Category: Nevin Manimala

J Intellect Disabil. 2025 Nov 14:17446295251392108. doi: 10.1177/17446295251392108. Online ahead of print.

ABSTRACT

The concept of ‘vulnerability’ in children is critical to needs-related planning and risk management. Despite proliferation of measures there is limited evidence-base to support the validity of existing, relevant clinical assessments. The FACE CARAS young person’s risk assessment toolkit includes a measure of vulnerability-the Learning Disability Vulnerability Assessment Scale (LD-VAS). Good inter-rater reliability has been reported but construct validity has not previously been demonstrated. The aims of this study were to assess the construct-validity of the tool by: (i) evaluating the dimensionality of the ratings produced, and (ii) modelling the ability of the scores to quantify the care needs of young people. LD-VAS ratings were available for 143 young people, the dimensionality of the scale ratings was assessed using a parallel analysis and confirmatory factor analysis (CFA). The ability of scores to predict care-level was modelled using discriminant function analysis and multinomial logistic regression. A single factor CFA model showed a good fit to the data. The discriminant function analysis suggested several scoring profiles exist, relating to care-level. On multinomial logistic regression the scores could statistically significantly differentiate between those in the lowest and higher intensity care categories. The LD-VAS appears to have construct validity and is potentially useful in supporting rational decision-making regarding care-provision for children affected by learning disability.

PMID:41235502 | DOI:10.1177/17446295251392108

Stud Health Technol Inform. 2025 Nov 12;333:88-89. doi: 10.3233/SHTI251582.

ABSTRACT

This study assessed nurse practitioner students’ engagement with Isabel, an AI-based differential diagnosis tool, during training. A survey of 26 students revealed mixed usage, with 44% using it regularly to confirm diagnoses. The tool scored an average of 2.16 for usability. While Isabel boosted diagnostic confidence and accuracy for many, inadequate training limited effectiveness for some. The findings highlight the necessity of structured, hands-on training to successfully integrate AI tools into nursing curricula and enhance digital competency in healthcare education.

PMID:41235498 | DOI:10.3233/SHTI251582

Stud Health Technol Inform. 2025 Nov 12;333:70-75. doi: 10.3233/SHTI251578.

ABSTRACT

Accurate hospital bed demand forecasting is critical for ensuring effective patient care and efficient resource allocation. This study evaluates various statistical and machine learning methods to predict daily and hourly inpatient admissions, separations, and emergency department (ED) presentations up to one year in advance. The Advanced Demand Prediction Tool (ADePT) is introduced, which leverages the SARIMAX time series model to capture trends, seasonal patterns, and public holiday effects. Its performance is evaluated using data from a large provider of tertiary health services in Melbourne, Australia against five other statistical and machine learning forecasting models, including rolling window, six-week rolling average, negative binomial regression, an ensemble approach, and random forest regression. The results demonstrated that ADePT generally outperformed other methods when predicting inpatient admissions and separations for multiple forecast horizons. For ED presentations, differences in accuracy were not statistically significant. Importantly, ADePT also showed high accuracy when applied to smaller patient subgroups, including emergency and elective inpatient admissions. By providing reliable short-term and long-term forecasts, ADePT could support more effective daily bed management as well as improved long-term capacity planning.

PMID:41235495 | DOI:10.3233/SHTI251578

Stud Health Technol Inform. 2025 Nov 12;333:64-69. doi: 10.3233/SHTI251577.

ABSTRACT

In Australia, heatwaves result in more fatalities than any other natural disaster, underscoring their significant public health impact. Heatwaves have been associated with heightened ambulance demand, and this study examines their relationship with emergency department (ED) presentations for circulatory and respiratory diseases. The analysis, focusing on the peak heatwave months of December and January over five years, revealed a positive correlation between maximum temperatures and ED presentations. Specifically, ED presentations increased by approximately 4.2% during heatwave periods and 3.9% during non-heatwave periods for every one-degree Celsius rise in maximum temperature. These findings suggest that, alongside well-recognised factors such as population growth and an ageing population, climate change poses an additional and significant challenge to the healthcare system. As maximum temperatures rise, the increased demand for emergency healthcare services could hinder the timely delivery of critical care, necessitating proactive planning and adaptation to ensure resilience in the face of a warming climate.

