Background: Background: Delayed extubation after general anesthesia increases complications like longer hospital stays and higher mortality. Current risk assessments often rely on subjective judgment or simple tools, while machine learning offers potential for real-time evaluation, though #research is limited and typically uses single-algorithm models. Objective: The aim of this study were to identify the risk factors for delayed extubation after general anesthesia in the sample and to construct a risk prediction model for delayed extubation in this population. Methods: Methods: Data from 4779 #patients admitted to the PACU between September 2023 and May 2024 were used to develop prediction models for delayed extubation using K-Nearest Neighbor, Decision Tree, Extreme Gradient Boosting, Random Forest, LightGBM, and Artificial Neural Network. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, F1-score, and Brier score. Calibration performance was evaluated using calibration curves generated with 100-bin quantile calibration and Loess smoothing to provide bias-corrected and smoothed visual assessment. Additionally, the Hosmer–Lemeshow (H-L) goodness-of-fit test was performed to quantitatively evaluate calibration, with P-values >.05 indicating good calibration. Results: Results:Among the six models evaluated, the XGBoost model demonstrated the best performance, with an AUROC of 0.750 (95%CI:0.703-0.796), a sensitivity of 0.734 (95% CI: 0.635-0.827), and a specificity of 0.647 (95%CI: 0.623-0.673). The model calibration was acceptable, with a Brier score of 0.0505 and a non-significant Hosmer–Lemeshow goodness-of-fit test (χ²=7.38, p=.287), indicating good calibration. Shapley Additive Explanations (SHAP) were used to rank feature importance. Conclusions: Conclusion: These machine learning models enable early identification of delayed extubation risk, supporting personalized clinical decisions and optimizing PACU resource allocation. Clinical Trial: This study was approved by the Chinese Clinical Trial Registry (ChiCTR2400090247).Ethical approval was granted by the Shenshan #medical center,Memorial hospital of Sun Yat-sen university(approval number: 2024-SSKY-113-01).

Background: Primary insomnia (PI), commonly identified by difficulties in initiating and/or maintaining sleep, negatively impacts both physical and mental health and increases the risk of occupational and vehicular accidents. Previous #Researches have indicated that shallow needling, a form of acupuncture, may ameliorate symptoms of primary insomnia. Nevertheless, the scientific evidence regarding its efficacy in enhancing sleep quality remains limited. Objective: This trial aims to assess the therapeutic efficacy of shallow needling in the treatment of chronic primary insomnia in adults. Methods: A single-center, prospective, assessor-blinded, and randomized controlled clinical trial with two parallel arms will be conducted in the Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine. A total of 124 participants with primary insomnia will be randomly divided into control group and treatment group in a ratio of 1:1 (n = 62 for each group). The control group will take eszopiclone orally at 1 mg once a day for 4 weeks. In addition to taking eszopiclone, the treatment group will receive shallow needling therapy once daily, five times a week, for 4 weeks. Data will be collected at three timeslots (pre, post, 4-weeks follow-up) and analyzed using SPSS 23.0. The primary outcome measure is Pittsburgh Sleep Quality Index (PSQI), Secondary outcome measures are Hamilton anxiety scale (HAMA), Insomnia Severity Index (ISI), Serum neurotransmitter detection (including the content of dopamine [DA], norepinephrine [NE], and melatonin [MT]), sleep parameters, and Magnetic Resonance Spectroscopy (MRS) of the thalamus. Results: This #Study has received formal ethical approval (Approval No: IRB 2023-014-02), the first participant was enrolled in August 2023, marking the official commencement of the trial. Participant recruitment for this #Study is currently in progress. Following the completion of recruitment, data processing and statistical analysis will be initiated. The final results of the #Study are expected to be prepared and submitted for publication in January 2026. Conclusions: This #Study will evaluate the therapeutic effectiveness and safety of shallow needling in the treatment of chronic insomnia, so as to provide the necessary scientific basis for the clinical application and promotion of shallow needling. The findings of this #Study may provide a scientific and standardized treatment #Protocol for shallow needling in adults with chronic insomnia. Clinical Trial: International Traditional Medicine Clinical Trial Registry ITMCTR2024000409; https://itmctr.ccebtcm.org.cn

