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There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

BackgroundDigital technologies connect young people with health services and resources that support their self-care. The lack of accessible, reliable digital resources tailored to young people with persistent musculoskeletal pain is a significant gap in the health services in Australia. Recognizing the intense resourcing required to develop and implement effective electronic health (eHealth) interventions, the adaptation of extant, proven digital technologies may improve access to pain care with cost and time efficiencies. ObjectiveThis study aimed to test the acceptability and need for adaptation of extant digital technologies, the painHEALTH website and the iCanCope with Pain app, for use by young Australians with musculoskeletal pain. MethodsA 3-phased, mixed methods evaluation was undertaken from May 2019 to August 2019 in Australia. Young people aged 15 to 25 years with musculoskeletal pain for >3 months were recruited. Phases were sequential: (1) phase 1, participant testing (3 groups, each of n=5) of co-designed website prototypes compared with a control website (painHEALTH), with user tasks mapped to eHealth quality and engagement criteria; (2) phase 2, participants’ week-long use of the iCanCope with Pain app with engagement data captured using a real-time analytic platform (daily check-ins for pain, interference, sleep, mood, physical activity, and energy levels; goal setting; and accessing resources); and (3) phase 3, semistructured interviews were conducted to gain insights into participants’ experiences of using these digital technologies. ResultsFifteen young people (12/15, 80% female; mean age 20.5 [SD 3.3] years; range 15-25 years) participated in all 3 phases. The phase 1 aggregated group data informed the recommendations used to guide 3 rapid cycles of prototype iteration. Adaptations included optimizing navigation, improving usability (functionality), and enhancing content to promote user engagement and acceptability. In phase 2, all participants checked in, with the highest frequency of full check-ins attributed to pain intensity (183/183, 100.0%), pain interference (175/183, 95.6%), and mood (152/183, 83.1%), respectively. Individual variability was evident for monitoring progress with the highest frequency of history views for pain intensity (51/183, 32.3%), followed by pain interference (24/183, 15.2%). For the goals set feature, 87% (13/15) of participants set a total of 42 goals covering 5 areas, most frequently for activity (35/42, 83%). For phase 3, metasynthesis of qualitative data highlighted that these digital tools were perceived as youth-focused and acceptable. A total of 4 metathemes emerged: (1) importance of user-centered design to leverage user engagement; (2) website design (features) promoting user acceptability and engagement; (3) app functionality supporting self-management; and (4) the role of wider promotion, health professional digital prescriptions, and strategies to ensure longer-term engagement. ConclusionsLeveraging extant digital tools, with appropriate user-informed adaptations, can help to build capacity tailored to support young people’s self-management of musculoskeletal pain.

The coronavirus disease (COVID-19) is a global pandemic that significantly impacts people living with diabetes. Diabetes-related factors of glycemic control, medication pharmacodynamics, and insulin access can impact the severity of a COVID-19 infection. In this commentary, we explore how digital health can support the diabetes community through the pandemic. For those living with diabetes, digital health presents the opportunity to access care with greater convenience while not having to expose themselves to infection in an in-person clinic. Digital diabetes apps can increase agency in self-care and produce clinically significant improvement in glycemic control through facilitating the capture of diabetes device data. However, the ability to share these data back to the clinic to inform virtual care and enhance diabetes coaching and guidance remains a challenge. In the end, it requires an unnecessarily high level of technical sophistication on the clinic’s part and on those living with diabetes to routinely use their diabetes device data in clinic visits, virtual or otherwise. As the world comes together to fight the COVID-19 pandemic, close collaboration among the global diabetes community is critical to understand and manage the sustained impact of the pandemic on people living with diabetes.

