Background: The postpartum period represents a critical window for maternal health, yet many individuals lack sustained support and timely identification of physical and mental health risks. Digital health interventions offer a scalable approach to extend care beyond clinical settings. Yet, key elements, including real-world challenges, usability, and effectiveness of such platforms, are insufficiently characterized in this literature. Objective: This study aimed to conduct a formative randomized evaluation to assess the feasibility, engagement, and preliminary signals of impact of the Joyuus platform and to examine the potential for early identification of postpartum health risks. Methods: We conducted a 12-week randomized evaluation with postpartum participants recruited through community-based organizations. Participants were randomized to either the Joyuus intervention or standard postpartum care. Primary and secondary outcomes included the Barkin Index of Maternal Functioning, the Edinburgh Postnatal Depression Scale (EPDS), the State-Trait Anxiety Inventory, and the Connor-Davidson Resilience Scale. Analyses were conducted using an intention-to-treat approach with linear regression models adjusting for baseline values. Engagement metrics (ie, sessions, time on site, and feature use) were captured through in-app analytics. An exploratory predictive model was developed using baseline clinical, behavioral, and demographic variables to identify individuals at risk for postpartum depression. Results: Baseline characteristics were generally balanced across arms, and no statistically significant differences were observed in education, income, or marital status. No statistically significant differences were found between treatment and control groups in the 12-week changes for the primary or secondary outcomes. Mean EPDS scores were 9.7 in the intervention group and 9.3 in the control group. In the sample, 60 (45.5%) of the 132 participants met the criteria for elevated depression (EPDS score ≥11 or a positive response to question 10 on self-harm), indicating a high burden of symptoms within the study population. Engagement with the platform was highest during the first 4 weeks. Among intervention participants, 80% created an account. Participants reported high levels of perceived usefulness, ease of use, and relevance of content. Qualitative analysis of open-ended survey responses highlighted limited awareness of postpartum-specific resources and a preference for simple, accessible information. An exploratory predictive model demonstrated a recall of 0.89 and a precision of 0.73 in identifying individuals at risk for postpartum depression, suggesting the feasibility of early risk identification using integrated data inputs. Conclusions: Joyuus demonstrated feasibility and acceptability but did not produce statistically significant improvements in maternal functioning or maternal health outcomes over 12 weeks. Joyuus identified high rates of depressive symptoms and early engagement patterns, which suggest an opportunity for earlier identification of risk and intervention during the postpartum period. Exploratory modeling results indicate the potential for data-driven approaches to support earlier detection. Future work includes optimizing engagement strategies and expanding validation of predictive detection to improve postpartum surveillance and outcomes. Trial Registration: ClinicalTrials.gov NCT05876559; https://clinicaltrials.gov/study/NCT05876559?cond=postpartum%20joyuus&viewType=Card&rank=1
The results of a newly reported study have shown that people who suffered a serious heart attack had higher levels of micro- and nanoplastics (MNPs) in their blood, compared with MNP levels in patients diagnosed with chronic ischemic heart disease and those who have normal blood vessels supplying the heart. The study findings also revealed that people who smoke and people exposed to higher levels of air pollution had higher levels of micro- and nanoplastics in their blood.
Headed by teams at Sapienza University of Rome, at the University of Verona, and at the Research Centre on Environmental Pollution and Cardiovascular Diseases at the University of Campania “Luigi Vanvitelli,” the study included 61 patients at Sant’Andrea University Hospital or Azienda Ospedaliera Universitaria Integrata of Verona, diagnosed with either a heart attack, chronic ischemic heart disease, or normal coronary arteries.
The researchers say their study adds to growing evidence that environmental pollution may affect cardiovascular health. Research lead Emanuele Barbato, MD, PhD, at Sapienza University of Rome, said, “These findings do not prove that microplastics cause heart attacks, but they reveal a strong association between environmental exposures, microplastics in the blood and cardiovascular disease. In our study, smoking history was strongly linked to microplastics in the blood. Our findings suggest that smoking might make it easier for micro and nanoplastics to enter the blood stream via the lungs. Air pollution may act in a similar way.”
