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Decentralised clinical trials (DCTs) may help address underrepresentation in digital mental health research, but their effectiveness in reaching underserved populations is unclear. This review assessed the reporting of equity-relevant demographic data in DCTs to identify groups at risk of exclusion and barriers and facilitators to inclusive participation. A systematic search was conducted in MEDLINE, PsycINFO, Embase, CINAHL, Cochrane Central Register of Controlled Trials, and Web of Science. We included studies reporting on mental health interventions evaluated via remote, online, virtual, or hybrid DCTs, published in English from 2020-2026 (last search date: 01/07/2025), that reported participant demographics. Demographic data were extracted and summarised according to the PROGRESS-Plus framework. Demographic frequencies were compared to national population statistics. Thematic analysis identified barriers and enablers to inclusive participation in DCTs. Fifty-nine papers reporting 57 DCTs were included. Studies involved a range of mental health and neurodevelopmental conditions across the ages. Gender (100%) and age (100%) were universally reported. Reporting of other PROGRESS-Plus variables across the 57 DCTs was limited: social capital (43.9%); race/ethnicity (40.4%); occupation (36.8%); socioeconomic status (35.1%); place of residence (12.3%); religion (5.3%), and non-mental health disability (1.8%). Participants from ethnic minority backgrounds, males, unemployed individuals, and those with lower educational attainment were consistently underrepresented. While rural populations were better represented in Australian studies, data on poverty, religion, and social capital were limited and varied in representativeness. Most studies focused on adults aged 18-50 years. Thematic analysis identified key barriers including, digital exclusion, low digital literacy, cognitive and sensory challenges. Facilitators included therapist or navigator support and simplified onboarding. Equity variables are persistently underreported. DCTs do not effectively engage underserved populations in mental health research, meaning digital interventions are evaluated on unrepresentative samples. This risks perpetuating, and exacerbating, existing health inequalities, limiting the real-world impact of digital mental health solutions.

Health policy analysis frameworks play a central role in understanding how reforms are designed, implemented, and experienced in practice. Walt and Gilson's Health Policy Analysis Triangle has been widely used to examine the interaction between policy content, context, actors, and processes, particularly in settings where implementation is shaped by political and organizational dynamics. However, many contemporary health reforms are increasingly digitally mediated and culturally embedded, raising questions about whether existing frameworks sufficiently capture the conditions that structure implementation. This paper proposes a conceptual extension of the Health Policy Analysis Triangle by theorizing Technology and Culture as cross-cutting dimensions that shape how policy content is enacted through actors and processes within specific contexts. The extension is grounded in secondary analysis and theoretical interpretation of empirical patterns previously identified in an evaluation of Saudi Arabia's Health Sector Transformation Program, a large-scale reform initiative implemented under Vision 2030. Drawing on previously collected policy documents and qualitative interview data from the doctoral study [6] SHSTP evaluation, the paper illustrates how digital infrastructure and sociocultural norms operate as structuring influences on coordination, accountability, participation, and patient-centred care. The proposed framework does not replace the original triangle but enhances its analytical adequacy for reforms unfolding in digitally mediated and culturally complex systems. By making Technology and Culture explicit, the extended model provides a pragmatic analytical framework for analyzing implementation variation and reform learning in Saudi Arabia, with potential relevance for other health systems undergoing rapid transformation when adapted to local contexts.

