Collection Platform Research Articles

Work Attitude Personal Life Record Collection Platform: A Framework for Daily Mental Health Monitoring of Workers

Work Attitude Personal Life Record Collection Platform: A Framework for Daily Mental Health Monitoring of Workers

Maximising the Quality of Stroke Care: Reporting of Data Collection Methods and Resourcing in National Stroke Registries: A Systematic Review.

Stroke registries are tools for improving care and advancing research. We aim to describe the methodology and resourcing of existing national stroke registries. We conducted a systematic search of the published, peer-reviewed literature and grey literature examining descriptions of data collection methods and resourcing of national stroke registries published from 2012 to 2023. The systematic review was registered in PROSPERO (CRD42023393841). 101 records relating to 21 registries in 19 countries were identified. They universally employed web-based platforms for data collection. The principal profession of data collectors was nursing. All included the acute phase of care, 28% (6) registered the pre-hospital (ambulance) phase and 14% (3) included rehabilitation. 80% (17) collected outcome data. The registries varied in their approach to outcome data collection; in 9 registries it was collected by hospitals, in 2 it was collected by the registry, and 7 had linkage to national administrative databases allowing follow-up of a limited number of end points. Coverage of the total number of strokes varies from 6 to 95%. Despite widespread use of Electronic Health Records (EHRs) the ability to automatically populate variables remained limited. Governance and management structures are diverse, making it challenging to compare their resourcing. Data collection for clinical registries requires time and necessary skills and imposes a significant administrative burden on the professionals entering data. We highlight the role of clinical registries as powerful instruments for quality improvement. Future work should involve creating a central repository of stroke registries to enable the development of new registries and facilitate international collaboration.

Evaluating the Impact of AI Dependency on Cognitive Ability among Generation Z in Higher Educational Institutions: A Conceptual Framework

This research aims to examine the factors of AI dependency (AID) and to investigate the relationship between AI dependency on cognitive ability (CA) among Generation Z (Gen Z) in higher educational institutions. The factors involved are academic self-efficacy (ASE), academic stress (AS), and performance expectation (PE). In this research, the proposed research design is a quantitative method. A self?administered questionnaire will be distributed to respondents, a group of students who were born between 1997 to 2012, and the AI user. The questionnaire will be utilizing the Google Form platform for easier data collection. A snowball sampling method will be applied. Then, the data collected will be analyzed through the partial least square (PLS-SEM) technique. The research findings are expected to highlight the significant emphasis on the contributing factors of AI dependency among Gen Z students. Then, the findings will also provide a model to understand better the impact of AI dependency on cognitive ability. Additionally, it is foreseen that the findings will help various parties, including the government, to create better models for helping Gen Z students apply AI in decent ways to help them increase their cognitive ability. It is also considered a long-term strategy to become a nation with numbers of high cognitive ability citizens. Further, a practical framework based on AI dependency on cognitive ability will be developed as a guideline to support the effort of the government or industry practitioners to increase awareness among Gen Z students on how crucial to possess high cognitive ability.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Value for solidarity: a proxy for community understanding and acceptance of the basic principles of community-based health insurance in rural Ethiopia

BackgroundSolidarity is an aspect of human association that gives emphasis to the cohesive social bond that holds a group together and is valued and understood by all members of the group. A lack of understanding of the solidarity principle is one of the main reasons for low population coverage in microhealth insurance schemes. This study aimed to examine the extent to which people value solidarity and the factors that explain the differences.MethodsA community-based cross-sectional study was carried out in two districts of northeast Ethiopia among 1232 randomly selected households which have ever been registered in a community-based health insurance scheme. Face-to-face interviews were conducted with household heads using a standardized questionnaire deployed to an electronic data collection platform. Solidarity was measured using three dimensions: income solidarity, risk solidarity, and cost coverage. Principal component analysis was used to construct composite variables, and the reliability of the tools was checked using Cronbach’s alpha. A multivariable analysis was performed using the partial proportional odds model to determine the associations between variables. The degree of association was assessed using the odds ratio, and statistical significance was determined at 95% confidence interval.ResultsThree-quarters (75%) of the respondents rated risk solidarity as high, while 70% and 63% rated income solidarity and cost coverage as high, respectively. Place of residence (AOR = 2.23; 95% CI: 1.68, 2.94), wealth index (AOR = 1.51; 95% CI: 1.07, 2.12), self-rated health status (AOR = 1.64; 95% CI: 1.12, 2.40), trust in insurance schemes (AOR = 1.68; 95% CI: 1.22, 2.30), perceived quality of care (AOR = 1.75; 95% CI: 1.33, 2.31) and frequency of outpatient visits (AOR = 2.05; 95% CI: 1.30, 3.24) were significant predictors of value for solidarity.ConclusionsThe community placed greater value for solidarity, indicating community understanding and acceptance of the core principles of microhealth insurance. Administrators of the insurance scheme, health authorities, and other actors should strive to create a transparent management system and improve access to high-quality health care, which will facilitate community acceptance of the insurance scheme and its guiding principles.