Cardiovascular diseases are increasingly related to lifelong environmental exposures, the authors noted. Among such exposures, MNPs are ubiquitous environmental pollutants, and evidence is increasing that they accumulate in human tissues following exposure and are emerging as a risk factor for health. Pasquale Paolisso, MD, PhD, at Sant’Andrea Hospital Sapienza University of Rome, said, “Micro and nanoplastics are tiny plastic particles that are found virtually everywhere in the environment, including the air we breathe, the water we drink, and many foods we consume. In recent years, scientists have begun to detect these particles in human tissues and organs, raising concerns about their potential health effects.”
Research findings have raised concerns about the potential role that MNPs may play in cardiovascular diseases. “Emerging evidence indicates that MNPs, once considered inert contaminants, are biologically active pollutants contributing to the pathophysiology of cardiovascular diseases, particularly by promoting the development and progression of atherosclerotic plaques and potentially triggering adverse cardiovascular events,” the team stated.
However, as Paolisso further noted, “… very little was known about whether these particles are present in the coronary circulation—the blood flowing through the arteries that supply the heart—or whether environmental exposures such as smoking and air pollution might influence their presence.” As the authors explained, “… current knowledge is predominantly based on in vitro experiments and preliminary ex vivo findings, highlighting the need for in vivo and clinical investigations.”
For their newly reported study the team measured MNPs in coronary and peripheral blood, in 61 patients at Sant’Andrea University Hospital or Azienda Ospedaliera Universitaria Integrata of Verona, who were undergoing coronary angiography for suspected coronary artery disease (CAD). Patients were stratified as those with ST-segment elevation myocardial infarction (STEMI), chronic coronary syndromes (CCS) and controls with normal coronary arteries.
As well as taking blood samples from the vessels supplying the heart and from elsewhere in the body, the team collected data on whether the patients were smokers and their exposure to pollution, both on the day of testing and over the preceding two years. Coronary micro and nanoplastics were analyzed at the Research Centre for Environmental Pollution and Cardiovascular Diseases, University of Campania ‘Luigi Vanvitelli,’ a center dedicated to understanding how environmental pollutants influence cardiovascular health.
The results showed that micro and nanoplastics were detected in 84% of patients diagnosed with heart attack, compared with 40% of patients with chronic ischemic heart disease and 32% of patients with normal coronary arteries. “The observation that IL-6 and TNF-α concentrations were highest in STEMI patients, particularly within the coronary circulation, and were more elevated in the presence of detectable MNPs supports an exploratory association between MNP burden and a localized pro-inflammatory milieu in patients with obstructive CAD,” the investigators suggested.
Heart attack patients also had a greater variety of plastic types in their blood. The most common type of plastic was polyethylene (PE), which is commonly used in packaging and consumer products. “Across all study cohorts, PE was the most frequently identified polymer, being present in 97% of the patients with detectable MNPs,” the investigators added.
Patients exposed to higher long-term levels of air pollution (PM2.5; particles measuring 2.5 μm or less in diameter) were more likely to have microplastics in their blood, and smokers were six times more likely to have microplastics in their blood. All patients who were smokers and were exposed to higher air pollution levels had plastics in their blood, compared with only 12.5% of patients who did not smoke and were not exposed to higher levels of air pollution. “MNPs, PM2.5, and smoking constitute potentially modifiable environmental risk factors for cardiovascular diseases, with significant implications for public health and cardiovascular disease prevention,” the scientists stated. “Future research should aim to quantify individual MNP exposure, assess combined pollutant burden, and validate interventions that target this expanded network of environmental cardiovascular hazards.”
Barbato added, “The results highlight the need to consider microplastic pollution as part of the broader environmental determinants of health. Policies that reduce air pollution, tobacco exposure, and environmental plastic contamination could have benefits that extend beyond environmental protection and potentially improve cardiovascular health.”
In an accompanying editorial Andreas Daiber, PhD, at University Medical Centre of the Johannes Gutenberg University, Mainz, and colleagues pointed to the observation by Paolisso et al. of an association between NMP levels and exposure to air pollution and tobacco smoking. “While the underlying mechanisms remain unclear, this finding underscores a key principle: environmental exposures rarely occur in isolation,” Daiber and colleagues stated. “Individuals are exposed to multiple environmental stressors simultaneously, including air pollution, noise, chemical contaminants, plastics, and climate-related stressors, especially in the urban setting. These exposures may interact through shared biological pathways, leading to additive or synergistic effects on cardiovascular risk.”