The prevalence of type 2 diabetes (T2DM) is increasing rapidly in the UK and worldwide and is linked to adverse outcomes including premature death. Previous studies have shown that developing self-management skills in this population can lead to health improvements. The National Health Service in England has implemented Healthy Living (HL), an online Diabetes Self-Management Education and Support (DSMES) intervention, offering information about T2DM and help with adopting healthy behaviours. To examine the uptake and retention of people living with T2DM registered with Healthy Living and how its use is associated with changes in 1-year clinical outcomes (effectiveness) compared with people with T2DM who did not register (controls). Anonymised linked patient-level Healthy Living and National Diabetes Audit (NDA) data were used to identify adults with T2DM in England. Multivariable logistic regression models identified predictors of participation in Healthy Living (uptake). Using 1-to-5 case-control propensity score matching (on age, sex, baseline HbA1c, body mass index (BMI), blood pressure (BP), cholesterol, ethnicity, deprivation) and multivariable linear and logistic models, we examined how Healthy Living use was related to 1-year HbA1c, BMI, BP, new insulin use, and completion of eight care processes recommended for people with diabetes (effectiveness). Several sensitivity and sub-group analyses were conducted to assess the robustness of the findings. A total of 21,820 people with T2DM activated a Healthy Living account. Compared with non-participants, account activators (cases) were more likely to be female (OR: 1.91; 95%CI: 1.85, 1.96), less likely to be Asian (OR: 0.35; 95% CI: 0.33, 0.37) or Black (OR: 0.56; 95% CI: 0.52, 0.60) compared with white people. Assessing effectiveness, 4,940 Healthy Living cases were matched to 24,685 NDA controls. Compared with controls, at 1-year, Healthy Living cases had lower HbA1c (by -1.3 mmol/mol (95% CI: -1.7, -0.8) or -0.1% (95% CI: -0.2, -0.1)); BMI (-0.2 kg/m2; 95% CI: -0.3, -0.1), systolic BP (-1.2 mmHg; 95% CI: -1.6, -0.7), and diastolic BP (-0.6 mmHg; 95% CI: -0.9, -0.3); higher odds of completing care processes (OR: 1.6; 95% CI: 1.5, 1.8), but non-significant for insulin use (OR: 1.0; 95% CI: 0.8, 1.2). The results of the sensitivity and sub-group analyses were consistent with the main findings. People who activated a Healthy Living account experienced, on average, moderate health benefits compared with non-participants. These benefits would be expected to reduce risks for diabetes-related complications at a population level.

In low- and middle-income settings, routine health information systems (RHIS) and digital health projects often coexist, but their epidemiological outputs are rarely compared-even though integrating digital tools could strengthen RHIS by reducing reporting challenges. We conducted a retrospective, facility-level analysis of quarterly data (2017-2021) from Adamawa State, Nigeria, comparing ALMANACH, a clinical decision support system used in primary health care, with the state RHIS for malaria, pneumonia, gastrointestinal disorders, and measles in children under five years of age. The primary outcome was the facility-aggregated quarterly absolute relative difference (ARD) between the two reporting systems; temporal trends and facility-level heterogeneity were also assessed. Paired non-parametric tests, effect sizes with 95% confidence intervals (95% CI), and linear mixed-effects models accounted for clustering and repeated measurements. Across the study period, ALMANACH reported fewer cases than RHIS for malaria (116,018 vs 233,548; ARD 80.6%, 95% CI: 64.4-89.6, p&#x2009;<&#x2009;0.01), gastrointestinal disorders (43,003 vs 62,412; ARD 32.6%, 95% CI: 17.1-47.4, p&#x2009;<&#x2009;0.01), and pneumonia (20,980 vs 29,416; ARD 34.0%, 95% CI: 32.3-48.0, p&#x2009;<&#x2009;0.05), but more measles cases (4,355 vs 2,508; ARD 86.2%, 95% CI: 45.6-162.8, p&#x2009;<&#x2009;0.01). Mixed-effects models showed that RHIS recorded, per facility-quarter, on average 40.2 (95% CI: 31.1-49.3) more malaria, 6.6 (95% CI: 4.3-8.0) more gastrointestinal disorders, and 2.9 (95% CI: 1.2-4.6) more pneumonia cases than ALMANACH (all p&#x2009;<&#x2009;0.01), while ALMANACH reported 0.6 (95% CI: 0.07-1.2) more measles cases (p&#x2009;<&#x2009;0.05). Divergence widened as ALMANACH scaled up. Without a gold standard, these results quantify discrepancies without implying superiority and highlight the need for data integration, harmonized case definitions, and stronger data-use practices. They underscore the importance of sustained stakeholder engagement and user involvement for effective digital health scale-up in resource-limited settings.