Background: Cardiac surgeries in Chile lack a national registry for systematic data collection and analysis, limiting insights into procedural outcomes and patient demographics. In response to this gap, we developed a web-based platform to support the documentation of high-complexity cardiac surgeries. Objective: Design, develop, and implement a cardiac surgery data collection and analysis platform that conforms to international standards to support clinical decision-making and research initiatives. Methods: A web-based platform was developed using the Model-View-Controller (MVC) architecture, incorporating input from healthcare professionals and based on the fourth European Association for #cardio-Thoracic Surgery (EACTS) adult cardiac surgical database report. The platform captures over 160 clinical variables across 15 categories, spanning preoperative, intraoperative, and postoperative stages. Results: The most significant outcome of this study is the development of the first online platform for documenting cardiac surgeries in Chile. Since its implementation in 2014, the platform has documented over 4,800 cardiac surgeries, establishing it as the largest database for a single institution in Latin America. The platform offers real-time access to data, supports planning and resource allocation, and enables the systematic evaluation of clinical outcomes. Integrating the EuroSCORE II risk model enables a standardized assessment of mortality risk. Conclusions: The platform contributes to the collection of cardiac surgery data in Chile, enabling evidence-based clinical decision-making and informed public health planning. It has documented cardiac surgeries for 10 years and has become the official registry tool for cardiac surgeries. By 2026, its application will be extended to two more centers, with the expectation that it will soon become the national database of cardiac surgeries. Future developments should improve scalability, interoperability, and data analysis to establish a national registry and further align Chilean cardiac surgery practices with international standards.

Background: Military veterans may be at increased risk of post-traumatic stress disorder (PTSD) compared to the general population. PTSD is often comorbid with harmful and problematic patterns of gambling. Behavioural therapies such as Acceptance and Commitment Therapy (ACT) have shown promise in treating these cooccurring disorders, especially if combined with #Mobile #Health (#mHealth) interventions to circumvent known help-seeking barriers faced by veterans. To date, however, recruitment for #mHealth interventions has been challenging and may impact intervention feasibility. Objective: Here, our objectives were to describe the strategies used to recruit UK military veterans with PTSD and/or experience of harmful gambling to a pilot study of a smartphone-based #Digital intervention, “ACT Vet”. Methods: We employed several recruitment strategies such as direct mailing, paid study advertising on social media (Facebook) and an online research platform (Prolific), study-specific website management, in-person event hosting with veterans’ charities, snowball sampling and incentives for completion. Results: Results showed that over 27 days, recruitment through Facebook accounted for 21 eligible veterans (seven unpaid, 14 paid advertising), while Prolific accounted for 50. Additional strategies recruited eight eligible veterans. In total, 79 eligible military veterans were recruited for ACT Vet, with 25 completing the pilot study. Conclusions: Difficulties such as low advertisement conversion rate, participant and data attrition occurred throughout the present study. Our findings illustrate the relative effectiveness of social media- and online platform-based initiatives in recruiting veterans with PTSD and/or harmful gambling. Future research should consider establishing an online presence for effective #Digital intervention recruitment with diverse branding to attract representative samples of veterans for #mHealth research.

We demonstrate the feasibility of a wearable, augmented reality–based nystagmus examination system, showing its preliminary diagnostic agreement with conventional video-oculography and its potential for portable vestibular assessment in patients with vertigo.

