UNASSIGNED: The Together for Health-Virginia (T4H-VA) Research Program aimed to advance cancer prevention, education, and outreach in Virginia. Creating a representative and inclusive cohort is critical to the program\'s mission and quality of outcomes. The T4H-VA Research Program utilized a multi-modal sampling approach to improve population health assessment. The current study describes the technology-based, non-probability platform developed for this purpose and compares differences between the probability-based (mail-based) and non-probability-based (e-cohort) methods with respect to participant demographics, health characteristics, and health information and technology use.
UNASSIGNED: T4H-VA is a research registry focusing on 54 counties within the Massey Comprehensive Cancer Center (MCCC) catchment area in Richmond, VA. Adult residents proficient in English were eligible. For the probability-based sampling, surveys were mailed to residents within the catchment area. For the non-probability sampling, an online study platform was developed and surveys were completed through the web/mobile app.
UNASSIGNED: Both cohorts fell short of recruitment goals. The study yielded 1158 participants (M=57, SD=16 years; 55.0% female; 72.1% White); 899 (77.6%) were sampled through the probability, mail-based approach. Participants who identified as \"other\" race were significantly less likely to be sampled by the non-probability method. Significant differences emerged, including health protective (greater moderate and high physical activity) and risk factors (greater alcohol consumption and personal history of cancer) in the non-probability, e-cohort relative to the probability sample. E-Cohort participants were significantly more likely to report using electronic health records.
UNASSIGNED: Overall difficulties in recruiting were caused, at least in part, by the onset of the COVID-19 pandemic and related factors. The e-cohort, which used exclusively digital recruitment strategies, fell significantly short of recruitment goals. This suggests in-person and mail-based strategies remain important for recruitment. Moreover, instead of favoring a singular approach, a combined approach to survey sampling may capitalize on the strengths of each sampling mode to increase diversity in sociodemographic and health risk characteristics.

The evolution of digital media has increased the number of crimes committed using digital equipment. This has led to the evolution of the computer forensics area to digital forensics (DF). Such an area aims to analyze information through its main phases of identification, collection, organization, and presentation (reporting). As this area has evolved, many techniques have been developed, mainly focusing on the formalization of terminologies and concepts for providing a common vocabulary comprehension. This has demanded efforts on several initiatives, such as the definition of ontologies, which are a means to identify the main concepts of a given area. Hence, the existing literature provides several ontologies developed for supporting the DF area. Therefore, to identify and analyze the existing ontologies for DF, this paper presents a systematic literature review (SLR) in which primary studies in the literature are studied. This SLR resulted in the identification of ontology building methodologies, ontology types, feasibility points, evaluation/assessment methods, and DF phases and subareas ontologies have supported. These results were based on the analysis of 29 ontologies that aided in answering six research questions. Another contribution of this paper is a set of recommendations on further ontology-based support of DF investigation, which can guide researchers and practitioners in covering existing research gaps.

The accelerated adoption of digital health technologies in the last decades has raised important ethical and safety concerns. Despite the potency and usefulness of digital health technologies, addressing safety, and ethical considerations needs to take greater prominence. This review paper focuses on ethical and safety facets, including health technology-related risks, users\' safety and well-being risks, security and privacy concerns, and risks to transparency and diminished accountability associated with the utilization of digital health technologies. In order to maximize the potential of health technology benefits, awareness of safety risks, and ethical concerns should be increased, and the use of appropriate strategies and measures should be considered.

As digital technology becomes more prevalent in the practice of dental medicine, methods to fully replace 2-dimensional photography and analog devices such as the facebow are still in their infancy. As more practices adopt 3-dimensional (3D) intraoral scanners, effective digital communication of the relationships between the teeth and the face becomes essential. With the high cost of intraoral scanners, the additional expense of a face scanner is not a feasible investment for many practices. This article explores a technique for meshing (lower resolution) facial data obtained from a smartphone-based scanner with high-resolution intraoral scan data. In this approach, the data from a free 3D scanning application on a smartphone and a traditional intraoral scanner are meshed so that high-resolution data are available for intraoral features and lower resolution data are used to capture the gross contours of the face. In this way, a hybrid-resolution composite scan that incorporates all of the data needed to simulate the face and accurately reproduce the teeth is generated without the cost of additional scanning equipment. This article defines a new term, the facial registration scan, for use alongside the familiar digital bite registration obtained with an intraoral scanner. To illustrate the clinical use of the hybrid-resolution scan concept, this article presents a case in which this method was used for the restoration of maxillary anterior implants.