And while substantial uncertainties remain, “the convergence of epidemiological, clinical, and mechanistic evidence suggests that plastic pollution may represent a previously underestimated cardiovascular risk factor,” Daiber et al. continued. “Addressing this challenge will require coordinated efforts across disciplines and policy domains. In the era of the Anthropocene, protecting cardiovascular health will increasingly depend on reducing not only traditional risk factors but also the growing burden of environmental pollutants (the detrimental part of the exposome), among which plastics may soon play a central role.”
For decades, psychiatry has used trial-and-error symptom-based diagnoses and treatments. Standardized diagnostic frameworks brought much-needed consistency to the field, but they also grouped diverse patients with different biology. Consequently, many people receive unsuitable treatments.
On this episode of Behind the Breakthroughs, Alto Neuroscience founder and CEO Amit Etkin, MD, PhD, discusses how precision medicine will change mental health care. Etkin explains how objective biological measures like cognitive testing, EEG brain activity, sleep and circadian rhythm monitoring, and advanced computational analysis can help identify patients who will benefit from specific therapies rather than just symptoms. Comparing psychiatry to precision oncology, he explains why it is at a turning point. Instead of finding a perfect biomarker, the field is developing practical, scalable tools to link brain function to targeted drug development.
Etkin shows from Alto’s clinical pipeline how matching therapies to biologically defined patient populations can improve outcomes and reduce psychiatric treatment uncertainty. We also examine the potential and limitations of genetics, multi-omics, wearables, and AI in precision psychiatry. Etkin explains why brain measurements may be more clinically useful than peripheral biomarkers and how AI can help find patterns in complex biological data. Finally, we discuss how precision psychiatry will become routine clinical practice, from regulatory acceptance and standardized data collection to the first biomarker-guided therapies. If successful, these advances could transform psychiatric disorder diagnosis, treatment, and understanding.
This interview has been edited for length and clarity.
IPM: What has been the key limiting factor to advancing precision psychiatry?
Etkin: There was a period of time in the 1940s, 1950s, and 1960s when psychiatry was beginning to develop. Our definition of diseases was bespoke to how you practiced them. They really made very little sense.
Amit Etkin, MD, PhD, co-founder and CEO of Alto Neuroscience [Alto Neuroscience]
The DSM did a great job of bringing everybody under the same diagnostic umbrella. We can talk about a common set of symptoms and everybody’s talking about them. Yes, we have wonky definitions of diseases, but at least we’re starting to use the same definition.
We are finding large, heterogeneous groups of people that fit into a category. If you examine the words used to describe some psychiatric labels from the past, they sound antiquated because they are linked to outdated concepts and are inconsistently defined. That was good. The problem is that we never transitioned beyond that.
I try to focus on a much simpler approach to what we’re doing. Instead of thinking about biomarkers, machine learning, and so forth, it’s about knowing what we are doing. If what you’re doing is developing a drug for a population with depression, it’s a massive category where some people just symptomatically might sleep more and others sleep less, and some eat more and others eat less. There seems like it’s a bit of a mess. What would you want to know? At a very basic level, that would simply allow you to know what you’re doing better. What would you like to measure?
There are any number of answers. We know that some patients with depression have cognitive problems and others don’t and that people with cognitive problems have worse outcomes regarding standards of care and treatment. We’ve known that for a long time. But why aren’t we doing that measurement systematically in every single drug trial or in clinical care? Because if we did that one little thing systematically, we would see that some treatments or people saw it one way and others saw it another way just by collecting that data more systematically. That’s what we had been doing in the lab: figuring out what kind of data to collect systematically.
That’s what led us to the form Alto, understanding that there are certain measures, like cognition and brain activity. We do this non-invasively with electroencephalogram (EEG) brainwave recordings and wearables to look at circadian rhythms that are physiologically and biologically meaningful, easily scalable, low cost to meet our health system’s needs, and, if done consistently, insightful for separating populations and identifying mechanisms to develop for them and for translating back to an animal model, which is not possible if you only look at depressive symptoms.
Do we know what we’re doing now? No. We’re really just beginning on this journey as a field. But I do think people now recognize that this is the direction of travel. You can see that more and more companies and academic research centers are emerging under this theme.
IPM: What are your thoughts on the use of genomics, multi-omics, or blood-based biomarkers of the central nervous system?