First-aid knowledge and practices towards patients with epileptic seizures among the dental students in Saudi Arabia – A cross-sectional study

ABSTRACT Background: Dental care of patients with epilepsy and seizures must be performed by dentists who are familiar with these disorders. Improper training and incapability to manage medical emergencies, including epileptic seizures, may cause thoughtful consequences and legal actions. Therefore, dental students must have a proper knowledge of the disease and its consequences. They should have the ability to provide first-aid measures helping a patient experiencing an epileptic seizure in their clinics. This study aims to evaluate the first-aid knowledge and practices towards patients with epileptic seizures among the dental students in Saudi Arabia. Methods: A cross-sectional study was conducted targeting dental students in Saudi Arabia from 15 September 2022 to 15 December 2022 using convenience sampling. An online questionnaire was sent to the students via social media platforms for data collection. The data analysis was conducted using the Statistical Package for the Social Sciences (SPSS) version 16.0. Results: Of the 499 dental students included, 154 (30.86%) students had ever seen an epileptic patient in the clinics. About 16% of students did not know about performing cardiopulmonary resuscitation (CPR) on patients. The majority (66.5%) would call the ambulance after the patient’s seizure stops. About 82% of students prefer to call an ambulance if the patients face difficulty breathing or waking up after the seizure. More than 60% of students were taking precautions before treating an epilepsy patient. About 58.5% of the students were using local anaesthesia for an epilepsy patient. Nearly 41.9% do not have the confidence to treat an epilepsy patient. About 28.0% would swipe the vagus nerve stimulation (VNS) magnet over the left side of the chest. Of 154 students, who had ever seen epileptic patients, the majority (68.8%) refused to treat patients if they observe noncompliance with medication and follow-up among the patients who had any recent illness or seizures (16.2%). Conclusion: The undergraduate dental students had an intermediate knowledge regarding epilepsy and epileptic seizure first aid. However, some students lacked the confidence to deal with the epileptic cases in their clinics. Educational campaigns on epilepsy and first-aid courses targeting dental students are highly recommended.

Confronting Fake News Through Collective Intelligence in Adolescents

Adolescents are vulnerable to the exponential increase in fake news spread. Collective judgment on the quality of news has been an effective intervention. Collective Learning platform makes it possible to apply collective intelligence through simultaneous interaction in a large group, similar to social networks. The objective of this article is to analyze whether this platform effectively improves how adolescents confront fake news. 360 Spanish students aged 15–16 years old participated in the experiment confronting a fictitious case of fake news. The quantitative analysis using ANOVA demonstrated that there were significant positive changes for addressing fake news, both overall as well as in each of the participants. We discuss how these results confirm that collective intelligence improves digital literacy in the Internet challenges.