BACKGROUND: Noncommunicable diseases (NCDs), particularly diabetes and chronic kidney diseases, pose a significant health burden in Thailand, especially among socioeconomically disadvantaged populations. The existing primary health care system faces challenges in providing optimal care for NCDs due to inadequate primary care workforce. The SMARThealth program offers a technology-based solution to enhance NCD management through task-sharing among nonphysician health care workers.
OBJECTIVE: This study aims to adapt and implement the SMARThealth Diabetes program in rural Thailand to improve diabetes management. The main objectives are to (1) adapt, validate, and integrate the SMARThealth Diabetes program for improving the management of type 2 diabetes mellitus at the primary health care level; and (2) to determine the feasibility and acceptability of the SMARThealth Diabetes program in rural communities of Thailand.
METHODS: A pragmatic, type 2 hybrid effectiveness or implementation, parallel-group cluster randomized controlled trial of 12 months duration and involving 51 subdistrict health offices in rural communities of Kamphaeng Phet province, Thailand, will be conducted. The intervention arm will receive the SMARThealth Diabetes program, including workforce restructuring, clinical decision support system, and continuous performance monitoring, while the control arm will continue with usual practice. Data will be collected using the SMARThealth platform and will be stored on a server in Thailand. The primary outcome measure will be the change in mean hemoglobin A1c (HbA1c) measured at randomization and 12 months from randomization between the intervention and control clusters. Secondary outcomes will include the difference in change in albuminuria status, estimated glomerular filtration rate, systolic blood pressure, and low-density lipoprotein cholesterol level. The analysis for change in HbA1c between baseline and end of study will be performed using linear mixed models. Any imbalances between the 2 arms will be addressed by sensitivity analyses. Additionally, a mixed methods process evaluation will be conducted to assess the implementation process, that will include in-depth interviews and focus group discussions, in addition to the quantitative data collected during the implementation process. The qualitative data will be thematically analyzed to explore factors that promote or inhibit the implementation and maintenance of the program.
RESULTS: The data collection commenced in November 2022, and the results will be ready for publication by the first quarter of 2025. Effectiveness of the intervention package will be assessed by change in mean HbA1c measures, and detailed feasibility, barriers, and enablers for the implementation of the intervention will be documented through a detailed process evaluation.
CONCLUSIONS: The study protocol outlines a novel approach to enhancing diabetes management in rural Thailand through digital technology-based interventions that will facilitate task-sharing among health care workers. This can help inform future strategies for improving NCD care in low-resource settings globally.
BACKGROUND: Thai Clinical Trials Registry TCTR20200322006; https://www.thaiclinicaltrials.org/show/TCTR20200322006.
UNASSIGNED: DERR1-10.2196/59266.

BACKGROUND: Malaria impacts nearly 250 million individuals annually. Specifically, Uganda has one of the highest burdens, with 13 million cases and nearly 20,000 deaths. Controlling the spread of malaria relies on vector surveillance, a system where collected mosquitos are analyzed for vector species\' density in rural areas to plan interventions accordingly. However, this relies on trained entomologists known as vector control officers (VCOs) who identify species via microscopy. The global shortage of entomologists and this time-intensive process cause significant reporting delays. VectorCam is a low-cost artificial intelligence-based tool that identifies a mosquito\'s species, sex, and abdomen status with a picture and sends these results electronically from surveillance sites to decision makers, thereby deskilling the process to village health teams (VHTs).
OBJECTIVE: This study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction.
METHODS: The VectorCam system has imaging hardware and a phone app designed to identify mosquito species. Two users are needed: (1) an imager to capture images of mosquitos using the app and (2) a loader to load and unload mosquitos from the hardware. Critical success tasks for both roles were identified, which VCOs used to train and certify VHTs. In the first testing phase (phase 1), a VCO and a VHT were paired to assume the role of an imager or a loader. Afterward, they swapped. In phase 2, two VHTs were paired, mimicking real use. The time taken to image each mosquito, critical errors, and System Usability Scale (SUS) scores were recorded for each participant.
RESULTS: Overall, 14 male and 6 female VHT members aged 20 to 70 years were recruited, of which 12 (60%) participants had smartphone use experience. The average throughput values for phases 1 and 2 for the imager were 70 (SD 30.3) seconds and 56.1 (SD 22.9) seconds per mosquito, respectively, indicating a decrease in the length of time for imaging a tray of mosquitos. The loader\'s average throughput values for phases 1 and 2 were 50.0 and 55.7 seconds per mosquito, respectively, indicating a slight increase in time. In terms of effectiveness, the imager had 8% (6/80) critical errors and the loader had 13% (10/80) critical errors in phase 1. In phase 2, the imager (for VHT pairs) had 14% (11/80) critical errors and the loader (for VHT pairs) had 12% (19/160) critical errors. The average SUS score of the system was 70.25, indicating positive usability. A Kruskal-Wallis analysis demonstrated no significant difference in SUS (H value) scores between genders or users with and without smartphone use experience.
CONCLUSIONS: VectorCam is a usable system for deskilling the in-field identification of mosquito specimens in rural Uganda. Upcoming design updates will address the concerns of users and observers.

