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Chen MY, Chen P, An FR, Sha S, Feng Y, Su Z, Cheung T, Ungvari GS, Ng CH, Zhang L, Xiang YT. Depression, anxiety and suicidality among Chinese mental health professionals immediately after China's dynamic zero-COVID policy: A network perspective. J Affect Disord 2024; 352:153-162. [PMID: 38316260 DOI: 10.1016/j.jad.2024.01.270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024]
Abstract
BACKGROUND Using network analysis, the interactions between mental health problems at the symptom level can be explored in depth. This study examined the network structure of depressive and anxiety symptoms and suicidality among mental health professionals after the end of China's Dynamic Zero-COVID Policy. METHODS A total of 10,647 mental health professionals were recruited nationwide from January to February 2023. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality was defined by a 'yes' response to any of the standard questions regarding suicidal ideation (SI), suicide plan (SP) and suicide attempt (SA). Expected Influence (EI) and Bridge Expected Influence (bEI) were used as centrality indices in the symptom network to characterize the structure of the symptoms. RESULTS The prevalence of depression, anxiety, and suicidality were 45.99 %, 28.40 %, and 7.71 %, respectively. The network analysis identified GAD5 ("Restlessness") as the most central symptom, followed by PHQ4 ("Fatigue") and GAD7 ("Feeling afraid"). Additionally, PHQ6 ("Guilt"), GAD5 ("Restlessness"), and PHQ8 ("Motor disturbance") were bridge nodes linking depressive and anxiety symptoms with suicidality. The flow network indicated that the strongest connections of S ("Suicidality") was with PHQ6 ("Guilt"), GAD7 ("Feeling afraid"), and PHQ2 ("Sad mood"). CONCLUSIONS Depression, anxiety, and suicidality among mental health professionals were highly prevalent after China's Dynamic Zero-COVID Policy ended. Effective measures should target central and bridge symptoms identified in this network model to address the mental health problems in those at-risk.
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Affiliation(s)
- Meng-Yi Chen
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China
| | - Pan Chen
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China
| | - Feng-Rong An
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sha Sha
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuan Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Zhaohui Su
- School of Public Health, Southeast University, Nanjing, China
| | - Teris Cheung
- School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Gabor S Ungvari
- Psychiatry Section, University of Notre Dame Australia, Fremantle, Australia; Division of Psychiatry, School of Medicine, University of Western Australia, Perth, Australia
| | - Chee H Ng
- Department of Psychiatry, The Melbourne Clinic and St Vincent's Hospital, University of Melbourne, Richmond, Victoria, Australia.
| | - Ling Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China.
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Schat E, Tuerlinckx F, De Ketelaere B, Ceulemans E. Real-time detection of mean and variance changes in experience sampling data: A comparison of existing and novel statistical process control approaches. Behav Res Methods 2024; 56:1459-1475. [PMID: 37118646 DOI: 10.3758/s13428-023-02103-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 04/30/2023]
Abstract
Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e.,s 2 , s , ln( s )). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA-S 2 , EWMA-ln(S 2 ), and EWMA- X ¯ -S 2 . We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA-S 2 and EWMA-ln(S 2 ); the performance difference with EWMA- X ¯ -S 2 is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects.
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Affiliation(s)
- Evelien Schat
- Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium.
| | - Francis Tuerlinckx
- Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium
| | - Bart De Ketelaere
- Mechatronics, Biostatistics and Sensors, Department of Biosystems, KU Leuven, Leuven, Belgium
| | - Eva Ceulemans
- Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium
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Lee J, Lee D, Ihm H, Kang HS, Yu H, Yoon J, Jang Y, Kim Y, Lee CW, Lee H, Baek JH, Ha TH, Park J, Myung W. Network structure of symptomatology of adult attention-deficit hyperactivity disorder in patients with mood disorders. Eur Arch Psychiatry Clin Neurosci 2023:10.1007/s00406-023-01719-2. [PMID: 38055014 DOI: 10.1007/s00406-023-01719-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/04/2023] [Indexed: 12/07/2023]
Abstract
Patients with mood disorders commonly manifest comorbid psychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD). However, few studies have evaluated ADHD symptoms in this population. The current study aimed to explore the network structure of ADHD symptomology and identify central symptoms in patients with mood disorders. The Korean version of the Adult ADHD Self-Report Scale was used to assess the overall ADHD symptoms in 1,086 individuals diagnosed with mood disorders (major depressive disorder [n = 373], bipolar I disorder [n = 314], and bipolar II disorder [n = 399]). We used exploratory graph analysis to detect the number of communities, and the network structure was analyzed using regularized partial correlation models. We identified the central ADHD symptom using centrality indices. Network comparison tests were conducted with different subgroups of patients with mood disorders, including three mood diagnosis groups, between the patients who met the diagnostic criteria for ADHD [ADHD-suspected, n = 259] in their self-report and the others [ADHD-non-suspected, n = 827], and groups with high [n = 503] versus low [n = 252] levels of depressive state. The network analysis detected four communities: disorganization, agitation/restlessness, hyperactivity/impulsivity, and inattention. The centrality indices indicated that "feeling restless" was the core ADHD symptom. The result was replicated in the subgroup analyses within our clinically diverse population of mood disorders, encompassing three presentations: Patients with suspected ADHD, patients without suspected ADHD, and patients with a high depressive state. Our findings reveal that "feeling restless" is the central ADHD symptom. The treatment intervention for "feeling restless" may thus play a pivotal role in tackling ADHD symptoms in adult patients with mood disorders.
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Affiliation(s)
- Jakyung Lee
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Daseul Lee
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - HongKyu Ihm
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Hyo Shin Kang
- Department of Psychology, Kyungpook National University, 80 Daehak-Ro, Buk Gu, Daegu, 41566, Republic of Korea
| | - Hyeona Yu
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Joohyun Yoon
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Yoonjeong Jang
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Yuna Kim
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Chan Woo Lee
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Hyukjun Lee
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
| | - Ji Hyun Baek
- Department of Psychiatry, School of Medicine, Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
| | - Tae Hyon Ha
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jungkyu Park
- Department of Psychology, Kyungpook National University, 80 Daehak-Ro, Buk Gu, Daegu, 41566, Republic of Korea.
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University, Bundang Hospital 29, Gumi-Ro 173 Beon-Gil Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13619, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Ross MK, Tulabandhula T, Bennett CC, Baek E, Kim D, Hussain F, Demos AP, Ning E, Langenecker SA, Ajilore O, Leow AD. A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity. SENSORS (BASEL, SWITZERLAND) 2023; 23:1585. [PMID: 36772625 PMCID: PMC9920816 DOI: 10.3390/s23031585] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/11/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.
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Affiliation(s)
- Mindy K. Ross
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Theja Tulabandhula
- Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Casey C. Bennett
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
- Department of Computing, DePaul University, Chicago, IL 60604, USA
| | - EuGene Baek
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
| | - Dohyeon Kim
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
| | - Faraz Hussain
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Alexander P. Demos
- Department of Psychology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Emma Ning
- Department of Psychology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | | | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60612, USA
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