Etkin: The way I interpret that literature is that there’s smoke but not yet fire. The classical molecular marker is genetics. We get that on every patient, but we do not use stratification here, as genetics cannot achieve meaningful stratification in our populations. It’s predominantly common variance with each of them, or in combination, having a very rare large effect size variance, which is really not going to be clinically meaningful from a drug perspective because you’re treating one out of every 10,000 people or whatever the prevalence is.
I don’t know the best polygenic risk score for schizophrenia, determined from 100,000 people, which is probably the high watermark for psychiatric genetics and may explain 1–2% of the variance. You needed to explain at least 10% to stratify the population even a little bit. I don’t think genetics will ever get us there for that purpose.
Where there’s smoke but not yet fire are immune measures. There have been many implications for different immune measures in psychiatric disorders, but every time it gets tested to see whether people with high inflammation respond to something that targets our process directly or indirectly, those studies never really work out.
Then there’s the even larger world of multi-omics, where you have a multiple testing issue and, fundamentally, the problem that what you’re sampling is very peripheral to the organ that matters. Where is the serotonin in the blood coming from? It’s mainly coming from platelets—it’s not coming from the brain. The bulk of serotonin in the body is in the gut. So you can measure… a ton of different proteins, different configurations, and modifications to those proteins probably have relatively little purchase on what is going on in the brain.
The simplest example is a protein called brain-derived neurotrophic factor (BDNF), which is a really important neuroplasticity protein in the brain. It’s also found in the blood, and people tried over and over and over again. You see some positive studies, but mainly studies that are negative and some that are just not published that come to the conclusion that there’s very little bearing of what you’re measuring peripherally to what’s going on centrally.
Measuring the brain directly with EEG or the output of specific brain circuits through behavioral tests is much more amenable and has better performance statistics and interpretability for gaining insights.
IPM: Has a specific layer or test modality enabled precision psychiatry programs for Alto Neuroscience, or are they ultimately based on aggregate measures?
Etkin: Less so in aggregate as measured together but each alone. We try not to combine everything into one model because it becomes very complicated, and we have already been told by the FDA in no uncertain terms that a multimodal biomarker is not something we will readily consider. To get a multimodal biomarker approved for some sort of use, you have to validate each and every component alone and their combination, which sounds like a headache. But I’m not sure you necessarily need to either.
What I would consider to be a win is getting a drug for the whole population with a marker that enriches finding ways to show additional value in a drug program through a biomarker perspective and, over time, an iteration. The field then transforms into one where oncology already exists, which means they expect you to know what you’re doing. You have to understand the population. You have to understand how your drug impacts the biology that defines the population. We’re not there yet.
But they weren’t there yet either, in the same kind of single stroke that we now envision. It was like the first precision therapeutics, like Herceptin, were approved well over a decade before the immuno-oncology (IO) revolution. That really brought precision oncology into maturity, as we understand it now. That history suggests we probably need our IO moment as an inflection point, but we are not yet ready for it.
We need that Herceptin moment first: start transitioning how people think and collect data and create a bit more of a common language across programs so that different drug makers and different academic labs aren’t collecting their own unique data sets that aren’t then harmonized across them. You can’t speak about a thing as an invariant measure of a process that doesn’t matter who is measuring; they get the same outcome.
The field has been somewhat resistant, probably for cultural reasons related to how people have historically operated, to a lot of data sharing and harmonizing of what we’re collecting, how we’re collecting it, and how we’re analyzing it. We’re just starting to really move in that direction. Those are all limits that gate the early-stage biomarker collection efforts.
IPM: Is there a future where someone walks in with a psychiatric condition and undergoes a battery of measurements that spits out a drug that has a high probability of being effective?
Etkin: I think that bar is a lot lower than that. It doesn’t have to be very effective—it just has to be more effective than chance. because that’s where we are. If I told you that instead of a 30% chance of remission with a drug, I could increase it to 40% or 45%, would that be helpful? That’s meaningful. You convert that to a number needed to treat it. For a clinician, this represents a significant effect, although it does not achieve perfect precision. All you need is something better than nothing, which is what we have. Of course, once you have something that’s better than nothing, now you have a new benchmark, and things will continue to improve, which is great. But the field’s got to start somewhere.
I don’t think it is that far away. I think in our efforts and the efforts of others in the field who have followed suit and taken a precision approach, something will work. When that changes, all of a sudden you can’t envision going back; it’s only going forward. That’ll be super exciting. It’s not like a “by the time I retire” kind of thing. It’s within the next five or a maximum of ten years that we will be at that inflection point.
