Pub Date : 2024-01-22DOI: 10.1038/s44184-023-00047-6
Bethanie Maples, Merve Cerit, Aditya Vishwanath, Roy Pea
Mental health is a crisis for learners globally, and digital support is increasingly seen as a critical resource. Concurrently, Intelligent Social Agents receive exponentially more engagement than other conversational systems, but their use in digital therapy provision is nascent. A survey of 1006 student users of the Intelligent Social Agent, Replika, investigated participants’ loneliness, perceived social support, use patterns, and beliefs about Replika. We found participants were more lonely than typical student populations but still perceived high social support. Many used Replika in multiple, overlapping ways—as a friend, a therapist, and an intellectual mirror. Many also held overlapping and often conflicting beliefs about Replika—calling it a machine, an intelligence, and a human. Critically, 3% reported that Replika halted their suicidal ideation. A comparative analysis of this group with the wider participant population is provided.
{"title":"Loneliness and suicide mitigation for students using GPT3-enabled chatbots","authors":"Bethanie Maples, Merve Cerit, Aditya Vishwanath, Roy Pea","doi":"10.1038/s44184-023-00047-6","DOIUrl":"10.1038/s44184-023-00047-6","url":null,"abstract":"Mental health is a crisis for learners globally, and digital support is increasingly seen as a critical resource. Concurrently, Intelligent Social Agents receive exponentially more engagement than other conversational systems, but their use in digital therapy provision is nascent. A survey of 1006 student users of the Intelligent Social Agent, Replika, investigated participants’ loneliness, perceived social support, use patterns, and beliefs about Replika. We found participants were more lonely than typical student populations but still perceived high social support. Many used Replika in multiple, overlapping ways—as a friend, a therapist, and an intellectual mirror. Many also held overlapping and often conflicting beliefs about Replika—calling it a machine, an intelligence, and a human. Critically, 3% reported that Replika halted their suicidal ideation. A comparative analysis of this group with the wider participant population is provided.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00047-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139522487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-16DOI: 10.1038/s44184-023-00045-8
Devashru Patel, Steven A. Sumner, Daniel Bowen, Marissa Zwald, Ellen Yard, Jing Wang, Royal Law, Kristin Holland, Theresa Nguyen, Gary Mower, Yushiuan Chen, Jenna Iberg Johnson, Megan Jespersen, Elizabeth Mytty, Jennifer M. Lee, Michael Bauer, Eric Caine, Munmun De Choudhury
Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of −2.768% for Utah, −2.823% for Louisiana, −3.449% for New York, and −5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.
{"title":"Predicting state level suicide fatalities in the united states with realtime data and machine learning","authors":"Devashru Patel, Steven A. Sumner, Daniel Bowen, Marissa Zwald, Ellen Yard, Jing Wang, Royal Law, Kristin Holland, Theresa Nguyen, Gary Mower, Yushiuan Chen, Jenna Iberg Johnson, Megan Jespersen, Elizabeth Mytty, Jennifer M. Lee, Michael Bauer, Eric Caine, Munmun De Choudhury","doi":"10.1038/s44184-023-00045-8","DOIUrl":"10.1038/s44184-023-00045-8","url":null,"abstract":"Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of −2.768% for Utah, −2.823% for Louisiana, −3.449% for New York, and −5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00045-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139474063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.1038/s44184-023-00043-w
Aliaksandr Kazlou, Kateryna Bornukova, Aidan Wickham, Vladimir Slaykovskiy, Kimberly Peven, Anna Klepchukova, Sonia Ponzo, Sarah Garfinkel
The chronic and acute effects of stress can have divergent effects on health; long-term effects are associated with detrimental physical and mental health sequelae, while acute effects may be advantageous in the short-term. Stress-induced analgesia, the attenuation of pain perception due to stress, is a well-known phenomenon that has yet to be systematically investigated under ecological conditions. Using Flo, a women’s health and wellbeing app and menstrual cycle tracker, with a world-wide monthly active usership of more than 57 million, women in Ukraine were monitored for their reporting of stress, pain and affective symptoms before, and immediately after, the onset of the Russian-Ukrainian conflict. To avoid potential selection (attrition) or collider bias, we rely on a sample of 87,315 users who were actively logging multiple symptoms before and after the start of the war. We found an inverse relationship between stress and pain, whereby higher reports of stress predicted lower rates of pain. Stress did not influence any other physiological symptoms with a similar magnitude, nor did any other symptom have a similar effect on pain. This relationship generally decreased in magnitude in countries neighbouring and surrounding Ukraine, with Ukraine serving as the epicentre. These findings help characterise the relationship between stress and health in a real-world setting.