611P Empowering patient decision-making and enhancing clinical care through the UK Duchenne muscular dystrophy patient data collection platform

611P Empowering patient decision-making and enhancing clinical care through the UK Duchenne muscular dystrophy patient data collection platform

Abstract B051: Improving Race and Ethnicity Data Completeness Using a Concise Survey Designed to Collect Social Determinants of Health (SDoH) Data: Results from Two Community Oncology Practices

Abstract Background: Race and ethnicity (R/E) information is crucial for understanding health disparities and the efficacy/effectiveness of treatments across different patient groups. However, these data are often missing or inaccurate in EHR. Literature suggests that surveys can improve upon these gaps in EHR R/E data. FDA recently issued guidance on collecting and reporting R/E data using standardized terminology, which should be applied to demographic data in new clinical trials and studies. N-Power Medicine (NPM) developed a concise survey, incorporating FDA’s R/E recommendations, to collect SDoH data as part of its registry (Kaleido) enhanced oncology network platform for quality data collection. Here, we report the initial R/E findings from this survey. Methods: Patients participating in the Kaleido Registry at 2 community oncology practices across different US regions completed the survey during their clinic visits with assistance from NPM onsite staff. The survey allowed multiple race selections to include mixed races and a free-text option for “Other”. R/E data from the same patients were also extracted from EHRs. Completeness was reported as the percentage of informative answers: any race, other, and chose not to disclose. “Unknown” and “Not available” were missing data. Results: Between November 11, 2023 and April 30, 2024, 1109 patients completed the questionnaire with over 99% response rate. Both sites contributed equally and had similar patient demographics: about 61% female and 79% aged 60 or above. The large majority of patients had breast cancer followed by lung and colorectal cancers. Survey completeness was 96.7% for race and 99.6% for ethnicity, as compared with EHR completeness of 78.6% for race and 91.1% for ethnicity. 21% of EHR data for race was coded as "Unknown and Not Available. About 20% of the survey respondents were Hispanic or Latino. Both sites’ EHR data included expected R/E categories except for one site having 21 additional unique race entries. The majority of survey participants were White (W, 75.7%) followed by Asian (A, 3.6%), American Indian or Alaska Native (AI/AN, 3.2%), Black or African American (B/AA, 2.4%), Native Hawaiian or Pacific Islander (NH/PI, 1.0%) and multiple-selection (2.9%; Other and W (8), AI/AN (3), A (2); W and AI/AN (10), A (7); A and AI/AN (1), W and B (1)). 11% of responses were Other, which included various races (15% European/Caucasian), nationalities, ethnicities (49% Hispanic) and religions, hence, could be further disambiguated. We will report details regarding the survey respondent’s other SDoH. Conclusions: We successfully implemented a standardized questionnaire, incorporating FDA R/E collection recommendations, at two community oncology sites. The results demonstrate that the NPM R/E questionnaire has higher completeness compared to EHR data, allowing better assessment of the impact of R/E on clinical trial enrollment, genomic testing utilization, treatment patterns, and outcomes. This improvement is vital for understanding and addressing health disparities in oncology care. Citation Format: Michael Zachary Koontz, Ayse Tezcan, Julie Latham, Phyllis Ortiz, Joseph Nathaniel Paulson, Annette Campbell Fontaine. Improving Race and Ethnicity Data Completeness Using a Concise Survey Designed to Collect Social Determinants of Health (SDoH) Data: Results from Two Community Oncology Practices [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B051.

Exploring Kleefstra syndrome cohort phenotype characteristics: Prevalence insights from caregiver-reported outcomes

Kleefstra syndrome (KLEFS1) is a rare genetic neurodevelopmental disorder affecting multiple body systems. It continues to be under-researched, and its prevalence remains unknown. This paper builds on the international KLEFS1 cohort of 172 individuals based on the caregiver-reported outcomes collected within the online data collection platform GenIDA and reports the occurrence, frequency and severity of symptoms in KLEFS1. The study clearly shows the importance of caregiver-reported outcomes collections in the rare disease domain. Moreover, the study emphasizes the need for more specific and enhanced data collection methods, suggesting recommendations to optimize caregiver-reported registries and foster an even more profound understanding of rare diseases.

