prediction models measure remain unclear because few studies focus on the interpretability of personality prediction models. The objective of this study is to develop and validate a machine learning model with domain knowledge introduced to enhance accuracy and improve interpretability. Methods Study participants were recruited via an online experiment platform. After excluding unqualified participants and downloading the Weibo posts of eligible participants, we used six psycholinguistic and mental health-related lexicons to extract textual features. Then the predictive personality model was developed using the multi-objective extra trees method based on 3,411 pairs of social media expression and personality trait scores. Subsequently, the prediction model's validity and reliability were evaluated, and each lexicon's feature importance was calculated. Finally, the interpretability of the machine learning model was discussed. Results The features from Culture Value Dictionary were found to be the most important predictors. The fivefold cross-validation results regarding the prediction model for personality traits ranged between 0.44 and 0.48 (p < 0.001). The correlation coefficients of five personality traits between the two "split-half" datasets data ranged from 0.84 to 0.88 (p < 0.001). Moreover, the model performed well in terms of contractual validity. Conclusion By introducing domain knowledge to the development of a machine learning model, this study not only ensures the reliability and validity of the prediction model but also improves the interpretability of the machine learning method. The study helps explain aspects of personality measured by such prediction models and finds a link between personality and mental health. Our research also has positive implications regarding the combination of machine learning approaches and domain knowledge in the field of psychiatry and its applications to mental health.
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Affiliation(s)
- Nuo Han
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Sijia Li
- Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Feng Huang
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Yeye Wen
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Yue Su
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Linyan Li
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Xiaoqian Liu
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- *Correspondence: Xiaoqian Liu,
| | - Tingshao Zhu
- Chinese Academy Sciences Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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7
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Wang Y, Zhao N. Prediction model of interaction anxiousness based on Weibo data. Front Public Health 2022; 10:1045605. [PMID: 36424965 PMCID: PMC9679504 DOI: 10.3389/fpubh.2022.1045605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
Adolescents who face social distress in real life are often accompanied by interaction anxiousness. To avoid direct social activities, they prefer to indulge in social networks to satisfy their psychological needs for interpersonal communication. Sina Weibo, China's leading social media platform, has a markedly young user base. It provides a rich sample of adolescents with interaction anxiousness and conditions for real-time monitoring. In this study, various word categories, such as perception of spatial distance and positional relationships, morality, and emotion, showed a significant relationship with interaction anxiousness. Furthermore, prediction models were established based on the original Weibo data of 839 active Sina Weibo users through a variety of machine learning algorithms to predict the scores of users' interaction anxiousness. The results showed that the performance of the prediction model established by the fully connected neural network was the best, and both criterion validity and split-half reliability were good (r criterionvalidity = 0.30, r split - halfreliability = 0.76). This study confirms the validity of the prediction model of interaction anxiousness based on social media behavior data, provides a feasible solution to examine adolescents' interaction anxiousness, and provides a scientific basis for more targeted mental health interventions.
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Affiliation(s)
- Yilin Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Nan Zhao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,*Correspondence: Nan Zhao
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8
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Wei XY, Wang X, Liu CZ. Functional connectome of vicarious traumatization: detecting and helping individuals vulnerable to mental illness. Psychoradiology 2022; 2:52-53. [PMID: 38665966 PMCID: PMC10917157 DOI: 10.1093/psyrad/kkac007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 04/28/2024]
Affiliation(s)
- Xiao-Ya Wei
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing 100029, China
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xu Wang
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Cun-Zhi Liu
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China
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9
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Guo Y, Liu X, Wang X, Zhu T, Zhan W. Automatic Decision-Making Style Recognition Method Using Kinect Technology. Front Psychol 2022; 13:751914. [PMID: 35310212 PMCID: PMC8931824 DOI: 10.3389/fpsyg.2022.751914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years, somatosensory interaction technology, represented by Microsoft's Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.
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Affiliation(s)
- Yu Guo
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoqian Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyang Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Tingshao Zhu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wei Zhan
- Information Science Research Institute, China Electronics Technology Group Corporation, Beijing, China
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Huang Y, Zhai D, Song J, Rao X, Sun X, Tang J. Mental states and personality based on real-time physical activity and facial expression recognition. Front Psychiatry 2022; 13:1019043. [PMID: 36699483 PMCID: PMC9868243 DOI: 10.3389/fpsyt.2022.1019043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 12/09/2022] [Indexed: 01/10/2023] Open
Abstract
INTRODUCTION To explore a quick and non-invasive way to measure individual psychological states, this study developed interview-based scales, and multi-modal information was collected from 172 participants. METHODS We developed the Interview Psychological Symptom Inventory (IPSI) which eventually retained 53 items with nine main factors. All of them performed well in terms of reliability and validity. We used optimized convolutional neural networks and original detection algorithms for the recognition of individual facial expressions and physical activity based on Russell's circumplex model and the five factor model. RESULTS We found that there was a significant correlation between the developed scale and the participants' scores on each factor in the Symptom Checklist-90 (SCL-90) and Big Five Inventory (BFI-2) [r = (-0.257, 0.632), p < 0.01]. Among the multi-modal data, the arousal of facial expressions was significantly correlated with the interval of validity (p < 0.01), valence was significantly correlated with IPSI and SCL-90, and physical activity was significantly correlated with gender, age, and factors of the scales. DISCUSSION Our research demonstrates that mental health can be monitored and assessed remotely by collecting and analyzing multimodal data from individuals captured by digital tools.
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Affiliation(s)
- Yating Huang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
| | - Dengyue Zhai
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jingze Song
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China.,ZhongJuYuan Intelligent Technology Co., Ltd., Hefei, China
| | - Xuanheng Rao
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
| | - Xiao Sun
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
| | - Jin Tang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
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