{"title":"Effects of stress on pain in females using a mobile health app in the Russia-Ukraine conflict","authors":"Aliaksandr Kazlou, Kateryna Bornukova, Aidan Wickham, Vladimir Slaykovskiy, Kimberly Peven, Anna Klepchukova, Sonia Ponzo, Sarah Garfinkel","doi":"10.1038/s44184-023-00043-w","DOIUrl":"10.1038/s44184-023-00043-w","url":null,"abstract":"The chronic and acute effects of stress can have divergent effects on health; long-term effects are associated with detrimental physical and mental health sequelae, while acute effects may be advantageous in the short-term. Stress-induced analgesia, the attenuation of pain perception due to stress, is a well-known phenomenon that has yet to be systematically investigated under ecological conditions. Using Flo, a women’s health and wellbeing app and menstrual cycle tracker, with a world-wide monthly active usership of more than 57 million, women in Ukraine were monitored for their reporting of stress, pain and affective symptoms before, and immediately after, the onset of the Russian-Ukrainian conflict. To avoid potential selection (attrition) or collider bias, we rely on a sample of 87,315 users who were actively logging multiple symptoms before and after the start of the war. We found an inverse relationship between stress and pain, whereby higher reports of stress predicted lower rates of pain. Stress did not influence any other physiological symptoms with a similar magnitude, nor did any other symptom have a similar effect on pain. This relationship generally decreased in magnitude in countries neighbouring and surrounding Ukraine, with Ukraine serving as the epicentre. These findings help characterise the relationship between stress and health in a real-world setting.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00043-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139407000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.1038/s44184-023-00041-y
Caitlin A. Stamatis, Jonah Meyerhoff, Yixuan Meng, Zhi Chong Chris Lin, Young Min Cho, Tony Liu, Chris J. Karr, Tingting Liu, Brenda L. Curtis, Lyle H. Ungar, David C. Mohr
While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one’s average was an early signal of PHQ-8 severity (distal β = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (β = 0.198, p = 0.022) and proximal (β = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (β = −0.131, p = 0.035) but did not predict (distal β = 0.034, p = 0.577; medial β = −0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.
{"title":"Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study","authors":"Caitlin A. Stamatis, Jonah Meyerhoff, Yixuan Meng, Zhi Chong Chris Lin, Young Min Cho, Tony Liu, Chris J. Karr, Tingting Liu, Brenda L. Curtis, Lyle H. Ungar, David C. Mohr","doi":"10.1038/s44184-023-00041-y","DOIUrl":"10.1038/s44184-023-00041-y","url":null,"abstract":"While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one’s average was an early signal of PHQ-8 severity (distal β = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (β = 0.198, p = 0.022) and proximal (β = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (β = −0.131, p = 0.035) but did not predict (distal β = 0.034, p = 0.577; medial β = −0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00041-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139110164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1038/s44184-023-00042-x
Ryan C. Yeung, Myra A. Fernandes
Researchers debate whether recurrent involuntary autobiographical memories (IAMs; memories of one’s personal past retrieved unintentionally and repetitively) are pathological or ordinary. While some argue that these memories contribute to clinical disorders, recurrent IAMs are also common in everyday life. Here, we examined how the content of recurrent IAMs might distinguish between those that are maladaptive (related to worse mental health) versus benign (unrelated to mental health). Over two years, 6187 undergraduates completed online surveys about recurrent IAMs; those who experienced recurrent IAMs within the past year were asked to describe their memories, resulting in 3624 text descriptions. Using a previously validated computational approach (structural topic modeling), we identified coherent topics (e.g., “Conversations”, “Experiences with family members”) in recurrent IAMs. Specific topics (e.g., “Negative past relationships”, “Abuse and trauma”) were uniquely related to symptoms of mental health disorders (e.g., depression, PTSD), above and beyond the self-reported valence of these memories. Importantly, we also found that content in recurrent IAMs was distinct across symptom types (e.g., “Communication and miscommunication” was related to social anxiety, but not symptoms of other disorders), suggesting that while negative recurrent IAMs are transdiagnostic, their content remains unique across different types of mental health concerns. Our work shows that topics in recurrent IAMs—and their links to mental health—are identifiable, distinguishable, and quantifiable.
