Nevin Manimala

Int J Psychiatry Med. 2025 May 22:912174251345007. doi: 10.1177/00912174251345007. Online ahead of print.

ABSTRACT

ObjectiveThis study investigated the effects of Electroconvulsive Therapy (ECT) on intracranial pressure (ICP) by measuring the optic nerve sheath diameter (ONSD) using ultrasonography. While ECT is a common and effective treatment for various psychiatric disorders, its impact on cerebral hemodynamics, particularly ICP, remains unclear. Previous research suggests that ECT may increase cerebral blood flow and oxygen consumption, potentially elevating ICP. However, there is limited direct evidence linking ECT to measurable ICP changes.MethodsIn this study, ONSD was measured at 4 time points during ECT in 24 patients, including pre-ECT, post-induction, post-ictal, and in the post-anesthesia care unit (PACU).ResultsThe results showed no statistically significant changes in ONSD, indicating that ECT does not significantly alter ICP based on this non-invasive measurement.ConclusionThese findings suggest that, at least in the context of this study, ECT does not lead to clinically relevant changes in ICP.

PMID:40403192 | DOI:10.1177/00912174251345007

Anesth Analg. 2025 May 22. doi: 10.1213/ANE.0000000000007548. Online ahead of print.

ABSTRACT

BACKGROUND: Postoperative delirium (POD), emergence delirium (ED), and postoperative cognitive dysfunction (POCD) are disorders of the neuropsychiatric spectrum affecting the elderly during the postoperative period, potentially sharing a common pathophysiological pathway. Disrupted sleep postoperatively correlates with both POD and POCD, revealing overlapping risk factors. This study investigates the potential of dexmedetomidine anesthesia to reduce the incidence of POD (primary outcome), ED, POCD, impairment of sleep quality, and emergent chronic pain (secondary outcomes) in older adults undergoing major orthopedic surgery under regional anesthesia.

METHODS: In this double-blind randomized control trial, patients scheduled for major lower limb orthopedic surgery under regional anesthesia were randomized to receive either dexmedetomidine or propofol for sedation at a 1:1 ratio. POD, ED, and POCD were assessed with the Confusion Assessment Method tool, the Riker Sedation-Agitation scale, and the European Battery of psychometric tests, respectively. Sleep quality was assessed using the Pittsburg Sleep Quality Index and chronic pain with the painDETECT tool. Assessments of all outcome variables were performed before surgery, and at 48 hours and 3 months postoperatively.

RESULTS: A total of 80 patients (dexmedetomidine group n = 41) were enrolled in the study and completed the follow-up. POD, ED, and early POCD incidence were significantly lower in dexmedetomidine compared to propofol group (4.8% vs 38.4%, P = .001; 2.4% vs 38.4%, P < .001; 2.4% vs 56.4%, P < .001, respectively). Patients in the dexmedetomidine group reported improved sleep quality in the immediate postoperative period (lower PSQI score) and lower painDETECT scores at 3 months (4.4 ± 0.7 vs 13.4 ± 0.8, P < .001; 2.4 ± 0.9 vs 5.3 ± 0.9, P = .023, respectively). Intraoperative bradycardia and hemodynamic instability episodes were more common in the dexmedetomidine group while a single patient presented airway obstruction (2.4% vs 30.8%, P = .002) in the dexmedetomidine group.

CONCLUSIONS: Sedation with dexmedetomidine resulted in a statistically and clinically important reduction in the incidence of POD, ED, and early POCD, while it improved self-reported postoperative sleep quality and reduced chronic pain scores in patients undergoing major elective lower limb surgery under regional anesthesia.

PMID:40403182 | DOI:10.1213/ANE.0000000000007548

Acad Med. 2025 May 22. doi: 10.1097/ACM.0000000000006093. Online ahead of print.

ABSTRACT

PURPOSE: To determine the relationship between adverse childhood experiences (ACEs), social disadvantage, psychological distress, and resilience in graduate health professions students.

METHOD: This study includes cross-sectional analyses from a longitudinal survey of medical, veterinary, and advanced practice provider students at matriculation to the University of California Davis in July 2019. The survey contained an expanded Adverse Childhood Experiences Questionnaire (ACEs-14), a measure of psychological distress (the Medical Student Well-Being Index [MSWBI]), and the Brief Resilience Scale. Responses were linked to demographics, including markers of social disadvantage (female gender, underrepresented in medicine [URM] status, and first-generation college graduate [first-gen] status). The relationships between ACEs, social disadvantage, psychological distress, and resilience were tested using linear or logistic regression.