Background: Large language models (LLMs) such as ChatGPT offer significant opportunities for medical education. However, empirical data on actual usage patterns, perceived benefits, and limitations among medical students remain limited. Objective: This study aimed to assess how medical students in Germany use ChatGPT, their perceptions of its educational value, and the challenges and concerns associated with its use. Methods: A cross-sectional 17-item online survey was conducted between May and August 2024 among medical students from Philipps University Marburg, Germany. A mixed-methods approach was applied, combining descriptive and inferential statistical analysis with qualitative content analysis of open-ended responses. Results: A total of 84 fully completed surveys were included in the analysis (response rate: 26.7%). 76.2% (64/84) of the participants reported having used ChatGPT for medical education, with significantly higher usage during exam periods (p = .003). Preclinical students reported higher overall usage than clinical students (p = .020). ChatGPT was primarily used for summarizing information by 60.7% (51/84) of students, for literature research by 57.7% (49/84), and for clarifying concepts by 47.1% (40/84). A total of 70.2% (59/84) felt that it helped them save time, and 51.2% (43/84) reported an improved understanding of content. In contrast, only 31.0% (26/84) saw benefits for applying knowledge, and 15.5 % (13/84) for long-term knowledge retention. Free-text responses also revealed that students see further potential for ChatGPT in clinical documentation and exam preparation. However, 73.3% (55/75) expressed concerns about misinformation, and 72.6% (61/84) reported lacking confidence in their AI-related skills. Only 41.7% (35/84) stated that they trust ChatGPT’s outputs. Students who used the tool more frequently also reported higher levels of trust (r = 0.374, p < .001). Over 70% of respondents indicated a strong desire for increased integration of AI-related education and practical applications within the medical curriculum. Conclusions: ChatGPT is already widely used among medical students, especially in exam preparation and the early stages of training. Students value its efficiency and support for understanding complex material, but its long-term influence on learning is limited. Concerns about reliability, source transparency, and data privacy remain, and AI skills play a key role in shaping usage. These findings underscore the need to integrate structured, practice-oriented AI education into medical training to support critical, informed, and ethical use of LLMs.

Background: Gastric #Cancer is a serious health issue both globally and in Kazakhstan. Worldwide, gastric #Cancer ranks fifth in incidence, with the highest rates reported in East Asia, the Andes of South America, and Eastern Europe, while the lowest rates are reported in North America, Northern Europe, Africa, and Southeast Asia. Over 70% of the cases occur in resource-limited countries. In Kazakhstan, gastric #Cancer ranks third in morbidity and second in mortality, representing a serious clinical and social problem. Annually, 1600 deaths from malignant gastric neoplasms are registered. Peritoneal metastasis is considered one of the most severe complications of gastric #Cancer and has long been regarded as its terminal stage. The average life expectancy of patients with peritoneal metastasis is only 3-6 months, reflecting a high mortality rate and the limited efficacy of current treatments. Objective: We hypothesize that the prophylactic use of pressurized intraperitoneal aerosol chemotherapy (PIPAC) may reduce the incidence of peritoneal metastasis. The aim of this #Study is to evaluate the incidence of peritoneal metastasis following prophylactic PIPAC in patients with gastric #Cancer. The primary goal of this #Research is to evaluate whether adding prophylactic PIPAC to standard treatment before neoadjuvant chemotherapy can reduce the incidence of peritoneal metastasis in patients with locally advanced gastric #Cancer. Methods: This #Study is a single-center non#RCT #ClinicalTrial with a planned enrollment of 160 patients. All participants will be included in one of the 2 groups: intervention group (PIPAC + perioperative chemotherapy + gastrectomy with D2 lymphadenectomy) or control group (perioperative chemotherapy + gastrectomy with D2 lymphadenectomy). The primary end point is the incidence of peritoneal metastasis, and the secondary end points are overall survival, recurrence-free survival, treatment-related adverse events, and personal satisfaction. Results: As of September 2025, this #Study is funded and recruiting; 102 participants have already been enrolled. Recruitment is planned to be performed between January 2025 and December 2026. No interim analyses have been performed; primary results are planned for Q1 2027. The manuscript is expected to be published by Q4 2027. Conclusions: Compared to standard chemotherapy, PIPAC has been reported to significantly improve survival rates in patients with peritoneal metastasis of gastric origin. We suggest that the neoadjuvant use of PIPAC may reduce the incidence of peritoneal metastasis and improve long-term survival outcomes. The results of our #Study will provide key information on the practicality and viability of PIPAC as a prophylactic technique for preventing the progression of gastric #Cancer. Trial Registration: ClinicalTrials.gov NCT06784765; https://clinicaltrials.gov/#Study/NCT06784765