{"title":"Specific topics, specific symptoms: linking the content of recurrent involuntary memories to mental health using computational text analysis","authors":"Ryan C. Yeung, Myra A. Fernandes","doi":"10.1038/s44184-023-00042-x","DOIUrl":"10.1038/s44184-023-00042-x","url":null,"abstract":"Researchers debate whether recurrent involuntary autobiographical memories (IAMs; memories of one’s personal past retrieved unintentionally and repetitively) are pathological or ordinary. While some argue that these memories contribute to clinical disorders, recurrent IAMs are also common in everyday life. Here, we examined how the content of recurrent IAMs might distinguish between those that are maladaptive (related to worse mental health) versus benign (unrelated to mental health). Over two years, 6187 undergraduates completed online surveys about recurrent IAMs; those who experienced recurrent IAMs within the past year were asked to describe their memories, resulting in 3624 text descriptions. Using a previously validated computational approach (structural topic modeling), we identified coherent topics (e.g., “Conversations”, “Experiences with family members”) in recurrent IAMs. Specific topics (e.g., “Negative past relationships”, “Abuse and trauma”) were uniquely related to symptoms of mental health disorders (e.g., depression, PTSD), above and beyond the self-reported valence of these memories. Importantly, we also found that content in recurrent IAMs was distinct across symptom types (e.g., “Communication and miscommunication” was related to social anxiety, but not symptoms of other disorders), suggesting that while negative recurrent IAMs are transdiagnostic, their content remains unique across different types of mental health concerns. Our work shows that topics in recurrent IAMs—and their links to mental health—are identifiable, distinguishable, and quantifiable.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00042-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138867518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.1038/s44184-023-00037-8
Tiffany Junchen Tao, Tsz Wai Li, Li Liang, Huinan Liu, Wai Kai Hou
Cognition and behavior could reciprocally impact each other and together determine mental health amid large-scale disasters such as COVID-19. This study reports a six-month cohort study of a population-representative sample of Hong Kong residents (N = 906) from March–August 2021 (T1) to September 2021–February 2022 (T2). Cross-lagged panel analyses reveal that T1 poor behavioral functioning as indicated by high daily routine disruptions is inversely associated with T2 cognitive adaptation as indicated by self-efficacy and meaning-making but not vice versa. T1 routine disruptions but not cognitive adaptation are positively associated with T2 probable depression/anxiety. The positive link between T1 routine disruptions and T2 probable disorders is mediated by poor cognitive adaptation at T2. The present findings suggest that upholding daily behavioral functioning relative to positive states of mind could have a more pivotal role in mental health amid large-scale disasters. Future studies can test interventions that enhance the sustainment of regular daily routines.
{"title":"Investigating the reciprocity between cognition and behavior in adaptation to large-scale disasters","authors":"Tiffany Junchen Tao, Tsz Wai Li, Li Liang, Huinan Liu, Wai Kai Hou","doi":"10.1038/s44184-023-00037-8","DOIUrl":"10.1038/s44184-023-00037-8","url":null,"abstract":"Cognition and behavior could reciprocally impact each other and together determine mental health amid large-scale disasters such as COVID-19. This study reports a six-month cohort study of a population-representative sample of Hong Kong residents (N = 906) from March–August 2021 (T1) to September 2021–February 2022 (T2). Cross-lagged panel analyses reveal that T1 poor behavioral functioning as indicated by high daily routine disruptions is inversely associated with T2 cognitive adaptation as indicated by self-efficacy and meaning-making but not vice versa. T1 routine disruptions but not cognitive adaptation are positively associated with T2 probable depression/anxiety. The positive link between T1 routine disruptions and T2 probable disorders is mediated by poor cognitive adaptation at T2. The present findings suggest that upholding daily behavioral functioning relative to positive states of mind could have a more pivotal role in mental health amid large-scale disasters. Future studies can test interventions that enhance the sustainment of regular daily routines.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00037-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1038/s44184-023-00040-z
Kaining Mao, Yuqi Wu, Jie Chen
Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.
