Npj mental health research最新文献

{"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}

{"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}

{"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}

{"title":"Systematic review of machine learning in PTSD studies for automated diagnosis evaluation","authors":"Yuqi Wu, Kaining Mao, Liz Dennett, Yanbo Zhang, Jie Chen","doi":"10.1038/s44184-023-00035-w","DOIUrl":"10.1038/s44184-023-00035-w","url":null,"abstract":"Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00035-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537104","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":"An automatic speech analytics program for digital assessment of stress burden and psychosocial health","authors":"Amanda M. Y. Chu, Benson S. Y. Lam, Jenny T. Y. Tsang, Agnes Tiwari, Helina Yuk, Jacky N. L. Chan, Mike K. P. So","doi":"10.1038/s44184-023-00036-9","DOIUrl":"10.1038/s44184-023-00036-9","url":null,"abstract":"The stress burden generated from family caregiving makes caregivers particularly prone to developing psychosocial health issues; however, with early diagnosis and intervention, disease progression and long-term disability can be prevented. We developed an automatic speech analytics program (ASAP) for the detection of psychosocial health issues based on clients’ speech. One hundred Cantonese-speaking family caregivers were recruited with the results suggesting that the ASAP can identify family caregivers with low or high stress burden levels with an accuracy rate of 72%. The findings indicate that digital health technology can be used to assist in the psychosocial health assessment. While the conventional method requires rigorous assessments by specialists with multiple rounds of questioning, the ASAP can provide a cost-effective and immediate initial assessment to identify high levels of stress among family caregivers so they can be referred to social workers and healthcare professionals for further assessments and treatments.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00036-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741762","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}