Background: Cardiovascular diseases remain the leading cause of mortality worldwide, accounting for 18 million deaths annually. Detection and prediction of cardiovascular conditions are essential for timely intervention and improved patient outcomes. #Wearable devices offer a promising, noninvasive solution for continuous monitoring of cardiovascular signals, vital signs, and #PhysicalActivity. However, the large data volumes generated by these devices and the rapid fluctuations in cardiovascular signals necessitate advanced artificial intelligence (#AI) (AI) techniques for real-time analysis and effective clinical decision-making. Objective: The objective of this scoping review was to identify the main challenges of AI-driven platforms for real-time cardiovascular condition monitoring with #Wearable devices and explore potential solutions. In addition, this review aimed to examine how AI algorithms are developed for robust monitoring and how deployment pipelines are optimized to enable real-time cardiovascular condition monitoring. Methods: A comprehensive search was conducted in the following electronic databases: MEDLINE(R) ALL (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (Ovid), Web of Science Core Collection (Clarivate), IEEE Xplore, and ACM #Digital Library, yielding 2385 unique records. Inclusion criteria focused on studies that used #Wearable devices for participant data collection and applied AI algorithms for real-time analysis to detect or predict cardiovascular events and diseases. After title and abstract screening, 153 papers remained, and following a full-text review, 19 studies met the inclusion criteria. Results: The findings indicate that despite the promise of AI and #Wearable devices, research on real-time cardiovascular monitoring remains limited and lacks comprehensive validation. Most studies relied on publicly available #Wearable datasets rather than real-world validation with recruited participants in community settings. Studies that deployed AI algorithms in real time frequently failed to report operational characteristics and challenges. Electrocardiography-based #Wearable sensors were the most frequently used devices, primarily in hospital settings. A variety of AI techniques, ranging from traditional machine learning to lightweight deep learning algorithms, were deployed either on #Wearable devices or via cloud-based processing. Conclusions: Robust, interdisciplinary research is needed to harness the full potential of AI-driven, real-time cardiovascular #Health management using #Wearable devices. This includes the development and validation of scalable solutions for continuous community-based deployment. Furthermore, real-world challenges such as participant compliance, hardware and connectivity constraints, and AI model optimization for real-time continuous monitoring must be carefully addressed.

Background: Underserved pregnant women have a greater risk of excessive or inadequate gestational weight gain (GWG) and adverse perinatal outcomes. In the United States, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) provides supplemental nutrition and is uniquely positioned to deliver equitable interventions that support recommended GWG. Yet to date, no randomized controlled trials have evaluated behavioral strategies for managing GWG in this setting. Objective: The primary objective was to examine the effects of a statewide randomized multicomponent mobile eHealth lifestyle intervention trial on change in physical activity and sedentary time across pregnancy. The secondary objective was to explore associations between changes in physical activity, sedentary time, and GWG. Methods: A total of 351 pregnant women were recruited from the Louisiana WIC clinics and were randomly assigned to a multicomponent mobile eHealth intervention for GWG management (N=179) or usual care (N=172; standard in-person WIC care) prior to 16 weeks of gestation. The multicomponent mobile intervention included daily weighing, step tracking, counseling, exercise videos, health coach interactions, and social support. For the first objective, physical activity, including movement duration and movement context, and sedentary time were assessed at baseline (early pregnancy) and at the end of the intervention (late pregnancy) using accelerometry and the Pregnancy Physical Activity Questionnaire. For the second objective, GWG was determined based on weight collected at study visits in early and late pregnancy. Linear mixed models assessed intervention effects on physical activity and GWG. Results: Both the Intervention Group and the Usual Care Group significantly increased sedentary time from early to late pregnancy (adjusted effect estimate [95% CI] 62 minutes per day (42-83), P