BackgroundThere is mixed evidence to support current ambitions for mobile health (mHealth) apps to improve chronic health and well-being. One proposed explanation for this variable effect is that users do not engage with apps as intended. The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions. ObjectiveThis study aimed to consolidate how analytic indicators of engagement have previously been applied across clinical and technological contexts, to inform how they might be optimally applied in future evaluations. MethodsWe conducted a scoping review to catalog the range of analytic indicators being used in evaluations of consumer mHealth apps for chronic conditions. We categorized studies according to app structure and application of engagement data and calculated descriptive data for each category. Chi-square and Fisher exact tests of independence were applied to calculate differences between coded variables. ResultsA total of 41 studies met our inclusion criteria. The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76%, 31/41), the frequency of interactions logged (73%, 30/41), the number of features accessed (49%, 20/41), the number of log-ins or sessions logged (46%, 19/41), the number of modules or lessons started or completed (29%, 12/41), time spent engaging with the app (27%, 11/41), and the number or content of pages accessed (17%, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes. ConclusionsAlthough researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being.

BackgroundChronic pain is a common problem in adolescents that can negatively impact all aspects of their health-related quality of life. The developmental period of adolescence represents a critical window of opportunity to optimize and solidify positive health behaviors and minimize future pain-related disability and impaired work productivity. This research focuses on the development and evaluation of a smartphone-based pain self-management app for adolescents with chronic pain. ObjectiveThe objectives of this study were to characterize (1) the feasibility of deploying a mobile health (mHealth) app (iCanCope) to the personal smartphones of adolescent research participants; (2) adherence to daily symptom tracking over 55 consecutive days; (3) participant interaction with their symptom history; and (4) daily pain-related experiences of adolescents with chronic pain. MethodsWe recruited adolescents aged 15-18 years from 3 Canadian pediatric tertiary care chronic pain clinics. Participants received standardized instructions to download the iCanCope app and use it once a day for 55 days. Detailed app analytics were captured at the user level. Adherence was operationally defined as per the relative proportion of completed symptom reports. Linear mixed models were used to examine the trajectories of daily symptom reporting. ResultsWe recruited 60 participants between March 2017 and April 2018. The mean age of the participants was 16.4 (SD 0.9) years, and 88% (53/60) of them were female. The app was deployed to 98% (59/60) devices. Among the 59 participants, adherence was as follows: low (4, 7%), low-moderate (14, 24%), high-moderate (16, 27%), and high (25, 42%). Most (49/59, 83%) participants chose to view their historical symptom trends. Participants reported pain intensity and pain-related symptoms of moderate severity, and these ratings tended to be stable over time. ConclusionsThis study indicates that (1) the iCanCope app can be deployed to adolescents’ personal smartphones with high feasibility; (2) adolescents demonstrated moderate-to-high adherence over 55 days; (3) most participants chose to view their symptom history; and (4) adolescents with chronic pain experience stable symptomology of moderate severity. Trial RegistrationClinicalTrials.gov NCT02601755; https://clinicaltrials.gov/ct2/show/NCT02601755 (Archived by WebCite at http://www.webcitation.org/74F4SLnmc)

BackgroundMobile health (mHealth) apps for pediatric chronic conditions are growing in availability and challenge investigators to conduct rigorous evaluations that keep pace with mHealth innovation. Traditional research methods are poorly suited to operationalize the agile, iterative trials required to evidence and optimize these digitally mediated interventions. ObjectiveWe sought to contribute a resource to support the quantification, analysis, and visualization of analytic indicators of effective engagement with mHealth apps for chronic conditions. MethodsWe applied user-centered design methods to design and develop an Analytics Platform to Evaluate Effective Engagement (APEEE) with consumer mHealth apps for chronic conditions and implemented the platform to analyze both retrospective and prospective data generated from a smartphone-based pain self-management app called iCanCope for young people with chronic pain. ResultsThrough APEEE, we were able to automate the process of defining, operationalizing, and evaluating effective engagement with iCanCope. Configuring the platform to integrate with the app was feasible and provided investigators with a resource to consolidate, analyze, and visualize engagement data generated by participants in real time. Preliminary efforts to evaluate APEEE showed that investigators perceived the platform to be an acceptable evaluative resource and were satisfied with its design, functionality, and performance. Investigators saw potential in APEEE to accelerate and augment evidence generation and expressed enthusiasm for adopting the platform to support their evaluative practice once fully implemented. ConclusionsDynamic, real-time analytic platforms may provide investigators with a powerful means to characterize the breadth and depth of mHealth app engagement required to achieve intended health outcomes. Successful implementation of APEEE into evaluative practice may contribute to the realization of effective and evidence-based mHealth care.

