OBJECTIVES:This study aims to explore the key factors that enhance engagement in Facebook health support groups among Australian culturally and linguistically diverse (CALD) communities. METHODS:A cross-sectional online survey was conducted using convenience sampling. A total of 1,145 CALD participants residing in New South Wales, Australia, were initially recruited. From this sample, 150 participants who self-reported regular engagement with Facebook health support groups were included in the final analysis. A pilot test (n = 30) demonstrated strong internal consistency (Cronbach's alpha >0.70). Data collection involved a structured questionnaire employing a 7-point Likert scale to assess factors such as motivation, trust, perceived support (received and given), social connectedness, and sense of virtual community. RESULTS:Motivation and trust significantly influenced both support dynamics and the perceived sense of virtual community. The sense of virtual community, in turn, strongly predicted engagement in Facebook health support groups. Interestingly, social connectedness alone was not a significant predictor of engagement. CONCLUSIONS:Fostering a strong sense of virtual community appears to be a critical factor in encouraging sustained engagement in digital health platforms among CALD populations.

The persistent scarcity of eye donations remains a significant concern. Various measures have been implemented to increase eye donation rates and mitigate supply shortages. Central to these efforts is the enhancement of awareness and knowledge, which are essential in overcoming barriers to donation. Persuasive Technology Design was employed to ensure the artefact was developed to increase individual awareness and knowledge of donation voluntarily. Nevertheless, the ethical advancement of eye donation procedures requires that informed consent be obtained from donors. The core values related to eye donation were identified by stakeholders with expertise in the field who participate actively in the design and development of the application. These values have guided the development of the app’s content and features, utilizing the Persuasive System Design and Value Sensitive Design frameworks as the theoretical foundation. The study aims to identify and clarify the primary values associated with eye donation artefacts, potentially informing the development of similar artefacts for other organ donations. Further research is needed to assess the significance of these values in enhancing awareness and understanding of donation, thereby facilitating an increase in eye donation rates.

Mobile health apps are becoming increasingly popular and are viewed as supplementary aids that simplify healthcare tasks, facilitate patient follow-up, monitor patient symptoms, and support self-care. Their utility may be essential in low-resource countries including Ethiopia. However, their accessibility, availability, trustworthiness, and credibility were identified as the main perceived challenges when recommending the app to patients. To explore mental healthcare professionals’ perceptions and perceived challenges about mental health apps for patients with depression and anxiety. Two focus group discussions were conducted with mental healthcare professionals (n = 14) from February 10–20, 2024, at Amanuel Mental Specialized Hospital in Ethiopia. The collected audio-recorded data were transcribed into text, coded, and then exported to NVivo version 14 for analysis. A total of 14 multi-disciplinary mental health professionals participated in focus group discussions. The first focus group consisted of 8 study participants, and the second focus group comprised 6 participants. Most of the interviewed health professionals (12) were male. Four main themes were identified: (1) perceived benefit (improvement of patients’ well-being and mental health accessibility), (2) perceived alleviation of healthcare work burden, (3) perceived challenges (usability, accessibility, trustworthiness, credibility and effectiveness), and (4) suggested app features (psychoeducational, self-assessment and self-monitoring, self-care, multimedia and reminders). The involvement of mental health professionals is vital for the successful development of mobile mental health apps. App features such as psychoeducational, self-assessment, self-monitoring, self-care, multimedia, and reminders received positive feedback from healthcare professionals. However, participants expressed and perceived challenges about the app’s accessibility, availability, effectiveness, trustworthiness, and credibility.