Background: #Transgender, nonbinary, and gender diverse (TGD) young adults face a disproportionately high mental health burden, including 2- to 4-fold increased risk of suicidality and depressive distress compared to their cisgender peers. Family and community support are protective factors that may mitigate these adverse outcomes, representing key targets for mental health interventions. Objective: This #RCT #ClinicalTrial (RCT) evaluates the effectiveness of TransHealthGUIDE, a novel #Digital, #App-based education and support intervention aimed at reducing past 3-month suicidal ideation and past 2-week depressive distress among TGD young adults. Participants are randomized 1:1 to an immediate intervention arm or a 6-month deferred intervention arm (waitlist control). Methods: From 2021 to 2024, a multidisciplinary team of academic and community partners conducted formative #Research and codeveloped a #Digital #App containing 33 self-paced modules, communication, and interactive resources. The content is grounded in the Social Ecological Suicide Prevention Model and the Gender Minority Stress and Resilience Framework, designed to enhance family support and community connection for TGD young adults and their families. TGD young adults (ages 18-24 years) and their caregivers are prospectively enrolled and randomized in 10 US states (California, Connecticut, Illinois, Maryland, Massachusetts, New Jersey, New York, Oregon, Vermont, and Washington, DC). Participants complete self-reported surveys at baseline, 6 months, and 12 months, assessing sociodemographics, family support, community connection, suicidal ideation, depressive distress, and intervention acceptability. #App usage and engagement are tracked via metadata. The target sample size is 500 TGD young adults, with 50% (n=250) identifying as people of color and 125 TGD young adult-caregiver dyads. Results: TGD young adult and caregiver communities, including our community advisory board (CAB), have guided all aspects of the project: #Study design (eg, waitlist control), survey development (eg, self-reported rather than clinician-administered suicidality measures and inclusion of social justice activism as a form of support), intervention content (eg, text, infographics, interviews with TGD people and caregivers, and simulations), and #Digital #App safety features (eg, a quick-exit button for privacy). Recruitment and #Study screening began on January 21, 2025. As of May 2, 2025, a total of 149 individuals completed a #Study screener, with 132 (88.6%) of 149 found eligible. Enrollment visits began on April 23, 2025. As of May 2, 2025, five TGD young adults have been enrolled. Ongoing engagement with the CAB will inform iterative improvements throughout the trial. Conclusions: Scalable, #App-based interventions tailored to meet the needs of TGD young people hold promise for population-level mental health impact. This RCT—targeting suicidality and depressive distress via education and support for both TGD young adults and their caregivers—addresses a critical gap in mental health interventions for this underserved population. Trial Registration: ClinicalTrials.gov NCT06177600; https://clinicaltrials.gov/#Study/NCT06177600

Background: The impact of air pollution on early childhood development in low-pollution settings is well known. However, comprehensive evidence from high-pollution environments such as the Delhi National Capital Region in India remains limited. Objective: This maternal-child cohort #Study aims to investigate the impact of air pollution on pregnancy outcomes and early childhood development, providing critical insights to inform targeted interventions and public health policy. Methods: This longitudinal maternal-child cohort #Study will enroll 2500 pregnant women from rural and urban Delhi National Capital Region and follow their children up to 2 years of age. Maternal data on pregnancy complications, delivery mode, parity, and gestational weight gain will be collected from clinical records and structured questionnaires, while newborn outcomes (gestational age, birth weight, anthropometry, and congenital anomalies) will be assessed from birth records and clinical examination. Early childhood development will be evaluated through standardized anthropometry and the Developmental Assessment Scale for Indian Infants. For a 10% subsample, trimester-specific and postnatal indoor and outdoor air pollution exposure (particulate matter with a diameter of less than 2.5 micrometers and particulate matter with a diameter of less than 10 micrometers) will be monitored using portable air quality monitors, along with household surveys and time-activity diaries. Maternal and infant blood samples will be analyzed for inflammatory, oxidative stress, and cardiovascular biomarkers. Exposure estimates will be integrated into personal exposure models, and associations with health outcomes will be examined using multivariable regression and longitudinal mixed-effects models. Results: As of July 2025, 45% of the planned sample size have been recruited, with baseline data collection completed, and 10% undergoing exposure assessment and sample collection. Conclusions: This #Study will provide a comprehensive evaluation of the effects of air pollution on maternal health, pregnancy outcomes, and early childhood development in urban and rural settings in India. This will generate context-specific evidence to support maternal and child health policies, air pollution mitigation strategies, and personal protection measures for pregnant women and infants in polluted environments.

Date Submitted: Aug 5, 2025. Open Peer Review Period: Nov 11, 2025 - Jan 11, 2026.

Date Submitted: Oct 21, 2025. Open Peer Review Period: Nov 10, 2025 - Jan 5, 2026.