Artificial Intelligence (AI) is rapidly transforming medical practice, with successful integration critically depending on healthcare professionals' self-reported perceived knowledge, attitudes, and perceived barriers to adoption. Despite rapid technological advances, comprehensive assessments of healthcare professionals' AI readiness remain limited, particularly regarding the relationship between enthusiasm and self-reported competency. We conducted a cross-sectional online survey of 148 healthcare professionals. The survey assessed demographics, AI knowledge/attitudes (12 Likert-scale items), institutional readiness, learning preferences, and perceived barriers. Inferential statistics including correlation analyses, Kruskal-Wallis H tests, and Mann-Whitney U tests were performed to examine relationships between attitudes and contextual factors. Participants (mean age 36.7&#x2009;&#xb1;&#x2009;8.1 years, 61.5% male, 77.7% from Germany) demonstrated a significant knowledge-enthusiasm gap. While 86.5% believed AI will transform medical practice and 76.4% expressed excitement about AI changes, only 20.3% felt well-informed about healthcare AI and 38.6% had medical AI experience. Correlation analysis revealed strong positive associations among enthusiasm measures (r&#x2009;=&#x2009;0.63-0.88, p&#x2009;<&#x2009;0.001) but weak correlations between knowledge and enthusiasm (r&#x2009;<&#x2009;0.20), providing evidence consistent with the knowledge-enthusiasm gap. Institutional AI stance significantly affected individual knowledge levels (Kruskal-Wallis H(3) = 28.11, p&#x2009;<&#x2009;0.001), but not enthusiasm. Primary barriers included knowledge deficits among leadership (62.8% institutional level), infrastructure limitations (52.0%), and system integration challenges (57.4%). Healthcare professionals, particularly in a German healthcare context, demonstrate strong enthusiasm for AI integration but face significant knowledge gaps and institutional barriers. While these findings might be most directly applicable to similar healthcare contexts, the identified knowledge-enthusiasm gap represents a critical target for educational interventions in similar well-resourced European healthcare systems. Successful AI implementation requires multi-level strategies addressing leadership education, infrastructure development, and hands-on training programs. These findings provide evidence-based guidance for healthcare institutions, educators, and policymakers developing AI adoption strategies.

Online surveys are potentially useful for sexual and reproductive health. However, there are many persistent problems related to online sexual and reproductive health surveys. This study aims to understand online recruitment, dissemination and implementation as part of the International Sexual Health And REproductive health (I-SHARE) consortium online sexual health survey. This study used a mixed-methods, cross-sectional, multi-country design. We used survey data from the I-SHARE study and organised a separate implementation survey completed by I-SHARE country leads. A total of 24,004 participants in 30 countries responded to the I-SHARE survey. All countries implemented the I-SHARE survey online and most (n&#x2009;=&#x2009;27, 90%) used convenience sampling. Social media promotion (n&#x2009;=&#x2009;27, 90%), and partner organisations sharing (n&#x2009;=&#x2009;21, 70%) were the most common recruitment methods. Twenty-nine countries responded to the implementation survey. We identified three themes related to online survey implementation: (1) Adaptation and flexibility highlighted research teams' responsiveness to rapidly changing contexts; (2) Better together: Partnerships illustrated the importance of multi-sectoral collaboration; and (3) Same but different: the heterogeneity of countries captured the ongoing tension between creating a standardised tool while honouring countries' unique socio-health climates and responses to the unfolding pandemic. This data demonstrates the potential for using online sexual health surveys in diverse settings. Our study suggests the need for greater consideration of bias related to communication, especially the digital divide, when designing and implementing online surveys.