Abstract Background Mobile Health applications (mHealth) have become a promising approach to support self-management of coronary heart disease (CHD). No previous studies have examined user acceptance constructs, and the results remain inconsistent. The aim of this study is to synthesize and quantify the pooled prevalence of current use, intention to use, perceived usefulness, and positive user attitudes toward mHealth apps among patients with coronary heart disease. Methods A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251018916). Five electronic databases (PubMed, Web of Science, Cochrane Library, CINAHL, and SCOPUS) were searched for studies published in English between January 2015 and April 2025. Primary studies reporting at least one acceptance-related construct: actual use, intention to use, perceived usefulness, and positive user attitude among patients with CHD or cardiac events were included. Random-effects models (metaprop, REML) were used to estimate pooled prevalence. Heterogeneity, sensitivity test, subgroup analysis, and publication bias assessments were performed. Results A total of 6113 participants in 17 studies. The pooled prevalence of actual use was [39% (95% CI: 24%-54%)], intention to use was [61% (95% CI: 53%-69%)], perceived usefulness was [69% (95% CI: 49%-88%)], and positive user attitude was [80% (95% CI: 69%-91%)]. Substantial heterogeneity was observed across studies. Sensitivity analysis indicated no influential outliers. The funnel plot and Egger’s test indicate no statistically significant publication bias. However, the findings should be interpreted with caution due to substantial heterogeneity and a small number of studies. Conclusion According to our findings, despite strong behavioural intention, high perceived usefulness, and positive user attitudes, actual usage remains relatively low, highlighting a gap between acceptance and implementation. The findings provide a potential quantitative basis for guiding the design and development of mHealth applications and for emphasizing user-centered interfaces to translate acceptance into sustained engagement in CHD self-management. These findings reveal significant untapped potential for mHealth-supported CHD self-care and the closely related cardiovascular population with similar self-management needs.

Mobile mental health applications (MMHAs) enable healthcare professionals to efficiently screen, identify, and refer individuals with mental health conditions, while also providing access to evidence-based educational resources. Although MMHAs are increasingly recognised as promising tools for improving mental healthcare delivery, little is known about healthcare professionals’ intentions to use these applications in routine clinical practice. Evidence on the factors influencing healthcare professionals’ intended use of MMHAs also remains limited. Therefore, this study aimed to assess healthcare professionals’ intentions to use MMHAs and associated factors. This cross-sectional study involved 427 health professionals, with data collected online using quota sampling. The single population proportion formula was used to calculate the sample size. Behavioral intention to use MMHAs was assessed using a modified e-Delphi study. Associated factors were identified through bivariate and multivariate logistic regression analyses. Odds ratios (ORs) with 95

BACKGROUND:Digital Health is currently showing promising results in reducing patient and caregiver suffering that arise from misconceptions. OBJECTIVE:To synthesize existing evidence on Perceived Usefulness, interest in use and willingness to use towards Epilepsy Digital Health Interventions. METHOD:Databases were searched for studies reporting on the outcomes of interest by using a comprehensive search strategy. Studies published in English from January 2015 to September 2025 were included. The Newcastle-Ottawa Quality Assessment Scale was employed to evaluate the quality of included studies. Stata version 19 was used to compute a pooled proportion using a random-effects model. Heterogeneity was assessed using the Cochrane chi-square and the index of heterogeneity test. Sensitivity tests and subgroup analyses were performed. Publication bias was examined by funnel plots and Egger's test. RESULT:Overall, 6041 studies were found from databases. After a step-by-step screening, 23 studies were included in this review. The total number of participants was 6703 with a sample size ranges from 12 to 1168. The pooled proportions of Perceived Usefulness, interest to use, and willingness to use Digital Health were 0.66 (0.58, 0.75), 0.69 (0.50, 0.88), and 0.75 (0.66, 0.83), respectively. In this review, Sensitivity tests indicated that none of the included studies exerted extreme influence on the pooled prevalence; and Funnel plots and Egger's test (p ≤ 0.772) showed no evidence of publication bias. CONCLUSION:In this review, 66% of respondents perceive Digital Health as useful; 69% were interested in using Digital Health, and 75% were willing to engage with Digital Health. Most of the studies were from high-income countries, with no studies found from developing countries. This review emphasizes the importance of focusing on the user's perceptions, their interest and willingness to use Digital Health Interventions. It also stresses the need for further studies in low-income countries.