Background and objectives: Extensive scrutiny within organizational research has positioned job satisfaction as a pivotal factor contingent upon organizational contexts. Our study aimed to refine job satisfaction measurement tools for diverse healthcare settings using insights from a university hospital in Vietnam, enhancing the validity and applicability of these instruments. Methods: The procedure for the contextualization of a job satisfaction measurement tool was established encompassing six key steps: (1) Developing, selecting, or modifying the questionnaire; (2) Assessing face validity; (3) Ensuring content validity; (4) Designing the research for field testing; (5) Assessing the tool reliability and validity; and (6) Assessing discriminant validity between two tools. This procedure served as the foundation for a cross-sectional study involving 216 healthcare staff at a university hospital in Vietnam. Results: The modified tool, comprising 35 items (6 fewer than the original 41-item reference tool), was derived through evaluations of face, content, and construct validity, conducted with 216 healthcare staff. The validity of the modified tool was subsequently confirmed through Confirmatory Factor Analysis, demonstrating favorable fit indices for the job satisfaction item, including a Chi-square/degrees of freedom ratio of 3.15, Comparative Fit Index of 0.86, Tucker Lewis Index of 0.85, and Root Mean Squared Error of Approximation of 0.1. Additionally, the modified tool exhibited a high Cronbach’s alpha of 0.97, a good convergence ranged from 0.4 to 0.6 and a good divergence with the maximum shared variance values were lower than the corresponding average variance extracted values. The job satisfaction scores obtained using the modified tool surpassed significantly those of the original reference tool ( p

BackgroundRandomized controlled trials (RCTs) have long been considered the primary research study design capable of eliciting causal relationships between health interventions and consequent outcomes. However, with a prolonged duration from recruitment to publication, high-cost trial implementation, and a rigid trial protocol, RCTs are perceived as an impractical evaluation methodology for most mHealth apps. ObjectiveGiven the recent development of alternative evaluation methodologies and tools to automate mHealth research, we sought to determine the breadth of these methods and the extent that they were being used in clinical trials. MethodsWe conducted a review of the ClinicalTrials.gov registry to identify and examine current clinical trials involving mHealth apps and retrieved relevant trials registered between November 2014 and November 2015. ResultsOf the 137 trials identified, 71 were found to meet inclusion criteria. The majority used a randomized controlled trial design (80%, 57/71). Study designs included 36 two-group pretest-posttest control group comparisons (51%, 36/71), 16 posttest-only control group comparisons (23%, 16/71), 7 one-group pretest-posttest designs (10%, 7/71), 2 one-shot case study designs (3%, 2/71), and 2 static-group comparisons (3%, 2/71). A total of 17 trials included a qualitative component to their methodology (24%, 17/71). Complete trial data collection required 20 months on average to complete (mean 21, SD 12). For trials with a total duration of 2 years or more (31%, 22/71), the average time from recruitment to complete data collection (mean 35 months, SD 10) was 2 years longer than the average time required to collect primary data (mean 11, SD 8). Trials had a moderate sample size of 112 participants. Two trials were conducted online (3%, 2/71) and 7 trials collected data continuously (10%, 7/68). Onsite study implementation was heavily favored (97%, 69/71). Trials with four data collection points had a longer study duration than trials with two data collection points: F4,56=3.2, P=.021, η2=0.18. Single-blinded trials had a longer data collection period compared to open trials: F2,58=3.8, P=.028, η2=0.12. Academic sponsorship was the most common form of trial funding (73%, 52/71). Trials with academic sponsorship had a longer study duration compared to industry sponsorship: F2,61=3.7, P=.030, η2=0.11. Combined, data collection frequency, study masking, sample size, and study sponsorship accounted for 32.6% of the variance in study duration: F4,55=6.6, P