Background: Technology use is increasingly integrated into daily life, including among older adults, whose adoption and engagement with technology warrant closer examination. According to the matching person and technology model, technology adoption is more likely when a person’s preferences and needs align with a device’s functions and features, as well as the environment in which it is used. However, factors such as age-related changes, evolving preferences, and the rapid pace of digital transformation complicate this process. Additionally, older adults often rely on support from family members and health professionals, yet their perspectives remain largely unexplored. Objective: This study aimed to examine the daily technology use of older adults in Israel from a triangle perspective, incorporating the views of older adults, family members, and health professionals. It explored preferred technology-based activities, device features, the shift from in-person to technology-based interactions, and responses to mismatches between preferences, device characteristics, and social support. Methods: Nine web-based focus groups, each with 6 to 7 participants, were conducted during the COVID-19 pandemic (February 2021 to July 2022). Participants included 20 healthy, independent older adults (10 women, aged 66-80 years), 19 family members (children and grandchildren, aged 21-55 years), and 20 health professionals with at least 5 years of experience working with older adults. Results: Older adults demonstrated diverse preferences for technology use in daily activities, shaped by urgency, significance, and device characteristics. This perspective was reinforced by family members and health professionals, who highlighted the importance of distinguishing between technology types based on their users and features. Additionally, older adults expressed mixed views on shifting from in-person interactions to digital alternatives, while family members and health professionals emphasized the importance of social interaction for well-being. Finally, older adults described significant emotional challenges when navigating technology. Family members and health professionals identified key contributors, including the gap between perceived and actual technological abilities, generational differences in digital exposure, and cognitive demands associated with technology use. Conclusions: This study highlights the significant variability in older adults’ daily activity preferences, which strongly influences their technology use. It suggests shifting the focus from technology to its practical application in meeting individual needs. In this context, it is important to consider the need for social interactions. Addressing social interaction needs and emotional challenges is crucial, as unmet technological needs can lead to frustration and disengagement. These insights can inform strategies to enhance technology use among older adults by aligning technology design and support systems with their preferences and needs.

Background: Self-reported dietary intake data are crucial in nutrition and health #Research; however, their accuracy is compromised by challenges such as portion size estimations, food identification, memory-related bias, social desirability bias, and reactivity bias. Dietary assessment in adolescents is particularly challenging due to irregular eating habits, meal skipping, and parent or peer influences, potentially resulting in misreporting. Leveraging adolescents’ receptiveness to technology, we investigated the use of an innovative #Smartphone #mHealth #App (Traqq) that facilitates dietary assessment using repeated short recalls instead of traditional 24-hour recalls. Evaluation studies of the Traqq #App in Dutch adults have shown successful results, but its suitability for other target populations, such as adolescents, requires further investigation. Objective: We designed a comprehensive, 3-phase #Study to evaluate the Traqq #App’s accuracy using repeated short recalls, usability, and user perspectives among Dutch adolescents aged 12 to 18 years. This manuscript details the #Study setup, #Research methods, and basic characteristics in phases 1 and 2. Methods: In phase 1, adolescents (aged 12-18 years) downloaded the Traqq #App and completed a demographic questionnaire. It was used on 4 random school days over 4 weeks, using 2-hour recalls on 2 days and 4-hour recalls on 2 days. A food frequency questionnaire and 2 interviewer-administered 24-hour recalls served as dietary reference methods to assess the Traqq #App’s accuracy. In addition, usability was evaluated using the System Usability Scale and an experience questionnaire. In phase 2, user experiences were further explored through semistructured interviews within a subsample of 24 adolescents. These first 2 phases of this mixed methods #Study are now finalized for data collection. Phase 3 will focus on collecting user insights to inform #App customization through cocreation sessions. Results: Recruitment concluded in September 2022 with 102 adolescents; 98 (96%) provided dietary data via the Traqq #App, and 79 (78%) completed the evaluation questionnaire. Adolescents had a mean age of 15 (SD 2) years. The mean BMI was 19.9 (SD 3) kg/m2. A total of 64 (63%) participants were girls, 81 (84%) attended high school, and 88 (92%) were born in the Netherlands. Interviews were held with 6 (25%) boys and 18 (75%) girls. Cocreation sessions will be planned after all data have been analyzed. Conclusions: In this holistic #Study, we combine quantitative and qualitative methods to evaluate the dietary assessment performance among adolescents of the Traqq #App, which was initially designed for adults. Specifically, next to quantitative comparisons of the Traqq #App’s dietary assessment methods, we conducted semistructured interviews, and we will carry out cocreation sessions. With this user-centered, synergistic approach, we aim to establish a list of requirements for a dietary assessment #App for adolescents, resulting in more efficient assessments, improved compliance, and enhanced overall accuracy in this population. Clinical Trial: ISRCTN Registry ISRCTN46230386; https://www.isrctn.com/ISRCTN46230386