UNASSIGNED: eHealth literacy is an essential skill for pursuing electronic health information, particularly for older people whose health needs increase with age. South Korea is now at the intersection of a rapidly digitalizing society and an increasingly aged population. eHealth literacy enables older people to maximize the effective use of emerging digital technology for their health and quality of life. Understanding the eHealth literacy of Korean older adults is critical to eliminating the gray digital divide and inequity in health information access.
UNASSIGNED: This study aims to investigate factors influencing eHealth literacy in older Korean adults and its impact on health outcomes and eHealth use.
UNASSIGNED: This was a cross-sectional survey. Community-dwelling older adults 65 years and older in 2 urban cities in South Korea were included. eHealth literacy was measured by the eHealth Literacy Scale. Ordinal logistic regression was used to analyze factors associated with eHealth literacy and multivariate ANOVA for the impact of eHealth literacy on health outcomes and eHealth use.
UNASSIGNED: In total, 434 participants were analyzed. A total of 22.3% (97/434) of participants had high eHealth literacy skills. Increasing age, higher monthly income, and time spent on the internet were significantly associated with eHealth literacy (P<.001), and social media users were 3.97 times (adjusted odds ratio 3.97, 95% CI 1.02-15.43; P=.04) more likely to have higher skill. Higher eHealth literacy was associated with better self-perceived health and frequent use of digital technologies for accessing health and care services (P<.001).
UNASSIGNED: Disparity in socioeconomic status and engagement on the internet and social media can result in different levels of eHealth literacy skills, which can have consequential impacts on health outcomes and eHealth use. Tailored eHealth interventions, grounded on the social and digital determinants of eHealth literacy, could facilitate eHealth information access among older adults and foster a digitally inclusive healthy aging community.

Objectives: As development and introduction of digital self-management technologies continues to increase, the gap between those who can benefit, and those who cannot correspondingly widens. This research aimed to explore the use of digital self-management technology by older adults with three highly-prevalent long-term conditions (chronic kidney disease, diabetes and dementia), and build expert consensus across the conditions on changes needed to improve effective usage. Method: This qualitative research involved a modified e-Delphi Study. The Delphi panel was comprised of experts with personal, academic or clinical expertise related to one of the long-term conditions and/or the development and use of digital self-management technology. The e-Delphi involved a round of online semi-structured interviews followed by two rounds of a structured online survey. Results: Fourteen experts participated in the study, with eleven of the fourteen completing all three rounds. Analysis of the interviews (round 1 of the Delphi) led to 7 main themes and 29 sub-themes. These were translated into 26 statements that formed the basis of the online survey questions. In the first administration of the survey (round 2) 19 statements reached consensus. After the second administration a further 6 statements reach consensus. Conclusion: The findings reflect expert consensus on barriers to the use of digital self-management by older adults with 3 different, but inter-related conditions, and identify ways in which the design and provision of such technologies could be improved to facilitate more effective use. It is concluded that both the design and the provision of technologies should consider a combination of individual, condition-specific and age-related requirements. By building a consensus on issues and potential strategies common across the three conditions, we aim to inform future research and practice and facilitate effective self-management by older adults.

The Healthcare Innovation Lab, established by BJC HealthCare and Washington University School of Medicine, has catalyzed care delivery innovations since 2017. Focusing on digital health to enhance care delivery and patient outcomes, the Lab emphasizes predictive analytics, digital point-of-care tools, and remote patient monitoring. The Lab identifies innovative ideas that align with the health system mission and deliver empiric value to its patients and care teams. Since its inception, the Lab has vetted 507 ideas, piloting 98, with a success rate of 40%. Examples include a predictive model to improve palliative care referrals and goal-of-care discussions, a digital approach to non-emergent medical transportation that enhances access and equity, and a COVID-19 home monitoring program that proved essential during the pandemic. These initiatives underscore the importance of integrating digital technology with health care, balancing innovation with practical application, and using a data-informed approach to innovation selection and assessment.

This study aims to invigorate China\'s Rural Revitalization Strategy by focusing on the training of China\'s new generation of professional farmers, particularly enhancing the skills of modern agricultural practitioners. Utilizing a mixed-method research approach, which includes the analysis of 343 questionnaire surveys and personal interviews, significant shortcomings in the previous training of farmers were revealed, such as limited reach, high conformity in training content, and outdated methods. These findings highlight the challenges traditional training methods face in the digital technology era. In response to these challenges, this study proposes a novel training model designed for the digital era. This model encompasses seven aspects: training goals, subjects, content, means, management, methods, and effect assessment, with the aspiration to reform traditional farmer training methods. This research expands the existing literature by constructing a comprehensive framework for training the new generation of farmers, bridging the gap between traditional practices and the demands of modern agricultural training influenced by digital advancements. The study\'s innovation lies in its proposition of a modernized training model for the new generation of farmers, leveraging the advancements of the digital technology era. By modernizing agricultural training and enhancing the capabilities of new professional farmers, it significantly contributes to the progression of China\'s Rural Revitalization Strategy.