Using a cross-country lens, we investigate the links between longitudinal work trajectories and health among parents with children under age 18. Employment serves as a valuable resource, affording us a decent standard of living. The rising dominance of digital and technology, together with the service economy since the 1980s, has transformed the utility of employment from a resource to a vulnerability, subjecting more families to uncertain, unstable, and insecure work. Nonstandard work schedules or shiftwork, which often fall outside regular 9-to-5 daytime hours and can be unpredictable, carry potential health consequences. Using the longitudinal data from Australia (HILDA), Germany (SOEP), the UK (UKHLS), and the US (NLSY79), we used sequence analysis to first chart parental work schedule patterns between three stages of the life course, 25-34, 35-44, and 45-54, to show the changes and transitions in work patterns. We then conducted multivariate regression analysis to examine how variations in parental work patterns may shape individual health (i.e., physical and mental health) at ages 35/40, 45/50, and 55/60 while controlling for a rich set of sociodemographic characteristics. Our sequence analyses uncovered roughly 4-6 work patterns during those three periods, revealing the heterogeneities of parental work trajectories that might correspond to childrearing demands and their sociodemographic backgrounds. We also found that mainly not-working pattern or volatile work arrangements (e.g., switching between daytime and non-daytime hours) were associated with significantly poorer physical and mental health; however, the persistence and magnitude of these associations varied by country. This study advances our understanding of the critical role of employment in our health from a cross-country perspective and bears important implications for the intergenerational transmission of employment and health vulnerabilities.

Air pollution is a major global health threat, with children and young people (CYP) among the most vulnerable. Delhi (India) and Dhaka (Bangladesh) are two of the world's most polluted cities, with persistently high levels of fine particulate matter (PM2.5). This study aimed to generate CYP-centered evidence on the real-time impacts of air pollution in these cities by comparing health, well-being, and daily activities during periods of high air pollution and good air quality, while also capturing CYP's ideas for air quality management. A cross-sectional, real-time digital survey was conducted in Delhi on January 9-10 and 15 March 2025, and in Dhaka on January 21-22 and March 13-16, 2025. For both cities, the January dates correspond to a period with high air pollution (PM2.5&#x2009;>&#x2009;55.5 &#xb5;g/m3) and the March dates to good air quality (PM2.5&#x2009;&#x2264;&#x2009;35.4 &#xb5;g/m3). Participants included CYP aged 13-29 years and parents of children under 18. Recruitment was carried out online. Data on health symptoms, well-being (general feelings and sleep quality), and daily activity disruptions were analyzed using descriptive statistics, chi-square tests, and regression models adjusted for demographics. Responses to open-ended questions were thematically coded. A total of 814 eligible responses were collected (Delhi&#x2009;=&#x2009;365; Dhaka&#x2009;=&#x2009;449). High-pollution days were associated with significantly higher reports of itchy eyes, respiratory difficulties, headaches, skin irritation or rash, diarrhoea or vomiting, low mood, anxiety or stress, and difficulty concentrating. These associations remained significant after adjusting for demographics. Disruptions to daily activities also increased, including reduced physical activity and greater odds of being late or missing school or work, meetings, social events, and healthcare, as well as a greater need for family assistance (adjusted odds ratios approximately 3.8 to 4.8). In Delhi, changes were more pronounced across most outcomes, particularly a sharper drop in physical activity. In Dhaka, the same pattern was observed, along with additional increases in sore throat, cough, food insecurity, and difficulty accessing clean water. Participants' suggestions clustered around five themes: cleaner environments, stronger communities, improved healthcare and education, pollution and technology solutions, and other ideas. High air pollution was linked to widespread impacts on health, well-being, and daily routines among CYP. Their proposed solutions offer insights for participatory and equitable approaches to urban air quality management.