RESULTS: Complete survey responses were provided from 240 of 357 students (67% completion rate). About two-thirds of students (67%, 161/240) reported ≥1 ACE, while a quarter (25%, 60/240) reported ≥4 ACEs. URM and first-gen students had higher odds of reporting ≥4 ACEs (odds ratio [OR] = 1.56; P = .049 and OR = 2.63; P < .001, respectively) than their nondisadvantaged peers based on binary logistic regression analysis. Higher ACEs-14 scores were associated with higher psychological distress scores (P < .001). The majority of students reported normal or high resilience (normal: 76%, 183/240; high: 10%, 25/240) regardless of ACEs-14 scores. There was not a statistically significant relationship between ACEs-14 scores and resilience scores (P = 0.936).

CONCLUSIONS: Health professions students from some socially disadvantaged backgrounds at this institution reported statistically significantly higher ACEs-14 scores than their nondisadvantaged peers. Childhood adversity was associated with increased psychological distress but not with low resilience. Implications for equity- and trauma-informed health professions education and interventions are discussed.

PMID:40403161 | DOI:10.1097/ACM.0000000000006093

JBJS Rev. 2025 May 22;13(5). doi: 10.2106/JBJS.RVW.25.00052. eCollection 2025 May 1.

ABSTRACT

BACKGROUND: Childhood obesity is a growing global health crisis with significant health and orthopedic complications such as slipped capital femoral epiphysis (SCFE), a hip disorder characterized by the displacement of the metaphysis relative to the epiphysis. SCFE always requires surgical intervention to prevent severe outcomes such as avascular necrosis, gait abnormalities, and lifelong disability and deformity. Obesity is a well-established risk factor for SCFE; however, emerging evidence suggests that elevated leptin levels may independently contribute to the development of SCFE, regardless of obesity status. This systematic review synthesizes geographic, socioeconomic, age, and sex-related trends in SCFE incidence among children with obesity.

METHODS: Searches of Embase, OVID Medline, and Emcare databases were performed from inception through October 1, 2024. Observational studies reporting the incidence of SCFE in children and adolescents with obesity (aged ≤18 years) across various geographic populations were included. Studies involving children with other chronic health conditions or animal studies on the physis were excluded. Study quality was evaluated using the methodological index for nonrandomized studies scoring system. Descriptive statistics were presented as absolute frequencies with percentages or as weighted means with corresponding measures of variance where applicable.

RESULTS: Fifteen studies (5,467 patients) from North America, Europe, Asia, and Oceania met inclusion criteria. SCFE patient samples ranged from 55 to 1,630, with some larger cohorts monitoring multiple medical conditions. The mean age was 12.0 years (SD = 0.4), and male-to-female ratios ranged from 1.43:1 to 3.12:1. SCFE incidence varied by region, from 50.5 per 100,000 (Sweden) to 0.33 per 100,000 (South Korea), with a pooled incidence of 9.62 per 100,000. Overweight prevalence was highest in Sweden (66%) and South Korea (67.6%) and lowest in Japan (11.8%). Unilateral SCFE predominated (68.4% to 90.6%). In situ screw fixation was the most common treatment, with 1 study reporting intertrochanteric osteotomy.

CONCLUSION: Geographic variation in SCFE incidence suggests multifactorial influences beyond obesity, including socioeconomic factors, healthcare access, and genetic predisposition. Limited high-quality comparative studies and inconsistent BMI criteria highlight the need for further research to clarify SCFE risk factors.

LEVEL OF EVIDENCE: Level IV, systematic review. See Instructions for Authors for a complete description of levels of evidence.

PMID:40403127 | DOI:10.2106/JBJS.RVW.25.00052

J Med Internet Res. 2025 May 22;27:e63961. doi: 10.2196/63961.

ABSTRACT

BACKGROUND: Virtual reality (VR) simulators are increasingly used in dental education, offering advantages such as repeatable practice and immediate feedback. However, evidence comparing their efficacy to traditional phantom heads for veneer preparation training remains limited.

OBJECTIVE: This study aimed to compare the effectiveness of 2 widely used VR simulators (Unidental and Simodont) against traditional phantom heads for veneer tooth preparation training and evaluate the impact of training sequence (simulator-first vs phantom-head-first) on skill acquisition.