BACKGROUND:Despite growing demand for palliative care, access remains limited and often occurs late in life. Existing machine learning approaches rarely integrate clinically validated measures of symptom burden and functional status with healthcare utilization, limiting the interpretability of care trajectories. OBJECTIVES:To (1) identify distinct patient subgroups based on symptom burden and functional status, and (2) predict palliative care episode duration using the identified subgroups. METHOD:A retrospective cohort study was conducted using national data from the Australian Palliative Care Outcomes Collaboration, including adults (≥18 years) who died between 2014 and 2023 (261,290 patients). Unsupervised clustering (Partitioning Around Medoids, K-means, Hierarchical, Gaussian Mixture Models) was used to derive symptom-function clusters with bootstrap stability and clinical validity assessed. Supervised prediction models (Elastic Net, Random Forest, XGBoost) predicted care duration using out-of-fold validation and a held-out test set. Post-hoc calibration addressed right-skewed outcomes. Model performance and prediction error were examined across diagnostic groups, clusters, and clinically defined episode duration categories. Explainability analyses were conducted to interpret model behavior. RESULTS:Clustering consistently identified a severity gradient, with a critical transition in mid-range functional states where symptom burden and care needs escalated rapidly. Predictive performance was modest (R² ≈ 0.2 after calibration), reflecting the complexity of palliative care trajectories. Calibration improved agreement between predicted and observed outcomes, particularly for longer episodes. In contrast, clustering did not improve predictive accuracy but enhanced interpretability by summarizing patient heterogeneity. Prediction accuracy varied systematically across clusters, diagnostic groups, and episode duration, with higher accuracy in shorter, late-stage episodes and greater uncertainty in longer trajectories. Functional status and episode type were the dominant drivers of care duration. CONCLUSIONS:Palliative care needs follow a continuous severity gradient, with prediction accuracy varying across disease stages, most closely aligned to functional status. Prediction is most accurate near the end of life and least accurate earlier, highlighting a clinical paradox where intervention potential is greatest when predictions are most uncertain. Methodologically, these findings show that calibration improves prediction reliability, while clustering enhances interpretability without increasing accuracy. The value of machine learning lies not in maximizing accuracy alone, but in combining calibrated predictions with interpretable stratification to support stage-specific care planning.

This study presents the design and development of a credible, evidence-based nutrition tracking system grounded in the Design Science Research (DSR) and Persuasive Systems Design (PSD) frameworks. Drawing on insights from large-scale user reviews and established nutrition science, the research integrates Australia’s official dietary standards, the Australian Dietary Guidelines (ADG), Nutrient Reference Values (NRV), and the national food composition database to enhance feedback interpretability, behavioural guidance, and potential clinical credibility. The system architecture combines voice-enabled meal logging, semantic food matching, and ADG/NRV-aligned analytics to provide persuasive, guideline-driven feedback while minimising logging effort. The study contributes a replicable methodological approach for translating evidence-based nutrition frameworks and persuasive systems design into practical, user-centred mHealth applications.

The prevalence of excessive gestational weight gain (eGWG) and its associated adverse outcomes pose significant public health challenges. However, the application and effectiveness of persuasive system design (PSD) features in digital interventions for gestational weight gain management have not been systematically examined. This systematic review analyzed 13 digital health interventions for gestational weight management to assess their application of PSD features. A literature search across 7 databases identified 32 articles of pregnancy-specific digital tools with weight-tracking. Analysis revealed Primary Task Support and Dialogue Support domains were most implemented, with Self-monitoring predominating, while Social Support feature remained significantly underutilized. Although most interventions employed multiple PSD domains, greater feature quantity did not correlate with superior outcomes. Strategic implementation of specific PSD features, rather than comprehensive inclusion, proves paramount for effectiveness. Future development should emphasize theoretically grounded design, explore social support mechanisms, and adopt evidence-based feature selection to enhance efficacy.

BACKGROUND:Digital Mental Health Interventions (DMHIs) hold significant potential in addressing gaps in mental health treatment, enhancing mental health literacy, and mitigating associated stigma. However, DMHIs have not been systematically evaluated in terms of potential users' attitudes, perceived usefulness, and intentions to use. Thus, this study aims to consolidate evidence to ascertain users' attitudes, perceived usefulness, and intentions to utilize DMHIs. METHODS:The meta-analysis reports adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. A comprehensive search of databases: Medline, CINHAL, PsycINFO, SCOPUS, and Web of Science, was conducted. As part of the screening process, Covidence database management software was used. Metaprop command was used to calculate the outcome using a random-effects model. Heterogeneity was assessed using Cochrane chi-square (χ2) and the index of heterogeneity (I2 statistics) test. Sensitivity test and subgroup analysis were performed. Publication bias was examined by funnel plots and Egger's test. RESULTS:In total, 26 studies were analyzed, including data from 13,923 participants. The overall percentage of users' positive attitudes, perceived usefulness, and intentions to use DHMIs was 0.66 (95 % CI; 0.52, 0.79), 0.73 (95 % CI; 0.64, 0.81), and 0.67 (95 % CI; 0.6, 0.74), respectively. Significant heterogeneity was observed; nonetheless, sensitivity analyses indicated that none of the included individual studies exerted undue influence on the overall pooled prevalence. Assessment of funnel plots and Egger's test (p ≤ 0.895) showed no evidence of publication bias. CONCLUSION:The results of this meta-analysis indicate that, overall, two-thirds of participants have a positive attitude toward DMHIs, around three-quarters find DMHIs useful, and around two-thirds intend to use them. The findings suggest the need to target users' positive attitudes, perceived utility, and willingness for the improved adoption and sustained use of DMHIs.