This study aimed to investigate the occurrence of potential pathogenic bacteria from surface water samples collected at 30 stations along Ha Long Bay, Vietnam. Among 116 bacterial isolates identified, the composition analysis indicated 24 distinct bacterial genera, with Vibrio (29.3%), Pseudomonas (23.3%), Staphylococcus (8.6%), and Bacillus (6.0%) being the most dominant. The taxonomic analysis revealed 61/116 (52.6%) isolates as potential pathogens (risk group 2) belonging to 21 bacterial species. Potential pathogens were detected in 28/30 (93.3%) sampling stations, with the most common pathogens including Vibrio alginolyticus (n=25), Vibrio parahaemolyticus (n=5), Enterobacter cloacae (n=3), Pseudomonas mendocina (n=3), Aeromonas hydrophila (n=2), Enterobacter asburiae (n=2), Klebsiella oxytoca (n=2), Bacillus cereus (n=2), and Staphylococcus haemolyticus (n=2). The selected pathogens were highly resistant to Amoxicillin (100%), Piperacillin (100%), Amoxicillin + Clavulanate (85.7%), Ticarcillin (71.4%), Cefoxitin (54.4%), Kanamycin (50%), and Fosfomycin (50%), and less resistant to Trimethoprim + sulfamethoxazole, Fusidic Acid, Tetracycline, Erythromycin, Pristinamycin, Dalacin, and Rifampicin (

Objectives: Vietnam has witnessed a severe shortage of qualified staff in the public health sector after the COVID-19 pandemic. Our cross-sectional study aimed to identify job motivation and associated factors among experienced frontline health staff working in public health in order to have preventive measures in the event of future pandemics. Methods: A cross-sectional study, from March 2022 to November 2022 at a Vietnamese public hospital, on the job motivation and the predicted factors of 381 healthcare workers who participated in the frontlines of the COVID-19 pandemic from 2020 to 2021. The survey tool, developed by Mbindyo Patrick (2009), includes three components: (i) job satisfaction, (ii) organizational commitment, and (iii) conscientiousness. The survey tool was revalidated in our study with structural equation model for the construction of job motivation model and confirmatory factor analysis for certifying the elementary three components (factors) of the tool. And the tool’s reliability was evaluated by Cronbach’s Alpha. Bivariate analysis and multiple logistic regression were used to identify the predicted factors with the job motivation cutoff of 4.0. Results: The tool for job motivation constructs showed all specifications were good fit indices and the Cronbach’s Alpha was 0.85. The job motivation of health staff decreased dramatically in all dimensions post COVID-19 pandemic, with a mean score of 3.26. Job satisfaction and organizational commitment were the most negatively impacted areas, with scores of 3.02 and 3.00, respectively. The predicted factors of low job motivation were young age (less than 30 years old), low monthly income (less than $400), high qualification, and non-managerial positions with odds ratio of 2.27, 2.5, 2.09, and 3.61, respectively. Conclusion: Following the COVID-19 outbreak in Vietnam, healthcare workers who had been in the frontlines of the COVID-19 pandemic, had experienced a significant decline in their job motivation, despite their continued employment at public hospitals.

Phlebosclerotic colitis (PC), also known as idiopathic mesenteric phlebosclerosis, is a rare disease resulting in ischemic colon due to venous sclerosis and calcifications that can be identified by characteristic imaging features on computed tomography and colonoscopy. Clinical examination reveals nonspecific symptoms with slow progression in the majority of cases. Patients with PC often require late-stage hospitalization and colectomy. We report a 79-year-old man with long-term clinical symptoms who used herbal medicines. Computed tomography and colonoscopy studies revealed several classical PC characteristics, and the patient subsequently underwent emergency total colectomy.

Tuberculosis bacilli can enter the human body through the respiratory system, digestive system, or skin and mucous membranes, with the respiratory tract representing the primary point of entry. Once inside the body, tuberculosis bacilli can enter the bloodstream and attack other organs, including the lymphatic system. One manifestation associated with lymphatic tuberculosis infiltration is the presence of large hilar and mediastinal lymph nodes, which are common in children and classified as primary tuberculosis. A diagnosis of primary tuberculosis in adults is often overlooked by doctors due to its low frequency. When a single enlarged mediastinal lymph node is observed, most doctors suspect other causes, especially malignancy. Determining the correct diagnosis for enlarged hilar and mediastinal lymph nodes can be difficult because diagnostic interventions in this area are challenging to perform. We report a clinical case of primary lymph node tuberculosis in an adult, confirmed by endobronchial ultrasound-guided transbronchial needle aspiration. We aim to provide doctors with a more comprehensive approach for diagnosing this disease.