Duchenne muscular dystrophy (DMD) is characterized by progressive decline in skeletal muscle function leading to loss of ambulation and premature cardiopulmonary failure. The ability to monitor declines in skeletal muscle function in a free-living setting would be advantageous. Prior studies have utilized accelerometer measures of movement quantity (e.g., counts per minute, fraction of activity time), but accelerometry research on measures of movement quality in DMD is limited. The aim of the study was to compare quality of movement between a healthy control cohort and individuals with DMD using accelerometry. Accelerometer data were obtained from one study visit for each healthy control (N&#x2009;=&#x2009;92; ActiGraph Link GT9X, GT3X-BT or a combination) and one to three study visits for each participant with DMD (N&#x2009;=&#x2009;100; Link GT9X). Measures included counts per minute, entropy, jerk, and movement frequency (mean and standard deviation). Median (IQR) of each measure was reported for each group, including healthy controls and both ambulatory and non-ambulatory DMD participants, and significant differences across each group were compared using Mann-Whitney U tests. Correlations were assessed between accelerometer measures of movement quantity and quality, and predictive change in DMD ambulatory status was assessed using longitudinal regression. Significant differences (P&#x2009;<&#x2009;0.01) were observed in all measures between healthy controls, ambulatory DMD, and non-ambulatory DMD participants. Most measures were lower in DMD participants, suggesting decreased movement. Movement frequency values were higher in DMD (Healthy Controls 3.19 [3.05-3.45], Ambulatory DMD 3.60 [3.43-3.89], Non-Ambulatory DMD 4.32 [4.04-4.52]), suggesting more disordered movements. Counts per minute correlated strongly with both jerk (r: 0.722, P&#x2009;<&#x2009;0.05) and mean frequency (r: -0.813, P&#x2009;<&#x2009;0.05). Matched to age, individuals with DMD produce progressively fewer and more disordered (lower quality) movement compared to healthy individuals. Significantly lower entropy and jerk may be explained by a progressive decline in the strength of movements produced by individuals with DMD.

Hypertension (HTN) is a major global health problem and a significant risk factor for cardiovascular disease. Mobile health (mHealth) applications offer an efficient, patient-centered approach to managing chronic conditions like HTN. Given the high prevalence of HTN in Iran, and a recognized lack of approved and scientifically-grounded mHealth applications, this study aimed to address this gap, particularly in Hormozgan Province. This study aimed to design and evaluate a HTN self-care application, named HOPE, to facilitate self-management and enable patients to access health services outside of clinical settings. The research was conducted in four steps: (1) determination of data elements and functional requirements based on a systematic review of guidelines and feedback from 25 cardiologists and 50 patients using a Likert scale questionnaire; (2) content design based on national and international clinical and educational standards; (3) application development using Visual Studio, ASP.NET framework with MVC architecture, and C#; and (4) usability assessment. The final evaluation involved 46 participants with HTN from the Hormoz Clinic, who used the application for one month, followed by an assessment using the Mobile Application Usability Questionnaire (MAUQ). The HOPE application was designed with nine main tabs and 52 sub-tabs, covering key areas such as demographic information, comprehensive education, nutrition tracking, BP recording, medication management, and a dialogue panel for communication with the doctor. The overall usability evaluation for the application yielded an average score of 4.32 (on a 5-point Likert scale), which was categorized as a "very good" level. The highest average score (4.37) was assigned to the "User Interface and Satisfaction" dimension. A significant relationship was determined between satisfaction with the user interface and the participants' level of education (P&#x2009;>&#x2009;0.05). The HOPE demonstrated very good usability across all evaluated dimensions-ease of use, interface quality, and usefulness. The strong usability performance suggests that the application is well-designed and has high potential to effectively enhance self-care practices and could be a valuable tool in digital health management programs for patients with HTN. Future research should explore the long-term impacts of using HOPE on clinical outcomes and patient adherence, as well as its integration into routine healthcare practice to optimize HTN management.

The modern world is largely built around the use of digital technologies, which are present in life. The effective use of such technologies requires appropriate competences. People with disabilities, like everyone else, require access to digital technologies to fully participate in modern life. However, considering user diversity and the accessibility of digital technologies, digital competence training can play a key role. Therefore, the aim of this article is to present the determinants of participation in digital skills training among people with disabilities in Poland. To identify these factors, a questionnaire-based study was conducted with a group of 449 people with different disabilities. Logistic regression analysis revealed that perceived availability of accessible training significantly affects participation. When training is seen as inaccessible, the likelihood of participation decreases significantly by more than 70%. These findings highlight the need to improve the accessibility of digital skills training to ensure equal opportunities for all.