IPM: Does precision psychiatry apply to the rest of neurology?
Etkin: If you anchored on the way I framed brain circuit function earlier. And what’s measurable is that there is no line between psychiatry and neurology. You have neurologists who are called “functional neurologists” or something in that vein, where they think about what I would call the “psychiatric aspects of neurology.”
A big part of Parkinson’s is cognitive impairment in a substantial portion of people, leading to dementia. Nothing to do with the movement disorder, but everything to do with the biology affecting different circuits. The right mood, in fact, is one of the earliest areas of perturbation in Parkinson’s that will then predict the development of the motor symptoms. Some people have perfectly well-controlled motor problems but have cognitive problems and mood problems that are even more prominent and lead to more, especially on the cognitive side, of their ultimate disabilities. Cognitive impairments are even a contraindication for deep brain stimulation.
Because of these interactions, all of these boundaries are artificial. It’s just a core engineering question of, can I know what I am measuring and what I am manipulating? There’s no reason we need to draw that line in an artificial way. It’s just about whether I can leverage the tools and the drugs in a useful way together.
IntroductionA traumatic brain injury (TBI) of mild or more severe degree affects approximately ½ of the global population at some stage of their life. Mild TBI occurs in 70–90%, with 30 and 50% having symptoms persisting for more than 6 months. Mild TBI presentations include cognitive, elementary neurological, neuropsychiatric, endocrine, autonomic, cardiac, and general medical entities, with many behavioral neurological syndromes flying under the radar.AimsA retrospective examination of the cognitive and behavioral impairments in people with traumatic brain injury to evaluate the range of differing syndrome presentations, including hypofunction, hyperfunction and superla+ve brain function syndromes.MethodologyThe Brainbeat Cognitive Registry was a prospectively designed observational registry that collected clinical, cognitive, behavioral, neurological, neuropsychiatric, laboratory, and radiographic data from people with cognitive and behavioral disorders.ResultsIn the registry (n = 73), of predominantly men (88%), with averages for age 55.1 years, BMI 28.9, education 15.1 years, and MOCA score 21.7. Migraine, olfactory impairment, depression, anxiety, and PTSD were all relatively commonly associated conditions. Relatively common disorders with more complex syndromes, including Diogenes syndrome, IEED, ADHD, field-dependent behavior, and hyperorality, the later on presenting as a human Klüver Bucy syndrome. Less common disorders included other higher cortical function disorders (17.1%), neuropsychiatric (10.5%), cortico-ponto- cerebellar pathway syndromes (10.5%), and visual radiation disorders (6.5%). The least common were chronotaraxis, schizophrenia, bipolar disorder, content-specific delusions, tremor, ataxia, astereopsis, and prosopagnosia. The majority of TBI patients presented with an overarching frontotemporal disorder (FTD) diagnosis (n = 68, 89.4%), with abnormal FRSBE scores for one or more entities of abulia, disinhibition, and executive dysfunction. Frontal Behavioral Inventory scores were abnormal in 86%. The most common neurological sub-syndrome was Geschwind-Gastaut syndrome (n = 49, 62.8%). A category of patients demonstrating superlative abilities (n = 9), including visual art, musical, literary, architectural brilliance, and precognition, all attributed to right hemisphere hyperfunction, was also identified.ConclusionPost-TBI frontotemporal disorders are common. Deconstructing the overarching FTD diagnosis into multiple subsyndromes is clinically useful, revealing hypofunction syndromes, hyperfunction, and superlative function syndromes. The range of neurological stealth syndromes as part of the post-TBI range of maladies may facilitate a more targeted, precision management approach.
IntroductionWorry and rumination are two common forms of repetitive negative thinking encountered as core symptoms of anxiety and depression. They are difficult to treat and increase the risk of relapse for remitted depressive and anxiety disorders, especially in late-life. We propose that dysfunctional belief-updating is a fundamental cognitive mechanism underlying repetitive negative thinking and use a Bayesian model to test whether distinct components of belief updating are differentially associated with worry and rumination.MethodsWe recruited 83 older participants (age≥50) dimensionally for worry and rumination to perform a belief-updating task and undergo neuropsychological testing. We extracted three parameters from a Bayesian model of belief-updating: performance compared to the Bayesian optimal model, emphasis on initial information (prior weight), and relative weighting of new evidence compared to current beliefs (update strength). These parameters were tested for associations with worry and rumination severity, and four neuropsychological domains were tested as moderators (attention, visuospatial, working memory maintenance, and executive function).ResultsWorry severity was uniquely associated with lower prior weight, while rumination was associated with low update strength. Neither worry nor rumination were associated with performance. None of the neuropsychological domains moderated these relationships.ConclusionWorry and rumination are associated with unique alterations in belief-updating but not overall performance. Low prior weight in worry may inflate perceived uncertainty, while low update strength in rumination—specifically reflective pondering—is consistent with slower incorporation of new information. Both biases may contribute to the distinct phenomenological profiles of worry and rumination and inform specific therapeutic targets for these symptoms.
