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Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

PurposeTo test the validity of the Ecological Video Identification of Physical Activity (EVIP) computer vision algorithms for automated video-based ecological assessment of physical activity in settings such as parks and schoolyards.MethodsTwenty-seven hours of video were collected from stationary overhead video cameras across 22 visits in nine sites capturing organized activities. Each person in the setting wore an accelerometer, and each second was classified as moderate-to-vigorous physical activity or sedentary/light activity. Data with 57,987 s were used to train and test computer vision algorithms for estimating the total number of people in the video and number of people active (in moderate-to-vigorous physical activity) each second. In the testing data set (38,658 s), video-based System for Observing Play and Recreation in Communities (SOPARC) observations were conducted every 5 min (130 observations). Concordance correlation coefficients (CCC) and mean absolute errors (MAE) assessed agreement between (1) EVIP and ground truth (people counts+accelerometry) and (2) SOPARC observation and ground truth. Site and scene-level correlates of error were investigated.ResultsAgreement between EVIP and ground truth was high for number of people in the scene (CCC = 0.88; MAE = 2.70) and moderate for number of people active (CCC = 0.55; MAE = 2.57). The EVIP error was uncorrelated with camera placement, presence of obstructions or shadows, and setting type. For both number in scene and number active, EVIP outperformed SOPARC observations in estimating ground truth values (CCC were larger by 0.11-0.12 and MAE smaller by 41%-48%).ConclusionsComputer vision algorithms are promising for automated assessment of setting-based physical activity. Such tools would require less manpower than human observation, produce more and potentially more accurate data, and allow for ongoing monitoring and feedback to inform interventions.

Objective ChatGPT and other large language models have the potential to transform the health information landscape online. However, lack of domain-specific expertise and known errors in large language models raise concerns about the widespread adoption of content generated by these tools for parents making healthcare decisions for their children. The aim of this study is to determine if health-related text generated by ChatGPT under the supervision of an expert is comparable to that generated by an expert regarding persuasiveness and credibility from the perspective of a parent. Methods In a cross-sectional study 116 parents aged 18–65 years (M  =   45.02, SD  =   10.92) were asked to complete a baseline assessment of their behavioral intentions regarding pediatric healthcare topics. Subsequently, participants were asked to rate text generated by either an expert or by ChatGPT under supervision of an expert. Results Results indicate that prompt engineered ChatGPT is capable of impacting behavioral intentions for medication, sleep, and diet decision-making. Additionally, there was little distinction between prompt engineered ChatGPT and content experts on perceived morality, trustworthiness, expertise, accuracy, and reliance. Notably, when differences were present, prompt engineered ChatGPT was rated as higher in trustworthiness and accuracy, and participants indicated they would be more likely to rely on the information presented by prompt engineered ChatGPT compared to the expert. Discussion Given that parents will trust and rely on information generated by ChatGPT, it is critically important that human domain-specific expertise be applied to healthcare information that will ultimately be presented to consumers (e.g. parents). [ABSTRACT FROM AUTHOR]

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings.

ObjectiveTo assess the role of amitriptyline in the effectiveness of an outpatient protocol for weaning medically complicated children from tube to oral feeding.Study designTwenty-one children seen in multidisciplinary outpatient feeding teams across 4 sites were recruited to a randomized placebo-controlled trial of a 6-month outpatient treatment protocol with behavioral, oral-motor, nutrition, and medication components.ResultsAll of the children who completed the 6-month program (73%) were weaned to receive only oral feeding, regardless of group assignment. The transition from tube to oral feeding resulted in decreases in body mass index percentile and pain, some improvements in quality of life, and no statistically significant changes in cost.ConclusionsAmitriptyline is not a key component of this otherwise effective outpatient, interdisciplinary protocol for weaning children from tube to oral feeding.Trial registrationClinicalTrials.gov: NCT01206478.

Post-traumatic stress disorder (PTSD) is a psychological disorder affecting individuals that have experienced life-changing traumatic events. The symptoms of PTSD experienced by these subjects-including acute anxiety, flashbacks, and hyper-arousal-disrupt their normal functioning. Although PTSD is still categorized as a psychological disorder, recent years have witnessed a multi-directional research effort attempting to understand the biomolecular origins of the disorder. This review begins by providing a brief overview of the known biological underpinnings of the disorder resulting from studies using structural and functional neuroimaging, endocrinology, and genetic and epigenetic assays. Next, we discuss the systems biology approach, which is often used to gain mechanistic insights from the wealth of available high-throughput experimental data. Finally, we provide an overview of the current computational tools used to decipher the heterogeneous types of molecular data collected in the study of PTSD.

Objective Consistent family rules and routines promote positive adaptation to stress and may be protective to child emotional and behavioral functioning. Few studies have quantified family engagement in these behaviors during pediatric cancer treatment or examined associations with child emotional and behavioral health. Methods In this cross-sectional observational study, 86 primary caregivers of youth ages 2–14 years (M  =   7.9) with an initial diagnosis of cancer within 16 weeks reported on their frequency of engagement in family rules and routines (e.g. sleep, schoolwork, and meal routines) before their child's cancer diagnosis and their current frequency of engagement in the same routines. Caregivers also reported demographics, psychosocial distress, and child emotional and behavioral health outcomes. Analyses examined demographic and psychosocial factors associated with engagement in rules and routines during cancer treatment, and associations with child emotional and behavioral health. Results Families reported a lower frequency of engagement in rules and routines during cancer treatment, compared to before treatment (mean difference 0.8 SDs [95% confidence interval 0.7–1.1 SDs]). Caregiver factors associated with lower engagement in rules and routines during treatment included being married, having lower educational attainment, and higher levels of psychosocial distress. Families who engaged in higher levels of rules and routines during treatment reported fewer child externalizing and behavioral challenges. There was limited evidence of association between family rules and routines and child internalizing outcomes. Conclusions Results found that engaging in family rules and routines during cancer treatment was associated with fewer child behavioral challenges during treatment. Future directions include longitudinal examinations of family rules, routines, and child emotional/behavioral outcomes to examine directional impact over time. [ABSTRACT FROM AUTHOR]

The current study examined whether caregiver hope was related to child outcomes (change in weight or health behaviors, treatment sessions attended) and caregiver outcomes (weight change, willingness to make behavioral changes) in a culturally diverse sample of 202 youth with overweight and obesity and their families participating in one of three weight management programs for children 2-18 years old. Within this treatment-seeking sample, dispositional caregiver hope did not predict willingness to make behavioral changes, number of treatment sessions attended, or treatment outcomes. These findings suggest specific efforts to foster caregiver hope within such interventions may not improve outcomes and investigation of other, potentially more modifiable caregiver and child factors may be more beneficial. [ABSTRACT FROM AUTHOR]