METHODS: A randomized controlled trial was conducted with 80 fourth-year dental students from Peking University School of Stomatology. Participants were stratified by gender and academic performance, then equally allocated to 8 groups. Groups 1-3 trained exclusively using Unidental, Simodont, or phantom heads, respectively, while groups 4-8 followed hybrid sequences combining simulator and phantom-head training. Each participant performed veneer preparations on a maxillary central incisor. Preparations were evaluated by a blinded instructor using a validated 100-point rubric assessing marginal integrity (30%), preparation depth (25%), proximal contour (25%), and surface smoothness (20%). Posttraining questionnaires (100-point scale) compared user perceptions of simulator realism, haptic feedback, and educational value.

RESULTS: There were no statistically significant differences in the preparation quality among groups using different training methods (Unidental: 88.9, SD 3.6; Simodont: 88.6, SD 1.6; phantom heads: 89.4, SD 2.8; P=.81) or different training methodologies (simulator-first vs phantom-head-first) (simulator first: P=.18; phantom head first: P=.09, different sequences of Unidental: P=.16; different sequences of Simodont: P=.11). However, significant differences were observed between the evaluations of the 2 simulators in terms of realism of the odontoscope’s reflection (Simodont: 55.6, SD 33.7; Unidental: 87.5, SD 13.9; P<.001), force feedback (Simodont: 66.2, SD 22.4; Unidental: 50.8, SD 18.9; P=.007), and simulation of the tooth preparation process (Simodont: 64.4, SD 16.0; Unidental: 50.6, SD 16.6; P=.003). Evaluation results showed no statistical differences between the 2 simulators in display effect (Simodont: 77.43, SD 21.58; Unidental: 71.68, SD 20.70; P=.24), synchronism of virtual and actual dental instruments (Simodont: 67.86, SD 19.31; Unidental: 59.29, SD 20.10; P=.11), and dental bur operation simulation (Simodont: 63.32, SD 19.99; Unidental: 55.79, SD 19.62; P=.16). The Unidental simulator was rated better than the Simodont simulator in terms of the realism of odontoscope’s reflection. In all other aspects, Simodont was superior to Unidental. There was no significant difference in the students’ attitudes towards the 2 simulators (improve skills: P=.19; inspire to learn: P=.29; will to use: P=.40; suitable for training: P=.39).

CONCLUSIONS: The study found no significant differences in training outcomes between VR simulators and traditional phantom heads for veneer preparation, suggesting that VR technology may serve as a viable alternative or supplementary tool in dental education. However, the absence of significant differences does not imply equivalence, as formal equivalence testing was not performed. Future studies should incorporate equivalence testing and explore cost-effectiveness, long-term skill retention, and adaptability to complex clinical scenarios.

PMID:40402564 | DOI:10.2196/63961

JMIR Hum Factors. 2025 May 22;12:e62960. doi: 10.2196/62960.

ABSTRACT

BACKGROUND: Problem anger is common after experiencing trauma and is under-recognized relative to other posttraumatic mental health issues. Previous research has shown that digital mental health tools have significant potential to support individuals with problem anger after trauma.

OBJECTIVE: The objective of this study was to describe the co-design and development of a just-in-time adaptive intervention (JITAI) targeting problem anger in individuals who have experienced trauma.

METHODS: We used a participatory design process following the double-diamond framework. Phase 1 involved one-on-one qualitative interviews with trauma-exposed individuals with problem anger (n=10). Using an inductive approach (interpretative phenomenological analysis), we thematically coded interview data to create design principles for this population and generate potential content for the intervention. Phase 2 involved academic and clinical experts in trauma and experts in digital health reviewing the Phase 1 results and an evidence-based cognitive behavioral approach to treating anger. We then created intervention content and prototypes, which we then took to workshops with all participants for feedback, using group discussions and ratings of desirability and feasibility.

RESULTS: From Phase 1, core considerations for a JITAI included look and feel preferences, self-led and personalized support and content, and different support needed for each anger stage. A JITAI was developed with the following components: (1) personalized schedules and content onboarding; (2) psychoeducation about problem anger; (3) crisis support; (4) mood monitoring via anger check-ins; (5) self-led and personalized circuit breakers; (6) cognitive-behavioral based skills; (7) and a digital Coach embedded in the app. Some suggested features, such as social networking and sharing data with loved ones, were not pursued due to feasibility reasons relating to participant safety or technical costs.

CONCLUSIONS: The resulting JITAI, termed “Shift,” is the first digital mental health tool designed with end users to manage anger after trauma.