Pharmacogenomics (PGx) can optimise cardiovascular therapy, yet routine integration in cardiology remains limited. In the United Arab Emirates, a hybrid public-private health system, the real-world PGx use is still emerging. However, there is limited understanding of how cardiologists perceive and navigate PGx implementation within such complex health system contexts. To examine cardiologists' perspectives on the feasibility, barriers, and facilitators of implementing PGx using the Consolidated Framework for Implementation Research (CFIR). A qualitative study using an abductive analytical approach was conducted through semi-structured interviews with 15 cardiologists from public and private institutions. Participants were recruited via purposive, convenience, and snowball sampling. Interviews were transcribed verbatim and thematically analysed in NVivo. The CFIR guided the analysis across intervention characteristics, outer setting, inner setting, individual characteristics, and process. Clinicians expressed strong conceptual support for PGx, especially in higher-risk scenarios, but reported limited hands-on exposure and confidence. Barriers included perceived test complexity, cost, and lack of reimbursement; insufficient laboratory capacity and EHR integration; unclear workflows and role ownership; and turnaround times misaligned with acute care. Outer-setting constraints (ambiguous policy signals and payer criteria) and inner-setting variability (resources, leadership engagement, and communication pathways) further limited uptake. Reported facilitators included multidisciplinary service models (with input from pharmacists and genetics), targeted case-based training, initial deployment in non-acute contexts, and the structured capture of results with EHR-embedded clinical decision support. PGx implementation in cardiology within the UAE is shaped by structural, organisational, and workforce-level gaps. Addressing these through targeted clinical guidance, improved training, stronger reimbursement mechanisms, enhanced laboratory capacity, and integrated digital decision support may enable more equitable and scalable adoption. These findings provide actionable insights for health systems seeking to operationalise PGx within diverse or hybrid healthcare contexts.

In the context of digital transformation, social media has become a key channel for the public to obtain health information, and bloggers play an important role in shaping public health behaviors. However, the internal mechanism of how PerceivedBloggers'&#xa0;Competence (PBC) and information characteristics jointly affect Information Adoption (IA) has not been fully revealed. This study integrates the Information Adoption Model (IAM) with Social Cognitive Theory (SCT) to examine the relationship between Perceived&#xa0;Bloggers'&#xa0;Competence and Information Adoption, and to explore how Perceived&#xa0;Bloggers'&#xa0;Competence moderates the impact of Information Quality (IQ) and Information Credibility (IC) on information adoption through Information Usefulness (IU). A total of 1219 participants (648 males and 571 females) were recruited using a cross-sectional online survey design. Data were collected through a structured questionnaire and analyzed using SPSS and AMOS. This study constructs a conceptual model with Information Usefulness as a mediator and perceived&#xa0;bloggers'&#xa0;competence as a moderating variable and employs path analysis along with moderating effect tests to verify the hypotheses. In the study, Information Quality and Information Credibility had significantly positive effects on Information Usefulness, which in turn strongly predicted Information Adoption, also Perceived&#xa0;Bloggers'&#xa0;Competence negatively moderates the relationship between information characteristics and adoption. This study challenges the assumption that&#xa0;bloggers'&#xa0;expertise necessarily promotes Information Adoption, also provides key insights for health communicators, showing that striking a balance between expertise and accessibility is essential for effective public health messaging on social media, exhibits an effect that can be called "competence discounting".

Brain structure plays a pivotal role in shaping neural dynamics. Current models lack the anatomical and functional resolution needed to integrate whole-brain structure and dynamics within a unified computational framework. Here, we introduce the FEDE (high FidElity Digital brain modEl) pipeline, generating anatomically accurate brain digital twins from imaging data. Combining advanced techniques of finite-element analysis and biophysical modeling, FEDE reconstructs multi-scale brain structure with high spatial resolution, while also replicating whole-brain neural activity. We demonstrated FEDE's application by creating the first brain digital twin of a toddler with autism spectrum disorder (ASD). Through parameter optimization, FEDE replicated experimental neural activity while reconstructing multi-scale structural features ranging from whole-brain connectivity to synaptic timescales. FEDE estimated possible patient-specific anomalies in synaptic transmission, consistent with ASD pathophysiology. Our pipeline represents a significant leap forward in brain modeling, paving the way for effective applications of digital twins in experimental and clinical settings.

