Npj mental health research最新文献

AI models may soon be poised to recommend mental health treatments or referrals in primary care, yet little is known regarding their impact on physician decision-making. In this web-based study, primary care physicians (n = 420) were presented with a clinical scenario describing a patient with psychiatric symptoms, an AI tool for referring or prescribing, and the recommendation of the AI. A sequentially randomized vignette method was used to test the impact of initial assessments and AI output on physician decision-making patterns. Physicians were significantly more likely to change their decisions when the AI recommendation was misaligned with their initial assessment, especially when AI recommended treatment. There was no difference between the change-in-decision rate of physicians who received an AI recommendation to not treat, indicating that the direction of AI recommendations may influence physician decision-making, and raising important considerations for how physician decisions may be anticipated in the context of AI.

Concerns about the accuracy of self-reported screen time persist due to discrepancies with objective measures. This study compared passive smartphone tracking via the "Effortless Assessment of Risk States'' (EARS) app with self-reported screen time from 495 adolescents. Based on self-reports, 94.26% of social media use occurred on smartphones. EARS-recorded social media use was higher (1.64 ± 1.93 h) than past-year self-report (1.44 ± 1.97 h; p = 0.037) but similar to post-sensing self-report (1.63 ± 1.93 h; p = 0.835). Higher picture vocabulary scores were associated with lower odds of under-reporting social media use (OR = 0.96, 95% CI: 0.93-0.99). Both self-reported (β = 0.06, 95% CI: 0.01-0.11) and EARS (β = 0.07, 95% CI: 0.03-0.12) measures correlated with externalizing symptoms. They were also correlated with social media addiction (self-reported:β = 0.15, 95% CI: 0.10-0.20; EARS:β = 0.06, 95% CI: 0.01-0.11). However, past-year self-report uniquely correlated with internalizing symptoms (β = 0.05, 95% CI: 0.01-0.09) and video game addiction (β = 0.05, 95% CI: 0.01-0.10). These findings highlight the value of integrating self-report and objective measures in screen media use research.

Little is known about early-life experiences that may lead to higher optimism levels in adulthood. Using data from 202,898 adults in 22 countries, we evaluated childhood candidate antecedents of optimism. We examined the associations between retrospectively reported childhood experiences and adult optimism levels in each country separately, and cross-nationally by pooling results across countries. Our pooled results suggest that higher adult optimism levels were associated with childhood experiences of having positive relationships with both parents, higher subjective financial status, better childhood self-rated health, frequent religious service attendance, an earlier year of birth, and being female. Conversely, lower adult optimism was associated with childhood experiences of parental divorce, abuse, financial hardship, and feeling like an outsider in the family. However, country-specific analyses showed substantial between-country variations in these associations, suggesting diverse societal influences. This study provides valuable insights into the association between early-life experiences and adult optimism levels across national contexts.

How might we cultivate a life imbued with purpose and meaning? Though common experience indicates purpose and meaning are inherently important, empirical evidence confirms they are also linked to improved health/well-being. However, childhood antecedents of purpose and meaning in adulthood are understudied. We analyzed nationally representative data from 22 countries in the Global Flourishing Study (N = 202,898) and evaluated whether 11 aspects of a child's upbringing correlate with purpose and meaning in adulthood, and if these associations vary by country. Some childhood factors were associated with increased purpose and meaning, including: good health, good relationship with parents, economic stability, regular religious service attendance, being female, and older birth cohorts. Childhood factors associated with decreased purpose and meaning included: abuse, feeling alienated, poor health, economic hardship, and loss of a parent. These findings may inform the development of programs designed to foster purpose and meaning that are globally adaptable and locally nuanced.