PMID:40402559 | DOI:10.2196/62960

Interact J Med Res. 2025 May 22;14:e64829. doi: 10.2196/64829.

ABSTRACT

BACKGROUND: The global targets for HIV testing for achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets are still short. Identifying gaps and opportunities for HIV testing uptake is crucial in fast-tracking the second (initiate people living with HIV on antiretroviral therapy) and third (viral suppression) UNAIDS goals. Machine learning and health technologies can precisely predict high-risk individuals and facilitate more effective and efficient HIV testing methods. Despite this advancement, there exists a research gap regarding the extent to which such technologies are integrated into HIV testing strategies worldwide.

OBJECTIVE: The study aimed to examine the characteristics, citation patterns, and contents of published studies applying machine learning and emerging health technologies in HIV testing from 2000 to 2024.

METHODS: This bibliometric analysis identified relevant studies using machine learning and emerging health technologies in HIV testing from the Web of Science database using synonymous keywords. The Bibliometrix R package was used to analyze the characteristics, citation patterns, and contents of 266 articles. The VOSviewer software was used to conduct network visualization. The analysis focused on the yearly growth rate, citation analysis, keywords, institutions, countries, authorship, and collaboration patterns. Key themes and topics were driven by the authors’ most frequent keywords, which aided the content analysis.

RESULTS: The analysis revealed a scientific annual growth rate of 15.68%, with an international coauthorship of 8.22% and an average citation count of 17.47 per document. The most relevant sources were from high-impact journals such as the Journal of Internet Medicine Research, JMIR mHealth and uHealth, JMIR Research Protocols, mHealth, AIDS Care-Psychological and Socio-Medical Aspects of AI, and BMC Public Health, and PLOS One. The United States of America, China, South Africa, the United Kingdom, and Australia produced the highest number of contributions. Collaboration analysis showed significant networks among universities in high-income countries, including the University of North Carolina, Emory University, the University of Michigan, San Diego State University, the University of Pennsylvania, and the London School of Hygiene and Tropical Medicine. The discrepancy highlights missed opportunities in strategic partnerships between high-income and low-income countries. The results further demonstrate that machine learning and health technologies enhance the effective and efficient implementation of innovative HIV testing methods, including HIV self-testing among priority populations.

CONCLUSIONS: This study identifies trends and hotspots of machine learning and health technology research in relation to HIV testing across various countries, institutions, journals, and authors. The trends are higher in high-income countries with a greater focus on technology applications for HIV self-testing among young people and priority populations. These insights will inform future researchers about the dynamics of research outputs and help them make scholarly decisions to address research gaps in this field.

PMID:40402556 | DOI:10.2196/64829

JMIR Public Health Surveill. 2025 May 22;11:e68213. doi: 10.2196/68213.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic, several US jurisdictions began to regularly report levels of SARS-CoV-2 in wastewater as a proxy for SARS-CoV-2 incidence. Despite the promise of this approach for improving COVID-19 situational awareness, the degree to which wastewater surveillance data agree with other data has varied, and better evidence is needed to understand the situations in which wastewater surveillance data track closely with traditional surveillance data.

OBJECTIVE: In this study, we quantified the statistical relationship between wastewater data and traditional case-based surveillance data for multiple jurisdictions.

METHODS: We collated data on wastewater SARS-CoV-2 RNA levels and COVID-19 case reports from July 2020 to March 2023 for 107 counties representing a range in terms of geographic location, population size, and urbanicity. For these counties, we used Bayesian hierarchical regression modeling to estimate the statistical relationship between wastewater data and reported cases, allowing for variation in this relationship across counties. We compared different model structural approaches and assessed how the strength of the estimated relationships varied between settings and over time.

RESULTS: Our analyses revealed a strong positive relationship between wastewater data and COVID-19 cases for the majority of locations, with a median correlation coefficient between observed and predicted cases of 0.904 (IQR 0.823-0.943). In total, 23/107 counties (21.5%) had correlation coefficients below 0.8, and 3/107 (2.8%) had values below 0.6. Across locations, the COVID-19 case rate associated with a given level of wastewater SARS-CoV-2 RNA concentration declined over the study period. Counties with greater population size (P<.001) and higher levels of urbanicity (P<.001) had stronger concordance between wastewater data and COVID-19 cases. Measures of model fit, and relationships with urbanicity and population size, were robust to sensitivity analyses in which we varied the time period of analysis and the sample of counties used for model fitting.