Tourism studies pay relatively little attention to the importance of reflexivity and metacognition. This article highlights the integral importance of explicitly considering reflexivity and metacognition issues as part of the research process when studying pro-poor tourism (PPT). Hence, the paper presents a reflexive and metacognitive account of the challenges and obstacles encountered while undertaking ethnographic fieldwork in PPT. Drawing on the first author's research experiences in Vietnam with informal tourism workers, it reflects the positional, ethical, and methodological challenges faced during and after the fieldwork. This article is the first to provide a critical reflection on fieldwork in PPT, thereby adding to the conceptual recognition of the importance of reflexivity and metacognition in tourism studies overall. It also shares deeper insights into the difficulties associated with research that involves poor people, whose voices remain largely absent from tourism studies. [ABSTRACT FROM AUTHOR]

Trong bài báo này, chúng tôi nghiên cứu siêu vật liệu hyperbolic trên cơ sở cấu trúc chứa sợi kim loại kích thước nanomet nhúng trong nền điện môi bằng phương pháp lý thuyết môi trường hiệu dụng. Kết quả nghiên cứu cho thấy siêu vật liệu hyperbolic với độ thấm điện môi gần bằng 0 (ENZ) có thể tồn tại cùng lúc ở loại I và loại II. Điểm đặc biệt lan truyền sóng điện từ phân cực từ ngang được nghiên cứu trong hai loại siêu vật liệu này. Chùm ánh sáng đến phân kỳ dường như chuyển thành chùm sáng chuẩn trực tốt và chuyển toàn bộ năng lượng theo hướng vuông góc với bề mặt siêu vật liệu hyperbolic loại I trong vùng giá trị đặc biệt của góc tới từ –5 đến 5°. Trong siêu vật liệu hyperbolic loại II, sóng ánh sáng truyền hoàn toàn qua lớp cấu trúc vật liệu; đồng thời, giá trị hệ số truyền qua không đổi trong vùng giá trị góc tới từ –40 đến 40°. Sự thay đổi bán kính sợi kim loại trong cấu trúc và độ dày của lớp siêu vật liệu có ảnh hưởng đến đặc tính lan truyền sóng điện từ.

SERVPERF (Service Performance) tool needs to be adapted to the context and the usage purpose. Our study aimed to validate the context adapted SERVPERF tool in an Oncology public hospital in Vietnam. A study was conducted in 2020 with 227 in-patients as respondents the modified SERVPERF tool. Data collected were analyzed for tool assessment (reliability and validity). The new order in 5 factors in the modified tool were: (1) Responsiveness; (2) Empathy; (3) Reliability; (4) Tangible and (5) Assurance. The modified tool has a high Cronbach’s alpha of .94. The tool validity was confirmed by Confirmatory Factor Analysis (CFA) where the healthcare service quality item corresponds as χ 2 /df = 3.79, Comparative Fit Index (CFI) = 0.85, Tucker Lewis Index (TLI) = 0.83, and Root Mean Squared Error of Approximation (RMSEA) = 0.08 are good fit indices. The modified tool SERVPERF with high reliability and validity could be applied for measuring the clients’ perceptions about healthcare service quality in other Oncology public hospitals in Vietnam.

While CD4 Th1 cells are required for resistance to intramacrophage infections, adoptive transfer of Th1 cells is insufficient to protect against Salmonella infection. Using an epitope-tagged vaccine strain of Salmonella, we found that effective protection correlated with expanded Salmonella-specific memory CD4 T cells in circulation and nonlymphoid tissues. However, naive mice that previously shared a blood supply with vaccinated partners lacked T cell memory with characteristics of tissue residence and did not acquire robust protective immunity. Using a YFP-IFN-γ reporter system, we identified Th1 cells in the liver of immunized mice that displayed markers of tissue residence, including P2X7, ARTC2, LFA-1, and CD101. Adoptive transfer of liver memory cells after ARTC2 blockade increased protection against highly virulent bacteria. Taken together, these data demonstrate that noncirculating memory Th1 cells are a vital component of immunity to Salmonella infection and should be the focus of vaccine strategies.