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Büscher R, Winkler T, Mocellin J, Homan S, Josifovski N, Ciharova M, van Breda W, Kwon S, Larsen ME, Torous J, Firth J, Sander LB. A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors. NPJ MENTAL HEALTH RESEARCH 2024; 3:42. [PMID: 39313519 PMCID: PMC11420362 DOI: 10.1038/s44184-024-00089-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 09/12/2024] [Indexed: 09/25/2024]
Abstract
Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.
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Affiliation(s)
- Rebekka Büscher
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Tanita Winkler
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jacopo Mocellin
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Natasha Josifovski
- Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
| | - Marketa Ciharova
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
- Amsterdam Public Health Research Institute, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ward van Breda
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sam Kwon
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Mark E Larsen
- Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
- Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Joseph Firth
- Division of Psychology and Mental Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Lasse B Sander
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Chen T, Niu L, Zhu J, Hou X, Tao H, Ma Y, Silenzio V, Lin K, Zhou L. Effects of frequent assessments on the severity of suicidal thoughts: an ecological momentary assessment study. Front Public Health 2024; 12:1358604. [PMID: 38827619 PMCID: PMC11141048 DOI: 10.3389/fpubh.2024.1358604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/25/2024] [Indexed: 06/04/2024] Open
Abstract
Objective In recent years, there has been a significant increase in research using ecological momentary assessment (EMA) to explore suicidal thoughts and behaviors (STBs). Meanwhile, concerns have been raised regarding the potential impacts of frequent and intense STBs assessments on the study participants. Methods From November 2021 to June 2023, a total of 83 adolescent and young adult outpatients (Mage = 21.0, SDage = 6.3, 71.1% female), who were diagnosed with mood disorders, were recruited from three psychiatric clinics in China. Smartphone-based EMA was used to measure suicidal thoughts three times per day at randomly selected times. We examined the change of suicidal thoughts in each measurement and within 1 day to evaluate potential adverse effects using Bayesian multilevel models. Results The 3,105 effective surveys were nested in 83 participants (median follow-up days: 14 days). The results of two-level models indicated that suicidal thoughts decreased during the monitoring period. However, this effect varied among different individuals in the two-level model. Conclusion Our findings did not support the notion that repeated assessment of suicidal thoughts is iatrogenic, but future research should continue to investigate the impact of frequent assessment on suicidal thoughts, taking into account individual differences and utilizing larger sample sizes.
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Affiliation(s)
- Tengwei Chen
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
| | - Lu Niu
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
| | - Jiaxin Zhu
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
| | - Xiaofei Hou
- Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China
| | - Haojuan Tao
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yarong Ma
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Vincent Silenzio
- Urban-Global Public Health, Rutgers School of Public Health, Rutgers, The State University of New Jersey, Newark, NJ, United States
| | - Kangguang Lin
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- School of Health and Life Sciences University of Health and Rehabilitation Sciences, Qingdao, China
| | - Liang Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
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Khoo LS, Lim MK, Chong CY, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:348. [PMID: 38257440 PMCID: PMC10820860 DOI: 10.3390/s24020348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.
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Affiliation(s)
- Lin Sze Khoo
- Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| | - Mei Kuan Lim
- School of Information Technology, Monash University Malaysia, Subang Jaya 46150, Malaysia; (M.K.L.); (C.Y.C.)
| | - Chun Yong Chong
- School of Information Technology, Monash University Malaysia, Subang Jaya 46150, Malaysia; (M.K.L.); (C.Y.C.)
| | - Roisin McNaney
- Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
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Lei C, Qu D, Liu K, Chen R. Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation Among Sexual and Gender Minority Individuals. JAMA Netw Open 2023; 6:e2333164. [PMID: 37695580 PMCID: PMC10495869 DOI: 10.1001/jamanetworkopen.2023.33164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/03/2023] [Indexed: 09/12/2023] Open
Abstract
Importance Suicidality poses a serious global health concern, particularly in the sexual and gender minority population. While various studies have focused on investigating chronic stressors, the precise prediction effect of daily experiences on suicide ideation remains uncertain. Objective To test the extent to which mood fluctuations and contextual stressful events experienced by sexual and gender minority individuals may predict later short- and long-term suicide ideation. Design, Setting, and Participants This diagnostic study collected twice-daily data on mood states and stressful events from sexual and gender minority individuals over 25 days throughout 3 waves of the Chinese Lunar New Year (before, during, and after), and follow-up surveys assessing suicidal ideation were conducted 1, 3, and 8 months later. Online recruitment advertisements were used to recruit young adults throughout China. Eligible participants were self-identified as sexual and gender minority individuals aged 18 to 29 years. Those who were diagnosed with psychotic disorders (eg, schizophrenia spectrum or schizotypal disorder) or prevented from objective factors (ie, not having a phone or having an irregular sleep rhythm) were excluded. Data were collected from January to October 2022. Main Outcomes and Measures To predict short-term (1 month) and longer-term (3 and 8 months) suicidal ideation, the study tested several approaches by using machine learning including chronic stress baseline data (baseline approach), dynamic patterns of mood states and stressful events (ecological momentary assessment [EMA] approach), and a combination of baseline data and dynamic patterns (EMA plus baseline approach). Results A total of 103 sexual and gender minority individuals participated in the study (mean [SD] age, 24.2 [2.5] years; 72 [70%] female). Of these, 19 (18.4%; 95% CI, 10.9%-25.9%), 25 (24.8%; 95% CI, 16.4%-33.2%), 30 (29.4%; 95% CI, 20.6%-38.2%), and 32 (31.1%; 95% CI, 22.2%-40.0%) reported suicidal ideation at baseline, 1, 3, and 8 months follow-up, respectively. The EMA approach showed better performance than the baseline and baseline plus EMA approaches at 1-month follow-up (area under the receiver operating characteristic curve [AUC], 0.80; 95% CI, 0.78-0.81) and slightly better performance on the prediction of suicidal ideation at 3 and 8 months' follow-up. In addition, the best approach predicting suicidal ideation was obtained during Lunar New Year period at 1-month follow-up, which had a mean AUC of 0.77 (95% CI, 0.74-0.79) and better performance at 3 and 8 months' follow-up (AUC, 0.74; 95% CI, 0.72-0.76 and AUC, 0.72; 95% CI, 0.69-0.74, respectively). Conclusions and Relevance The findings in this study emphasize the importance of contextual risk factors experienced by sexual and gender minority individuals at different stages. The use of machine learning may facilitate the identification of individuals who are at risk and aid in the development of personalized process-based early prevention programs to mitigate future suicide risk.
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Affiliation(s)
- Chang Lei
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Diyang Qu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Kunxu Liu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Runsen Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
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Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. NPJ Digit Med 2023; 6:84. [PMID: 37147384 PMCID: PMC10163239 DOI: 10.1038/s41746-023-00828-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/19/2023] [Indexed: 05/07/2023] Open
Abstract
Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
- College of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar
| | - Farag Shuweihdi
- School of Medicine, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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