Parental relationship dissolution is a disruptive event in children and adolescents' lives that can negatively affect mental health, psychosocial adjustment, and everyday well-being. Psychosocial interventions are typically evaluated using measures of mental health and psychosocial adjustment, but less is known about whether they also improve less obvious well-being indicators such as somatic complaints, sleep latency problems, and body size perceptions. This study examined the effects of SES NXT, a digital psychosocial intervention from the Samarbejde Efter Skilsmisse (SES; "Cooperation After Divorce") platform, on these outcomes among children and adolescents (ages 3-17; referred to collectively as youth) experiencing parental relationship dissolution. We conducted a randomized controlled, parallel-group, superiority trial comparing youth who received access to SES NXT with those in a waitlist control group. The sample included 467 families and 866 youth recruited across Denmark. Data were collected at baseline, 4 weeks, and 12 weeks after enrollment. Outcomes were assessed with single self- or parent-reported items on somatic complaints, sleep latency problems, and perceived body size, drawn from previous research. Multilevel regression models using generalized estimating equations tested group differences at the 12-week endpoint, adjusting for baseline scores and demographic covariates. At 12 weeks, youth in the waitlist control group had higher odds of reporting somatic complaints (OR = 4.18), sleep latency problems (OR = 3.99), and extreme body size perceptions (OR = 2.59) than youth in the intervention group. Differences in somatic complaints and sleep latency problems were evident at 4 weeks and remained through 12 weeks. These findings suggest that a digital psychosocial intervention may also affect less obvious self- or parent-reported well-being indicators among youth following parental relationship dissolution.

Timely forecasting of dengue hospitalizations is essential for public health preparedness but is frequently limited by delays in official reporting systems. While climatic variables are known to influence dengue transmission and can be obtained in near-real time, hospitalization data often become available only weeks after patient admission, reducing their value for early response. Digital information generated during clinical practice, such as physicians' search patterns, may provide a complementary and more timely signal of emerging disease activity. This study evaluates whether integrating climate data with real-time records of physicians' searches for dengue-related information improves short-term forecasts of dengue hospitalizations in Brazil under both ideal and realistic reporting conditions. Three complementary data sources were combined to generate forecasts across multiple geographic regions: weekly hospitalization counts, climatic indicators, and anonymized physician search records from a widely used clinical decision-support platform. Model performance was compared under two scenarios: one assuming immediate availability of hospitalization data and another incorporating typical reporting delays. When hospitalization data were timely, simpler model configurations - particularly those relying on hospitalization history alone or combined with climate - achieved the highest predictive accuracy, indicating that the temporal structure of the outcome itself carried substantial forecasting value. Under realistic reporting delays, however, models incorporating physicians' search behavior consistently outperformed all other approaches across most regions. In several regions, increases in physician search activity preceded or coincided with rises in hospital admissions, indicating early clinical engagement with dengue cases. These findings indicate that physician search behavior constitutes a valuable real-time indicator of dengue activity. Integrating digital clinical behavior with climate data enhances forecasting performance under real-world reporting constraints and may strengthen early-warning systems and public health decision-making for dengue and other climate-sensitive diseases.

