Npj mental health research最新文献

{"title":"Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality","authors":"Romain Bey, Ariel Cohen, Vincent Trebossen, Basile Dura, Pierre-Alexis Geoffroy, Charline Jean, Benjamin Landman, Thomas Petit-Jean, Gilles Chatellier, Kankoe Sallah, Xavier Tannier, Aurelie Bourmaud, Richard Delorme","doi":"10.1038/s44184-023-00046-7","DOIUrl":"10.1038/s44184-023-00046-7","url":null,"abstract":"There is an urgent need to monitor the mental health of large populations, especially during crises such as the COVID-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the COVID-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the COVID-19 outbreak with an estimated trend variation reaching 3.7 (95%CI 2.1–5.3), mainly driven by an increase among girls aged 8–17 (trend variation 1.8, 95%CI 1.2–2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95%CI 1.16–1.48; 1.3, 95%CI 1.10–1.64 and 1.7, 95%CI 1.48–1.98), fewer patients died (p = 0.007) and stays were shorter (p < 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the COVID-19 pandemic.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00046-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732420","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}

{"title":"Association between continuity of care and attendance of post-discharge follow-up after psychiatric emergency presentation","authors":"Ben Hoi-Ching Wong, Petrina Chu, Paul Calaminus, Cathy Lavelle, Rafik Refaat, Dennis Ougrin","doi":"10.1038/s44184-023-00052-9","DOIUrl":"10.1038/s44184-023-00052-9","url":null,"abstract":"The number of accident and emergency (A&E) hospital attendances by young people aged 18 or under with a recorded diagnosis of a psychiatric condition more than tripled between 2010 and 2022. After discharge from the hospital, attendance at follow-up appointments in the community is critical to ensure the safety of young people and optimise the use of clinical resources. A retrospective cohort study was conducted to evaluate the association between follow-up attendance and the continuity of clinicians and clinical teams, using electronic clinical record data from East London NHS Foundation Trust (ELFT), between April 2019 and March 2022. Multi-level mixed effects logistic regression was performed to model the follow-up attendance odds based on whether the same or different clinician and clinical team offered the initial A&E and the community follow-up appointment or whether a crisis team was involved. 3134 A&E presentations by 2368 young people were identified within the study period. Following these presentations, 2091 follow-up appointments in the community were offered. The attendance rate increased by more than three times if the follow-up appointment was offered by the same clinician who saw the young person in A&E (odds ratio (OR) = 3.66; 95% CI 1.65–8.13). Whether the same clinical team provided the community follow-up appointment, or whether a crisis team was involved before discharge made no difference to the likelihood of follow-up attendance. The findings support the importance of the continuity of clinicians in the care of young people in crisis.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00052-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695553","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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}