Posttraumatic stress disorder (PTSD) is associated with poor hippocampal function and disrupted pattern recognition. Cannabis use is highly prevalent in individuals with PTSD, yet the impact on these cognitive functions is poorly understood. Participants (n = 111) with a range of PTSD symptoms with and without regular cannabis use completed the mnemonic similarity task. We hypothesized that regular use would be associated with alterations in pattern separation ability in individuals with PTSD symptoms. High PTSD symptoms were associated with reduced pattern separation performance in minimal users. Regular users with high PTSD symptoms showed greater pattern separation, but reduced pattern separation with low PTSD symptoms. These results suggest that regular cannabis use may disrupt pattern separation and similar hippocampal-dependent processes, while it may improve pattern separation in individuals with high PTSD symptoms. These cross-sectional results require longitudinal follow-up studies to evaluate the causal effects of regular cannabis use on cognitive function in PTSD.

Seasonal and weather changes can significantly impact depression severity, yet findings remain inconsistent across populations. This study explored depression variations across the seasons and the interplays between weather changes, physical activity, and depression severity among 428 participants in a real-world longitudinal mobile health study. Clustering analysis identified four participant subgroups with distinct patterns of depression severity variations in 1 year. While one subgroup showed stable depression levels throughout the year, others peaked at various seasons. The subgroup with stable depression had older participants with lower baseline depression severity. Mediation analysis revealed temperature and day length significantly influenced depression severity, which in turn impacted physical activity levels indirectly. Notably, these indirect influences manifested differently or even oppositely across participants with varying responses to weather. These findings support the hypothesis of heterogeneity in individuals' seasonal depression variations and responses to weather, underscoring the necessity for personalized approaches in depression management and treatment.

The burgeoning field of the microbiome-gut-brain axis has inspired research into how the gut microbiome can affect human emotion. Probiotics offer ways to investigate microbial-based interventions but results have been mixed, with more evidence of beneficial effects in clinically depressed patients. Using a randomised, double-blind, placebo-controlled design in 88 healthy volunteers, we conduct a comprehensive study into effects of a multispecies probiotic on emotion regulation and mood through questionnaires, emotional processing tests and daily reports. We find clear evidence that probiotics reduce negative mood, starting after two weeks, based on daily monitoring, but few other changes. Our findings reconcile inconsistencies of previous studies, revealing that commonly used pre- versus post-intervention assessments cannot reliably detect probiotic-induced changes in healthy subjects' emotional state. We conclude that probiotics can benefit mental health in the general population and identify traits of individuals who derive greatest benefit, allowing future targeting of at-risk individuals.

Despite the established association between social and behavioral determinants of health (SBDH) and suicide risk, SBDHs from unstructured electronic health record notes for suicide prediction remain underutilized. This study investigates the impact of SBDH identified from both structured and unstructured data utilizing a natural language processing (NLP) system on suicide prediction at 7, 30, 90, and 180 days post-discharge. Using data from 2,987,006 US Veterans between 1 October 2009, and 30 September 2015, we designed a case-control study demonstrating that structured and NLP-extracted SBDH significantly enhance distinct prediction models' performance. For example, the random forest model improved its 180-day post-discharge prediction with an area under the receiver operating characteristic curve increase from 83.57% to 84.25% (95% CI = 0.63%-0.98%, p val < 0.001) and area under the precision-recall curve increase from 57.38% to 59.87% (95% CI = 3.86%-4.82%, p val < 0.001) after integrating NLP-extracted SBDH. These findings underscore the potential of NLP-extracted SBDH in advancing suicide prediction.

This study applies network theory to registry data to identify prospective differences between individuals who develop long-term pain later in life and those who do not. The research is based on assessments of biological, psychological, and social variables in late adolescence during military conscription in Sweden. The analysis reveals significant differences in the network profiles of adolescent men who later developed long-term pain. These differences are reflected in several network-based outputs, including global, nodal, and edge levels, revealing a consistent picture of the pain-associated network profile. This profile demonstrates how those vulnerable to long-term pain have a specific configuration of variables that skew away from the rest of the population, mainly relating to psychosocial aspects.