Visual Interpretation of High-Resolution Aerial Imagery: A Tool for Land Managers

Abstract Remotely sensed imagery from various collection platforms (e.g., satellites, crewed and uncrewed aircraft) are used by biologists and other conservation personnel to support management activities ranging from monitoring invasive species to assessing land cover and vegetation characteristics. Although remote sensing–based vegetation indices and models have been developed and used for some management applications, straightforward visual interpretation of imagery by on-the-ground personnel may be a pragmatic approach for obtaining time-sensitive and spatially relevant information to support and guide local management activities. Our primary objective was to qualitatively assess our ability to identify patches of target invasive plant species based on simple visual interpretation of high-resolution aerial imagery. We also sought to compare the high-resolution imagery to widely available imagery (e.g., National Agriculture Imagery Program) to determine the efficacy of each for assessing vegetation communities and land-cover features in support of management activities. To accomplish these objectives, we obtained high-resolution imagery and visually scanned and assessed the imagery by using standard geographic information system software. We were able to differentiate patches of crownvetch Securigera varia (L.) Lassen and wild parsnip Pastinaca sativa L., but not spotted knapweed Centaurea stoebe L. or leafy spurge Euphorbia esula L. The relative success in identifying these species had a relationship to plant characteristics (e.g., flower color and morphology, height), time of year (phenology), patch size and density, and potentially site characteristics such density of the underlying vegetation (e.g., grasses), substrate color characteristics (i.e., color contrast with flowers), and physical disturbance. Our straightforward, qualitative assessment suggests that visual interpretation of high-resolution imagery, but not some lower-resolution imagery, may be an efficient and effective tool for supporting local invasive species management through activities such as monitoring known patches, identifying undetected infestations, assessing management actions, guiding field work, or prioritizing on-the-ground monitoring activities.

Associations of Implicit and Explicit Sexual Double Standard Endorsement and Sexual Assertiveness with Sexual and Interactional Competence in Emerging Adults.

In this study, among emerging adults, we investigated the interrelationships of explicit and implicit measures of sexual assertiveness (SA) and sexual double standard endorsement (SDS) on the one hand, and different aspects of sexual and interactional competence (SAIC) on the other hand, using Partial Least Squares Path Modeling (PLS-PM) of cross-sectional data. Participants were 159 sexually active, heterosexual individuals in the Netherlands between 18 and 25 years. No exclusion criteria were used. The Sexual Competence and Interaction Competence in Youth and lifetime number of sexual partners were used to measure SAIC. Explicit SA was measured using the Hurlbert Index of Sexual Assertiveness, while Explicit SDS was assessed using the Scale for the Assessment of Sexual Standards in Youth. Two implicit association tests were performed to measure implicit SA and SDS. Participants accessed these computerized reaction time tasks via a secure online data collection platform. Results showed a strong association between the latent factors of sexual attitudes and SAIC. Greater SA and lower SDS were associated with a greater competence level. No gender effects were found.

A Digital Twin of the Trondheim Fjord for Environmental Monitoring—A Pilot Case

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period.

Prevalence and Predictors of Carpal Tunnel Syndrome Symptoms Among Teachers in Jazan: A Cross-Sectional Study.

Background Carpal tunnel syndrome (CTS) is a common musculoskeletal condition of the hand and wrist frequently associated with repetitive hand motion and environmental considerations. Teachers are more likely to acquire CTS because of their lengthy writing and computer use. This study aimed to determine the prevalence of CTS symptoms and related variables among schoolteachers in Jazan, Saudi Arabia. Methods This study was conducted as a cross-sectional survey of teachers in Jazan, utilizing an online platform for data collection. The Boston Carpal Tunnel Syndrome Questionnaire (BCTQ) was the primary tool used to determine symptom intensity and functional status. The data were rigorously analyzed using a range of statistical methods, including descriptive statistics, the Mann-Whitney U test, the Kruskal-Wallis test, Spearman's correlation, the chi-square test, Fisher's exact test, and binary logistic regression, ensuring the robustness of the findings. Results The study comprised 336 schoolteachers with a mean age of 43.3 ± 6.5 years, of whom 58.0% were female and 42.0% were male. About 8.0% of instructors reported CTS symptoms. Female gender (median Symptom Severity Scale (SSS): 15.0 vs. 12.0, p < 0.001; median Functional Status Scale (FSS): 8.0 vs. 8.0, p < 0.001), increased time spent writing (r = 0.237, p < 0.001 for SSS; r = 0.217,p < 0.001 for FSS), and presence of comorbidities such as diabetes (median SSS: 16.0, p = 0.002; median FSS: 8.0, p = 0.001) had a negative correlation with symptom severity (r = -0.174, p = 0.002) and functional impairment (r = -0.141, p = 0.011). Surgical therapy (median SSS: 32.0; median FSS: 24.0; p<0.001) and post-treatment symptom recurrence (median SSS: 28.0; median FSS: 22.0; p<0.001) were associated with increased severity and disability. According to binary logistic regression, increased writing time significantly predicted CTS diagnosis (OR = 1.151, 95% CI: 1.024-1.295, p = 0.018). Conclusion CTS symptoms are common among Jazan teachers, and various sociodemographic, vocational, and clinical variables influence their intensity and functional status. Ergonomic treatment, early identification, and suitable management measures are critical for preventing and mitigating the effects of CTS among teachers. Additional research is required to develop focused therapies and enhance the results of this occupational group.