PMID:41235494 | DOI:10.3233/SHTI251577

Stud Health Technol Inform. 2025 Nov 12;333:40-45. doi: 10.3233/SHTI251573.

ABSTRACT

This study investigates the persistent underperformance of health information systems (HISs) in rural Indonesian mental healthcare, despite national digital health initiatives. Utilising a socio-technical systems theoretical lens, an eight-month exploratory qualitative study was conducted, involving focus groups, in-depth interviews with healthcare providers, community health workers, and residents, alongside a literature review. Thematic analysis identified three critical socio-technical misalignments hindering HIS effectiveness: severe data integration issues due to fragmented tools and lack of interoperability; significant resource constraints (technical, human, and budgetary), and pervasive cultural and social stigma, which impede help-seeking, data accuracy and holistic care delivery. The study concludes that these are not technological failures but systemic design breakdowns, and calls for a situated, multi-stakeholder approach to co-design context-sensitive, user-centred HISs that integrate informal work systems, thereby laying foundations for equitable mental healthcare in resource-limited environments.

PMID:41235490 | DOI:10.3233/SHTI251573

Stud Health Technol Inform. 2025 Nov 12;333:28-33. doi: 10.3233/SHTI251571.

ABSTRACT

Prediabetes presents a critical window to prevent type 2 diabetes, a rising global health crisis, yet young adults often lack engaging preventive tools. This ongoing study aims to design and evaluate a web application to enhance health knowledge, engagement, and self-management for this at-risk group. This theoretical lens combines Design Science Research Methodology (DSRM), the theory of Task-Technology Fit (TTF), and the Unified Theory of Acceptance and Use of Technology (UTAUT). The proposed solution incorporates a unique combination of features learned through a previously conducted systematic literature review (SLR). Features include Machine Learning (ML)-based recommendations, educational modules, goal setting, gamification elements, and an artificial intelligence (AI)-incorporated chatbot. The proposed design to date is presented, in addition to the planned scenario-driven use cases to highlight the relevance of the proposed solution. A pilot study will assess usability, usefulness, satisfaction, and health knowledge via initial, midway, and final surveys mapped along with the design process. The data will be analysed via descriptive statistics and thematic analysis. This work-in-progress paper offers a streamlined, user-centred approach to designing and developing digital health interventions for prediabetes prevention while contributing insights for personalised digital health interventions.

PMID:41235488 | DOI:10.3233/SHTI251571

Stud Health Technol Inform. 2025 Nov 12;333:14-19. doi: 10.3233/SHTI251568.

ABSTRACT

This study explores Australian consumers’ digital literacy (DL), use of digital health technologies (DHTs), and curiosity toward emerging tools. A cross-sectional online survey (n = 416) examined DL levels, current usage of technologies such as telehealth, wearables, mHealth apps, e-pharmacy, and chatbots, and preferences for future innovations like smart glasses, virtual reality/augmented reality, medical drones, and robot companions. DL was highest in data and communication domains and varied by age, gender, education, and location. Despite women and younger adults reporting higher DL, technology adoption often hinged on perceived usefulness, usability, and trust. Telehealth was widely used (90%+) while emerging technologies attracted greater curiosity from men and the 30-39 age group. These findings suggest that curiosity – both diversive and specific – drives early exploration and continued engagement with DHTs. To support equitable adoption, digital health strategies should integrate DL-building interventions and curiosity-driven design, aligned with the Australian Digital Health Strategy’s goals for inclusive, consumer-centred innovation.

PMID:41235485 | DOI:10.3233/SHTI251568

Knee Surg Sports Traumatol Arthrosc. 2025 Nov 14. doi: 10.1002/ksa.70191. Online ahead of print.