BACKGROUND:As machine learning models become increasingly prevalent in palliative care, explainability has become a critical factor in their successful deployment in this sensitive field, where decisions can profoundly impact patient health and quality of life. To address these concerns, Explainable AI (XAI) aims to make complex AI models more understandable and trustworthy. OBJECTIVE:This study aims to assess the current state of machine learning models in palliative care, specifically focusing on their compliance with the principles of XAI. METHODS:A comprehensive literature search in four databases was conducted to identify articles on machine learning in palliative care studies published until May 2024, followed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. The Checklist for Assessment of Medical Artificial Intelligence was used to evaluate the quality of the studies. RESULTS:Mortality and survival prediction were the primary focus areas in 15 (54%) of the included 28 studies. Regarding data explainability, 20 studies (71%) documented their data preprocessing methods. However, a notable concern is that 45% of the studies did not address handling missing data. Across these studies, 74 machine learning algorithms were employed. Complex models, including Random Forest, Support Vector Machines, Gradient Boosting Machines, and Deep Neural Networks, were predominantly used (64%) due to their high predictive power, achieving AUC values between 0.82 and 0.96. Post-hoc explanation techniques were applied in only 11 studies, using seven different XAI techniques, focusing on global explanations to enhance understanding of model behavior. CONCLUSION:Given the critical role of AI-driven decisions in patient care, adopting XAI techniques is essential for fostering trust and usability. Although progress has been made, significant gaps persist. A main challenge remains the trade-off between model performance and interpretability, as highly accurate models often lack the transparency required to build trust in clinical settings. Additionally, complex models frequently provide inadequate explanations for their outputs, lack consistent documentation, and have limited XAI applications, reducing the interpretability of machine learning studies for clinicians and decision-makers.

This study consists of advanced text mining and Natural Language Processing (NLP) technique to analyse user reviews on commercial diet tracking apps, focusing on enhancing user engagement and satisfaction through persuasive System Design (PSD) model. By systematically categorising user feedback into areas such as primary task, dialogue, social and credibility, the research identifies key patterns and factors impacting user interactions, as well as provide deeper insight into how app features influence sustained user engagement and adherence to health goals. The categorised user feedback provides a distinct user reviews into four categorises which allow developers to pin point specific areas of where users are satisfied or requires specific refinements according to the four PSD models. The findings illustrate the diverse influences of PSD elements on user satisfaction and engagement. This methodological approach not only addresses a significant gap in understanding user feedback but also serves as pioneer attempt of using NLP incorporated with PSD model to refine health app features, thereby improving user outcomes and retention in specific areas of persuasive design models.

While European researchers have developed decision support systems for cardiovascular disease (CVD) prevention, Australia lacks a guideline-based knowledge system for assessing primary CVD risk. This thesis addresses this gap by developing two key components: (1) a case-based algorithm reconstructed from Australian CVD guidelines, and (2) an ontology-driven knowledge base that formalizes these guidelines through axiomatic rules. These components form the foundation for a personalized decision support platform to evaluate individual CVD risk profiles. Both the algorithm and ontology require expert validation through risk assessment and reasoning evaluation. The resulting system can facilitate shared decision-making in CVD prevention and potentially contribute to the Unified Medical Language System (UMLS) for integration into BioPortal. Future work should implement SPARQL Query techniques to enhance information retrieval from the ontology framework, addressing limitations identified in this study.