由于多种原因，包括难以获得医疗服务提供者的帮助，评估精神疾病和确定治疗方法可能很困难。评估和治疗可能不具有连续性，而且会受到精神症状不可预知性的限制。使用在临床环境中收集的数据建立机器学习模型可以改善诊断和治疗。已有研究利用语音、文本和面部表情分析来识别抑郁症。然而，我们仍需要更多的研究来应对挑战，例如临床使用多模态机器学习模型的需求。我们采用《系统综述与元分析首选报告项目》（Preferred Reporting Items for Systematic Reviews and Meta-Analysis，PRISMA）指南，对过去十年利用语音、文本和面部表情分析检测《精神疾病诊断与统计手册》（DSM-5）所定义的抑郁症的研究进行了综述。我们提供了每项研究的参与人数、用于评估临床结果的技术、语音诱导任务、机器学习算法、指标和其他重要发现等信息。共审查了 544 项研究，其中 264 项符合纳入标准。我们创建了一个数据库，其中包含查询结果以及如何利用不同特征检测抑郁症的总结。虽然机器学习在加强心理健康障碍评估方面显示出了潜力，但仍有一些障碍必须克服，尤其是要求用于临床目的的机器学习模型更加透明。考虑到这一领域使用的数据集、特征提取技术和衡量标准多种多样，我们提供了收集数据和训练机器学习模型的指南，以保证在不同情况下的可重复性和通用性。
{"title":"A systematic review on automated clinical depression diagnosis","authors":"Kaining Mao, Yuqi Wu, Jie Chen","doi":"10.1038/s44184-023-00040-z","DOIUrl":"10.1038/s44184-023-00040-z","url":null,"abstract":"Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00040-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138867661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1038/s44184-023-00044-9
Omer A. Syed, Benjamin Tsang
Microdosing psychedelics is a growing practice among recreational users, claimed to improve several aspects of mental health, with little supporting empirical research. In this comment, we highlight the potential role of expectations and confirmation bias underlying therapeutic effects of microdosing, and suggest future avenues of research to address this concern.
{"title":"Managing expectations with psychedelic microdosing","authors":"Omer A. Syed, Benjamin Tsang","doi":"10.1038/s44184-023-00044-9","DOIUrl":"10.1038/s44184-023-00044-9","url":null,"abstract":"Microdosing psychedelics is a growing practice among recreational users, claimed to improve several aspects of mental health, with little supporting empirical research. In this comment, we highlight the potential role of expectations and confirmation bias underlying therapeutic effects of microdosing, and suggest future avenues of research to address this concern.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00044-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1038/s44184-023-00038-7
Leif Simmatis, Emma E. Russo, Joseph Geraci, Irene E. Harmsen, Nardin Samuel
Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals’ responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.
{"title":"Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder","authors":"Leif Simmatis, Emma E. Russo, Joseph Geraci, Irene E. Harmsen, Nardin Samuel","doi":"10.1038/s44184-023-00038-7","DOIUrl":"10.1038/s44184-023-00038-7","url":null,"abstract":"Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals’ responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00038-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-05DOI: 10.1038/s44184-023-00039-6
María P. Raveau, Julián I. Goñi, José F. Rodríguez, Isidora Paiva-Mack, Fernanda Barriga, María P. Hermosilla, Claudio Fuentes-Bravo, Susana Eyheramendy
Over the past few years, the COVID-19 pandemic has exerted various impacts on the world, notably concerning mental health. Nevertheless, the precise influence of psychosocial stressors on this mental health crisis remains largely unexplored. In this study, we employ natural language processing to examine chat text from a mental health helpline. The data was obtained from a chat helpline called Safe Hour from the “It Gets Better” project in Chile. This dataset encompass 10,986 conversations between trained professional volunteers from the foundation and platform users from 2018 to 2020. Our analysis shows a significant increase in conversations covering issues of self-image and interpersonal relations, as well as a decrease in performance themes. Also, we observe that conversations involving themes like self-image and emotional crisis played a role in explaining both suicidal behavior and depressive symptoms. However, anxious symptoms can only be explained by emotional crisis themes. These findings shed light on the intricate connections between psychosocial stressors and various mental health aspects in the context of the COVID-19 pandemic.
{"title":"Natural language processing analysis of the psychosocial stressors of mental health disorders during the pandemic","authors":"María P. Raveau, Julián I. Goñi, José F. Rodríguez, Isidora Paiva-Mack, Fernanda Barriga, María P. Hermosilla, Claudio Fuentes-Bravo, Susana Eyheramendy","doi":"10.1038/s44184-023-00039-6","DOIUrl":"10.1038/s44184-023-00039-6","url":null,"abstract":"Over the past few years, the COVID-19 pandemic has exerted various impacts on the world, notably concerning mental health. Nevertheless, the precise influence of psychosocial stressors on this mental health crisis remains largely unexplored. In this study, we employ natural language processing to examine chat text from a mental health helpline. The data was obtained from a chat helpline called Safe Hour from the “It Gets Better” project in Chile. This dataset encompass 10,986 conversations between trained professional volunteers from the foundation and platform users from 2018 to 2020. Our analysis shows a significant increase in conversations covering issues of self-image and interpersonal relations, as well as a decrease in performance themes. Also, we observe that conversations involving themes like self-image and emotional crisis played a role in explaining both suicidal behavior and depressive symptoms. However, anxious symptoms can only be explained by emotional crisis themes. These findings shed light on the intricate connections between psychosocial stressors and various mental health aspects in the context of the COVID-19 pandemic.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00039-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134976065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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