Background: The use of smartphones and interest in mobile health (mHealth) has grown in recent years with physical activity apps demonstrating potential to facilitate behaviour change. However, there remains limited understanding of what specifically motivates children to engage meaningfully with these tools. Objective: This qualitative formative study aimed to determine children's perceptions of a bespoke physical activity mhealth app (Bestlife). It sought to explore the app’s appeal, functionality, and potential to support behaviour change among children aged 8–13. Methods: A total of 68 Young Citizen Scientists (YCSs) aged 8–13 years were recruited from five schools (three primary, two secondary) in Bradford, UK, through purposive sampling as part of a whole-system physical activity programme (JU:MP). Recruitment procedures were school-led, incorporating consented whole-class involvement at primary level and teacher-nominated groups at secondary level. YCSs were asked to download and explore the Bestlife app 1- 2 weeks before the school based research session, completing a booklet to capture their experiences and those of their families. Thirteen focus groups were conducted across five schools to explore children's views in depth. The focus groups were designed to investigate children's perceptions of the app. Qualitative data were analysed inductively and deductively: An initial inductive analysis identified emerging themes, which were then mapped onto a framework of #feasibility, #usability, acceptability, and behaviour change. Results: Sixty-eight children (60 from primary schools and 8 from secondary schools) participated in the study [See Table 1]. The study identified key factors influencing the #feasibility, acceptability, #usability, and behaviour change potential of the Bestlife app among children. #feasibility was hindered by the parental email requirement during registration, which limited autonomy for older children. Acceptability was driven by gamified features, proportional rewards, and avatar customisation, though participants requested more personalisation to promote cultural inclusion and dynamic updates, linked to seasonal themes. #usability findings showed the interface was intuitive, with features promoting social interaction and competition enhancing engagement. However, younger users experienced navigational challenges, underscoring the need for clearer guidance. The app effectively incorporated behaviour change techniques, including goal-setting, self-monitoring, and social collaboration, but required adjustments, such as reducing the frequency of emotional tracking prompts. Conclusions: The Bestlife app shows potential as an mHealth intervention for promoting physical activity in children. Enhancing cultural representation, simplifying onboarding processes, and refining engagement strategies could strengthen both uptake and sustained use. These findings highlight the importance of integrating user feedback into the iterative design process to optimise digital health tools for young populations. Further longitudinal research is recommended to evaluate longer term engagement with the app's it’s impact on physical activity levels, and behaviour change sustainability.

Background: Substance use is a high-impact biopsychosocial problem in Chile, where 21% of adults have experienced a severe alcohol use episode. In the past year, the prevalence rates were 12.5% for marijuana, 0.8% for cocaine, and 0.4% for cocaine base paste. Cannabis prevalence in Chile is higher than the global (4.2%) and South American (3.58%) averages. Cocaine prevalence in Chile is lower than in South America (1.62%) but higher than the global average (0.42%). No international reports are available for cocaine base paste. Mental health and substance use programs in Chilean primary care involve psychologists and social workers. Solution-focused brief interventions (SFBIs) are based on solution-focused brief therapy, a strengths-based and person-centered approach in which practitioners adopt a stance of “not being the expert,” respecting clients’ needs and perspectives. Objective: This #Study aims to determine whether the SFBI implemented by psychosocial teams (psychologists and social workers) for individuals with alcohol and other drug use in primary health care centers leads to better outcomes than usual care. Methods: We will conduct a randomized controlled clinical trial (ClinicalTrials.gov registration pending) comparing a 3-session SFBI (experimental group) with a single session of brief counselling as usual care (control group) in primary care. Interventions will be delivered in person by a psychologist or social worker. A total of 320 participants are expected to be recruited during preventive routine checkups using the Alcohol, Smoking, and Substance Involvement Screening Test. Participants reporting intermediate- to high-risk substance use on this screening tool will be randomly assigned to each group. #Research assistants will administer instruments at baseline and at 3-, 6-, and 9-month follow-ups and will be blinded to the assigned treatments. The primary outcome assessed will be substance use patterns, while secondary outcomes include background information, depressive symptoms, anxiety symptoms, and motivation for treatment. Statistical analyses, including t tests, ANOVA, and Fisher exact tests will be conducted depending on variable type and normality. A qualitative component to assess acceptability and pertinence will include focus groups with participants and practitioners, followed by a content analysis. Results: Funding for this #Study started in April 2024. As of the submission date of this #Protocol, 55 practitioners from 9 primary health care centers have been trained in SFBIs. Recruitment began in February 2025, with 73 participants enrolled and 23 who dropped out. Recruitment will continue until December 2026. No analyses have been conducted to date. Findings are expected to be published during the second half of 2028. Conclusions: This #Study strengthens primary care by integrating targeted psychosocial interventions for substance use into existing programs, thereby enhancing real-world applicability. If effective, the intervention could be adopted into routine care and inform public policy on mental health and substance use. Trial Registration:

Date Submitted: Oct 30, 2025. Open Peer Review Period: Nov 11, 2025 - Jan 6, 2026.

Background: Virtual healthcare models that incorporate registered nurse (RN) triage with rapid access to same-day virtual visits with clinicians represent a growing innovation in healthcare delivery. While traditional telephone advice lines focus primarily on RN-led triage and care navigation, systems such as the Department of Veterans Affairs (VA) are beginning to embed physicians and advanced practice providers directly into these platforms. This hybrid model has potential to enhance clinical responsiveness, reduce unnecessary emergency department (ED) and urgent care visits, and increase patient satisfaction by providing timely care from home. Objective: The aim of this study was to explore Veterans’ experiences and perceptions of the VA’s integrated virtual triage and urgent care model, specifically through the VA Health Connect platform. We sought to understand how Veterans learned about and interacted with these services and to gather their insights on aspects to preserve or improve. Methods: We conducted in-depth qualitative interviews with 24 Veterans from various geographical regions served by six VA healthcare systems. Participants were recruited from recent respondents to the V-Signals survey, who indicated willingness to discuss their experiences further. Interviews were carried out between June 18 and August 8, 2024. Data were analyzed using a qualitative descriptive approach with constant comparison to identify emergent themes and representative quotes. Results: Participants reported high satisfaction with VA Health Connect’s nurse triage and virtual clinical visit (VCV) services. Key benefits included timeliness of care, personal time savings, efficient service organization, and positive interactions with nurses and providers. Veterans appreciated the convenience of resolving health issues quickly and remotely, often citing significant travel burdens avoided. They also highlighted the knowledgeable and personalized clinical advice received. However, several areas for improvement were identified. Some Veterans expressed frustration about being routed to nurse triage instead of directly scheduling with their primary care providers (PCP). Moreover, many were initially unaware of the full range of services available through VA Health Connect and suggested enhanced outreach and communication strategies. Conclusions: Veterans are highly satisfied with the VA Health Connect model, valuing its timeliness, convenience, and the professionalism of clinical staff. Effective promotion and clear communication about the capabilities and limitations of the service could further enhance user experience and uptake. As this integrated care model continues to evolve, its success will depend on effectively integrating virtual services into routine care and ensuring Veterans are well-informed and confident in using these resources. Clinical Trial: N/A

Date Submitted: Nov 4, 2025. Open Peer Review Period: Nov 11, 2025 - Jan 6, 2026.

Date Submitted: Nov 10, 2025. Open Peer Review Period: Nov 11, 2025 - Jan 6, 2026.

Date Submitted: Oct 31, 2025. Open Peer Review Period: Nov 11, 2025 - Jan 6, 2026.

Date Submitted: Nov 10, 2025. Open Peer Review Period: Nov 11, 2025 - Jan 6, 2026.

JOURNAL OF MEDICAL INTERNET RESEARCH. Zeba et al https://www.jmir.org/2025/1/e87042. J Med Internet Res2025 | vol. 27 | e87042 | p. 1. (page number ...