The Persuasive Systems Design (PSD) model identifies software features that enhance mHealth app persuasiveness through behavioural reinforcement. Despite its potential, research remains limited in measuring users' perceptions of these persuasive features. This study validated an instrument for measuring user perceptions of persuasive features in a mHealth app, explored user experiences, and identified areas for improvement. This study used a descriptive cross-sectional online survey to collect quantitative and qualitative data. Construct validity was evaluated through Exploratory Factor Analysis (EFA), with data suitability confirmed by Bartlett's test and Kaiser-Meyer-Olkin measures. Cronbach's alpha assessed internal consistency, and an independent sample t-test examined demographic-response relationships (significance at 0.05, two-tailed).A total of 168 participants completed the survey. EFA revealed a four-factor solution with 23 items explaining 62.71% of total variance. The instrument demonstrated good discriminant validity and excellent internal consistency (Cronbach's alpha > 0.8). No significant demographic variations influenced responses.One-sample t-test results showed significant above-neutral PSD features perception scores (t(167) = -21.7, p < .001), indicating participants perceived all features as persuasive. Thematic analysis of 288 qualitative inputs identified three main themes.Findings confirmed the instrument's validity and reliability for assessing user perceptions of persuasive features in mHealth apps. Practical design recommendations based on the findings are provided.

Mobile applications for depression and anxiety are valuable in low-resource settings with limited mental health services. Engaging users and key stakeholders in application design and development ensure patients' needs are met, particularly in developing countries. Currently, no mental health applications are tailored to the Ethiopian context. The Design Science Research Method was applied to present the requirements of mental health professionals and usability tests. The results of the study informed essential features such as information on depression, anxiety, and psychological distress; self-care strategies; self-assessment tools; and reminders for medication and appointments. The Cognitive Walkthrough (CW) results showed that study participants liked and agreed with most of the app interface design. Furthermore, closed testing results showed high user satisfaction, highlighting its potential to be widely adopted for other types of mental health conditions in Ethiopia. Overall, this paper provides a comprehensive and methodologically rigorous approach to developing a mobile mental health app tailored to the needs of low-resource settings, with a specific focus on Ethiopia. The research contributions lie in identifying key stakeholder requirements, involving mental health professionals in the design process, utilizing iterative feedback from experts, and preparing the app for real-world testing.

Background There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race). Objectives Our objectives are to canvas the range of strategies stakeholders endorse in attempting to mitigate algorithmic bias, and to consider the ethical question of responsibility for algorithmic bias. Methodology The study involves in-depth, semistructured interviews with healthcare workers, screening programme managers, consumer health representatives, regulators, data scientists and developers. Results Findings reveal considerable divergent views on three key issues. First, views on whether bias is a problem in healthcare AI varied, with most participants agreeing bias is a problem (which we call the bias-critical view), a small number believing the opposite (the bias-denial view), and some arguing that the benefits of AI outweigh any harms or wrongs arising from the bias problem (the bias-apologist view). Second, there was a disagreement on the strategies to mitigate bias, and who is responsible for such strategies. Finally, there were divergent views on whether to include or exclude sociocultural identifiers (eg, race, ethnicity or gender-diverse identities) in the development of AI as a way to mitigate bias. Conclusion/significance Based on the views of participants, we set out responses that stakeholders might pursue, including greater interdisciplinary collaboration, tailored stakeholder engagement activities, empirical studies to understand algorithmic bias and strategies to modify dominant approaches in AI development such as the use of participatory methods, and increased diversity and inclusion in research teams and research participant recruitment and selection.

Previous studies have shown that providing donation education through smartphone applications (apps) significantly enhances knowledge and improves users' attitudes. In this study, two hundred forty participants were surveyed to determine the perceived persuasiveness of the Eye Donor Aust app. SPSS software was employed to test the correlations between demographic variables and analyze data distribution, and Structural Equation Modeling was created to explore the multivariate causal relationships among constructs. The findings indicate that three PSD principles (Credibility Support, Dialogue Support, and Social Support) significantly enhanced the app's persuasiveness, while participants' age and gender were significantly associated with the app's persuasiveness. These findings can inform the development of high-quality educational apps employing PSD principles that effectively persuade users.