CONCLUSIONS: In a sample of 107 US counties, wastewater surveillance had a close relationship with COVID-19 cases reported for the majority of locations, with these relationships found to be stronger in counties with greater population size and urbanicity. In situations where routine COVID-19 surveillance data are less reliable, wastewater surveillance may be used to track local SARS-CoV-2 incidence trends.

PMID:40402554 | DOI:10.2196/68213

JMIR Hum Factors. 2025 May 22;12:e72838. doi: 10.2196/72838.

ABSTRACT

Traditional medical education encounters several challenges. The introduction of advanced facial expression recognition technology offers a new approach to address these issues. The aim of the study is to propose a medical education-assisted teaching and student evaluation method based on facial expression recognition technology. This method consists of 4 key steps. In data collection, multiangle high-definition cameras record students’ facial expressions to ensure data comprehensiveness and accuracy. Facial expression recognition uses computer vision and deep learning algorithms to identify students’ emotional states. The result analysis stage organizes and statistically analyzes the recognized emotional data to provide teachers with students’ learning status feedback. In the teaching feedback stage, teaching strategies are adjusted according to the analysis results. Although this method faces challenges such as technical accuracy, device dependency, and privacy protection, it has the potential to improve teaching effectiveness, optimize personalized learning, and promote teacher-student interaction. The application prospects of this method in medical education are broad, and it is expected to significantly enhance teaching quality and students’ learning experience.

PMID:40402552 | DOI:10.2196/72838

JMIR Mhealth Uhealth. 2025 May 22;13:e56176. doi: 10.2196/56176.

ABSTRACT

BACKGROUND: Exercise therapy is strongly recommended as a treatment for chronic nonspecific low back pain (CNSLBP). However, therapist-guided exercise therapy requires significant medical resources. Ordinary digital telerehabilitation affects efficacy due to a lack of guidance and dynamic support. Artificial intelligence (AI)-assisted interactive health promotion systems may solve these problems.

OBJECTIVE: We aimed to explore whether AI-assisted multimodal exercise telerehabilitation is superior to conventional telerehabilitation in the treatment of people with CNSLBP.

METHODS: This study was a prospective, double-arm, open-label, randomized clinical controlled trial. People with CNSLBP were randomly allocated to either the AI or video group, receiving AI-assisted multimodal exercise therapy or conventional video guidance, respectively, via a WeChat application add-in. The multimodal exercise consisted of deep core muscle, flexibility, Mackenzie, and breathing exercises. The exercises were performed for 30-45 minutes per session, 3 times a week, for 4 weeks. Participants underwent face-to-face assessment at baseline and week 4, and web-based assessment at weeks 2 and 8. The primary outcome was the change in Numerical Rating Scale (NRS) relative to baseline at week 4. Secondary outcomes included changes in the Roland-Morris Disability Questionnaire (RMDQ), Oswestry Disability Index (ODI), Pain Castastrophizing Scale (PCS), Timed Up-and-Go (TUG) test, and thickness of the transverse abdominus (TrA) and multifidus (MF) muscles relative to baseline at week 4. Generalized estimating equation and covariance were used to examine the efficacy of the interventions.

RESULTS: A total of 38 participants (19 participants per group) were recruited. Eighteen participants in the AI group and 16 participants in the video group completed and were included in the final analysis. There was a significant difference in NRS at week 4 between the AI group and video group (most severe NRS: -3.00 vs -1.50; adjusted mean difference -1.08, 95% CI -1.68 to -0.49; P<.001; mean NRS: -2.61 vs -1.62; adjusted mean difference -0.67, 95% CI -1.19 to -0.15; P=.01). The difference in most severe NRS persisted until week 8 (-3.06 vs -1.69; adjusted mean difference -0.95, 95% CI -1.73 to -0.18; P=.02). Compared with the video group at week 4, the AI group showed significant improvement in secondary outcomes, including RMDQ, PCS, and core muscle thickness of left TrA, right TrA, left MF, and right MF.

CONCLUSIONS: We showed that 4 weeks of telerehabilitation based on AI-assisted multimodal exercise has better therapeutic effects compared to conventional exercise telerehabilitation in people with CNSLBP. This study provides guidance for developing effective real-time home-based exercise therapies for people with CNSLBP, which may help reduce economic and human resource costs associated with treatment.

PMID:40402551 | DOI:10.2196/56176