Research trends related to emergence agitation in the post-anaesthesia care unit from 2001 to 2023: A bibliometric analysis.

Emergence agitation (EA) is a behavioural disturbance encountered during the recovery phase of patients following general anaesthesia. It is characterised by restlessness, involuntary limb movements, and drainage tube withdrawal and may significantly harm patients and medical staff. The mechanism of EA has not been fully understood and is still a challenging subject for researchers. We extracted relevant publications published between 1 January 2001 and 31 December 2023 on the Web of Science Core Collection platform. VOSviewer software was utilised to analyse the retrieved literature and predict the development trends and hotspots in the field. The results show that the number of publications grew annually, with China contributing the most, followed by the United States and South Korea. The co-occurrence of keywords "children," "propofol," "risk factors" are current research hotspots. Owing to its self-limiting and short-duration characteristics, EA lacks standardised clinical time guidelines and objective assessment tools, which may be the focus of future research in this field. Understanding the research hotspots and the latest progress in this field, this study will help to continuously improve the clinical understanding and management of EA, and help to timely identify environmental risk factors for EA in clinical practice.

Impact of COVID-19 lockdown on sleep patterns and the related problems among nursing students.

To evaluate the impact of the COVID-19 lockdown on sleep patterns and quality among nursing students in our college. A cross-sectional study was carried out. A total of 302 nursing students aged 18-25 years, representing both genders and various academic levels, participated in this study. A pre-validated, self-administered questionnaire was used to assess sleep quality during COVID-19 and it was distributed through various social media platforms for data collection. Female students comprised the majority (92.1%) of participants. Of 332 nursing students, 302 completed the questionnaire, yielding a 91% response rate. Statistically significant differences were observed before and during the COVID-19 lockdown regarding the need to sleep after waking, feeling refreshed upon waking, satisfaction with individual sleep patterns and experiencing restless and troubled sleep (p = 0.001). Additionally, approximately one-third of nursing students (32.9%) reported poor sleep quality during the COVID-19 pandemic, with minimal impact on the total sleep hours among the studied cohorts. The COVID-19 pandemic has statistically significant impacted nursing students' sleep quality and levels. Acknowledging these challenges and planning for providing supporting measurements is essential to ensuring that nursing students can maintain their physical and mental health, which is critical for their ability to provide quality healthcare.

Exploring the Role of Attachment and Coping Strategies in Sexual Orientation Disparities in Depressive Symptoms

ABSTRACT We tested whether sexual orientation disparities in depressive symptoms are partially explained by a path through attachment anxiety or avoidance leading to ineffective coping strategies. A convenience sample of 1290 individuals from two reliable data collection platforms (Credamo and Wenjuanxing) in China were included. Structural equation modeling was performed, with sexual orientation treated as the independent variable, ineffective coping strategies, attachment avoidance and anxiety as mediators, and depressive symptoms as the dependent variable. We found that lesbian, gay and bisexual (LGB) individuals reported higher levels of attachment anxiety and avoidance than heterosexual individuals, regardless of sex, with Cohen’s ds ranging from 0.25 to 0.76. For both sexes, LGB individuals reported more depressive symptoms than heterosexual individuals, with total effects (standardized regression coefficients) ranging from 0.22 to 0.26. More depressive symptoms reported by LGB individuals compared to heterosexual individuals were partially explained by a path involving attachment avoidance or anxiety leading to ineffective coping strategies, with indirect effects ranging from 0.04 to 0.09 (small effect sizes). Overall, our results suggest that psychological factors, such as the cognitive or emotional representations of self and others, as well as ineffective coping strategies, may partially explain sexual orientation disparities in depressive symptoms.