ABSTRACT

PURPOSE: To develop a Kellgren-Lawrence (K-L) grading recognition framework for knee osteoarthritis (KOA) with enhanced capability for early-stage detection and to validate its transferability across three independent cohorts.

METHODS: Weight-bearing anteroposterior knee radiographs were obtained from three datasets: the osteoarthritis initiative (OAI), Wuchuan and Shunyi. The OAI dataset included baseline, 72-month, and 96-month follow-up images, while the Wuchuan and Shunyi datasets were collected from Wuchuan (China) and Shunyi District (Beijing), respectively. Contrastive learning was incorporated into model training to construct the Augmented Dataset-Wide-ResMRnet-Contrastive Loss-Cross Entropy (AW2C) framework.

RESULTS: The AW2C framework achieved overall classification accuracies of 83.0%, 82.0% and 80.5% on the OAI, Wuchuan and Shunyi datasets, respectively, with corresponding area under the curve (AUC) of 97.0%, 96.7% and 95.6%. Compared with the baseline model, accuracy for K-L grade 2 improved from 64% to 80%, and discrimination between K-L grades 1 and 2 was notably enhanced.

CONCLUSIONS: The proposed AW2C framework demonstrated robust and transferable performance for automated radiographic K-L grading of KOA, particularly improving recognition of early-stage and suspected disease. With further optimisation, it holds promise as a reliable tool for large-scale studies and clinical decision support.

LEVEL OF EVIDENCE: Level III.

PMID:41235478 | DOI:10.1002/ksa.70191

Knee Surg Sports Traumatol Arthrosc. 2025 Nov 14. doi: 10.1002/ksa.70194. Online ahead of print.

NO ABSTRACT

PMID:41235476 | DOI:10.1002/ksa.70194

Epilepsia Open. 2025 Nov 14. doi: 10.1002/epi4.70185. Online ahead of print.

ABSTRACT

OBJECTIVE: This study evaluated the long-term risk of major cardiovascular diseases (CVDs) in patients with epilepsy using a nationwide cohort, aiming to address critical gaps in population-based evidence on brain-heart interactions.

METHODS: Data from the Korean National Health Insurance Service (2002-2013) were analyzed. For each cardiovascular outcome, an independent matched cohort was constructed, comprising 1740 to 3164 patients with newly diagnosed epilepsy and corresponding 10-fold matched controls. The primary outcomes included six CVDs: hypertension (HTN), ischemic heart disease (IHD), cardiac arrhythmia (CA), heart failure (HF), atherosclerosis (AS), and peripheral artery disease (PAD). Incidence rate ratios (IRRs) and adjusted hazard ratios (aHRs) were calculated using multivariable Cox regression models.

RESULTS: Epilepsy was significantly associated with increased risk of all six CVDs. The highest aHRs were observed for CA (2.02 [95% CI, 1.70-2.39]), IHD (1.71 [95% CI, 1.50-1.95]), and HF (1.64 [95% CI, 1.28-2.10]). Risk was higher in patients aged <60 years and in men. Notably, younger patients showed substantially elevated risks for CA (2.61 [95% CI, 1.99-3.42]) and AS (2.06 [95% CI, 1.47-2.89]). The sex-specific difference was most prominent for HF, with higher aHRs in men (1.86 [95% CI, 1.29-2.67]) than in women (1.49 [95% CI, 1.07-2.09]).

SIGNIFICANCE: Patients with epilepsy have a significantly increased long-term risk of CVD, especially CA, IHD, and HF. Risk is disproportionately elevated in younger individuals and men, suggesting the need for targeted cardiovascular surveillance and prevention in these subgroups.

PLAIN LANGUAGE SUMMARY: People with epilepsy may face a higher risk of heart and blood vessel diseases such as heart attack, irregular heartbeat, and heart failure. This study analyzed national health data from Korea and found that epilepsy patients had more cardiovascular problems than those without epilepsy, especially younger men. These results suggest that doctors should monitor heart health more closely in people living with epilepsy.

PMID:41235461 | DOI:10.1002/epi4.70185