Breast cancer incidence rates are rising due to early detection, improved screening methods and advances in treatment options. As mortality rates reduce, there is a growing population living longer with treatment side-effects who require assistance. Prevalent issues arising after breast cancer treatment include pain, fatigue, lymphoedema and arm and shoulder mobility issues. Evidence demonstrates that upper limb exercises help reduce these common side effects and improve day-to-day functioning and quality of life. However, access to physiotherapy and rehabilitation services remains limited due to resource and access constraints. Despite a growing interest in digital health resources, little is known about the effectiveness of using digital exercise interventions to help resolve this problem. A scoping review of available literature will be undertaken to explore what digital or online prehabilitation or rehabilitation interventions exist for people with breast cancer that incorporate an exercise component aimed at improving physical function or mobility. Peer-reviewed studies in English will be eligible for inclusion. A systematic search of Medline ALL, Embase, CINAHL, and Web of Science will be conducted and any relevant grey literature form trusted sources will also be included in our review. The Arksey and O'Malley framework will be applied together with updated guidance from other authors. To enhance rigour, the Joanna Briggs Institute methodology for scoping reviews will also be used to ensure accurate reporting. Articles will be screened by title and abstract against eligibility criteria before independent full text screening by two researchers. Arising conflicts will be resolved by consulting a third reviewer. To summarise the available evidence, data will be extracted using a tailored charting template and a descriptive narrative synthesis will follow. As this research involves the analysis of already published, peer-reviewed literature, ethical approval is not required. The results of this scoping review will be submitted for publication in a peer-reviewed journal. Any significant deviations from the original protocol will be transparently reported and justified.

Ocular microtremor (OMT) is an involuntary fixational eye movement linked to brainstem activity. OMT is thought to have a mean frequency range of 70-90 Hz in healthy adults. Previous research suggests OMT may be reduced in neurological diseases like Parkinson's Disease. Historically, OMT has been measured invasively in specialist laboratories using lengthy and expensive protocols. Developments now allow for OMT measurement quickly and non-invasively using hand-held technology (i.e., iTremor ONE). This pilot study aimed to examine the analytical and clinical validation of OMT measurement via the iTremor ONE in people with Parkinson's Disease (PwPD). 33 PwPD and 31 age matched healthy controls participated in this study. For analytical validation, 22 PwPD completed a test re-test reliability assessment of OMT measurement, assessed using interclass correlation coefficients (ICC). For clinical validation, OMT frequency in PwPD (n&#x2009;=&#x2009;33) was compared to controls. Correlations were explored with demographics and clinical scales. Additionally, 24 PwPD were tested 'OFF' (12hr withdrawal) and 'ON' their anti-Parkinson's (dopaminergic) medication to compare OMT response to a known intervention. The iTremor ONE demonstrated excellent test-retest reliability (ICC&#x2009;>&#x2009;0.9) for measuring OMT frequency in PwPD. Mean OMT frequency was significantly lower in PwPD (63.78&#x2009;&#xb1;&#x2009;4.82 Hz) compared to controls (69.44&#x2009;&#xb1;&#x2009;6.47 Hz, p&#x2009;<&#x2009;.001), with good discriminative ability (AUC 0.75-0.77). OMT frequency correlated with age in both groups and with specific motor features (speech, facial expression, gait) in PwPD. No significant differences in OMT frequency were observed between 'OFF' and 'ON' dopaminergic medication states. This is the first study to demonstrate that a non-invasive hand-held device can reliably measure OMT in PwPD and presents OMT analytical and clinical validation evidence. OMT frequency may provide a supporting measure for diagnosis or screening. Further research is required to understand the neural mechanisms underpinning OMT in PwPD and the role it could play in clinical practice.

Malaria is a potentially fatal illness caused by a parasite of the genus Plasmodium that humans get by being bitten by female Anopheles mosquitoes carrying the infection. The incidence of malaria worldwide is disproportionately high in the African continent. Automated systems and cognitive analysis of digitized images of blood smears were used to diagnose Plasmodium malaria. This method is implemented in the Aidos intelligent system, which is easily accessible online. For the study, the database included images of 191 blood smears of patients infected with malaria and 227 images of blood samples from healthy patients. The images were digitized using the method developed by Professor Lutsenko E.V. The images were digitized for 12 light spectra. Then, spectral analysis of the blood smear images was carried out only for 18 new patients, and the duration was 10 seconds. The average similarity value of Plasmodium malaria recognition in patients was achieved at 66.965%. No false positive decisions were obtained for digitalized blood smears from healthy patients. The automated system-cognitive analysis of digitized blood smears provides instant diagnostic support. It allows medical workers with limited knowledge in microscopy and artificial intelligence to perform diagnostics.