A local community on a global collective intelligence platform: A case study of individual preferences and collective bias in ecological citizen science.

The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.

Using mHealth Technologies for Case Finding in Tuberculosis and Other Infectious Diseases in Africa: Systematic Review.

Mobile health (mHealth) technologies are increasingly used in contact tracing and case finding, enhancing and replacing traditional methods for managing infectious diseases such as Ebola, tuberculosis, COVID-19, and HIV. However, the variations in their development approaches, implementation scopes, and effectiveness introduce uncertainty regarding their potential to improve public health outcomes. We conducted this systematic review to explore how mHealth technologies are developed, implemented, and evaluated. We aimed to deepen our understanding of mHealth's role in contact tracing, enhancing both the implementation and overall health outcomes. We searched and reviewed studies conducted in Africa focusing on tuberculosis, Ebola, HIV, and COVID-19 and published between 1990 and 2023 using the PubMed, Scopus, Web of Science, and Google Scholar databases. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review, synthesize, and report the findings from articles that met our criteria. We identified 11,943 articles, but only 19 (0.16%) met our criteria, revealing a large gap in technologies specifically aimed at case finding and contact tracing of infectious diseases. These technologies addressed a broad spectrum of diseases, with a predominant focus on Ebola and tuberculosis. The type of technologies used ranged from mobile data collection platforms and smartphone apps to advanced geographic information systems (GISs) and bidirectional communication systems. Technologies deployed in programmatic settings, often developed using design thinking frameworks, were backed by significant funding and often deployed at a large scale but frequently lacked rigorous evaluations. In contrast, technologies used in research settings, although providing more detailed evaluation of both technical performance and health outcomes, were constrained by scale and insufficient funding. These challenges not only prevented these technologies from being tested on a wider scale but also hindered their ability to provide actionable and generalizable insights that could inform public health policies effectively. Overall, this review underscored a need for organized development approaches and comprehensive evaluations. A significant gap exists between the expansive deployment of mHealth technologies in programmatic settings, which are typically well funded and rigorously developed, and the more robust evaluations necessary to ascertain their effectiveness. Future research should consider integrating the robust evaluations often found in research settings with the scale and developmental rigor of programmatic implementations. By embedding advanced research methodologies within programmatic frameworks at the design thinking stage, mHealth technologies can potentially become technically viable and effectively meet specific contact tracing health outcomes to inform policy effectively.

Developing a system for the real-time collection and analysis of mobile vehicle emission data

In this study, an integrated system for remotely monitoring air pollution data was developed. This system combines vehicle exhaust emission data with driving information. It comprises an exhaust extraction device (EED), an improved On-Board Diagnostic II (OBDII) device, a mobile app, and a backend server database. The EED detects exhaust gases such as NOx, COx, and PM2.5 and transmits this information to the OBDII device through 2.4G communication. The OBDII device gathers vehicle driving information through a CAN 2.0 interface, integrates this information with vehicle exhaust emission data, and transmits the integrated information to the app through Bluetooth. The app forwards the integrated data to the backend database through 4G/5G communication for storage. Overall, this system provides a platform for the comprehensive collection, analysis, and application of big data pertaining to vehicle pollution sources and driving behavior. Analysis of actual driving experiments involving three vehicles revealed a correlation of over 80 % between vehicles’ revolutions per minute and CO2 and PM2.5 emissions, indicating that driving behaviors affect vehicle exhaust emissions. In summary, the proposed system not only stores relevant driving information in real time but also provides a platform for the future analysis of driving behavior and vehicle exhaust emissions.