Mental health disorders represent a growing burden across the Middle East and North Africa (MENA) region, where depression and anxiety are highly prevalent amid conflict, displacement, and socioeconomic strain, affecting up to 40 percent of adults, yet treatment gaps remain at 80-95% due to provider shortages, financial strain, and cultural barriers. In this context, artificial intelligence (AI), in the form of large language models (LLMs) and specialized psychotherapy chatbots, may offer a scalable adjunct to help address these gaps through anonymous screening, predictive risk modeling, psychoeducation, and brief interventions. This narrative review examines current evidence of AI-driven conversational tools in mental health with a specific focus on their application, acceptance, and limitations within the MENA region. To do so, A structured search of MEDLINE and Embase (2000–2026) identified studies on conversational AI in mental health, prioritizing evidence from the MENA region and supplemented by relevant global literature. Overall, findings suggest that while these tools offer high accessibility and user engagement, particularly for low-intensity support, their effectiveness is limited by linguistic and cultural mismatches, including Arabic diglossia and poor alignment with locally grounded expressions of distress. At the same time, user acceptance reflects a paradox in which stigma and privacy concerns drive reliance on anonymous AI tools while simultaneously limiting trust in their clinical reliability, reinforcing a preference for hybrid models with human oversight. Taken together, these findings indicate that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.
ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.
Background: Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health using passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, knowledge regarding whether short-term changes in symptoms can be captured using passive smartphone data in general population samples remains limited, as does the understanding of how such findings should be interpreted vis-à-vis behavioral patterns and demographic variability. Objective: This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes. Methods: We collected 2 weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale, respectively. Behavioral features—including physical activity, app use, and screen usage metrics—were extracted and compressed using an autoencoder and principal component analysis. The resulting features—along with age, sex, and baseline Hamilton scores—were used to train random forest classifiers predicting symptom score changes (increase, decrease, or unchanged). Additionally, we examined whether model performance differed across demographic subgroups and whether models excluding baseline scores retained predictive performance, as baseline severity was expected to be a strong predictor. To add explanatory value beyond prediction, behavioral subtypes associated with symptom changes were identified by applying unsupervised clustering. Results: The model exhibited moderate performance in predicting changes in the Hamilton Depression Rating Scale (mean accuracy=0.70, mean area under the receiver operating characteristic curve=0.74) and Hamilton Anxiety Rating Scale (mean accuracy=0.65, mean area under the receiver operating characteristic curve=0.69) scores. Performance varied according to demographics, with reduced accuracy among younger adults and females, although these differences were not significant in permutation tests. Excluding baseline Hamilton scores diminished performance substantially, suggesting that baseline symptom severity accounted for a substantial proportion of the predictive performance. Clustering revealed 4 distinct behavioral subtypes according to smartphone usage patterns. A cluster characterized by structured, daytime-focused smartphone use and lower temporal entropy demonstrated greater improvement in depressive symptoms, whereas clusters with lower and irregular usage patterns exhibited minimal improvement or worsening. Conclusions: Passive smartphone-derived behavioral data demonstrated moderate ability to model short-term symptom changes in this predominantly nonclinical sample. However, a substantial proportion of the predictive performance was attributable to baseline symptom severity, underscoring that passive smartphone data may provide modest supplementary information rather than robust stand-alone predictive value. Nevertheless, clustering analyses indicated that passive data may still assist in identifying behaviorally distinct subtypes associated with different depressive symptom trajectories. These findings reflect a practical contribution to digital phenotyping research by elucidating both the potential and constraints of passive smartphone data for short-term symptom monitoring in small general population samples.
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<![CDATA[Where can vagus nerve stimulation fit into treating depression?]]>
