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Revista Gaucha de Enfermagem 2023To verify whether nursing and medical students take measures regarding their cardiovascular health and the associated risk factors.
OBJECTIVE
To verify whether nursing and medical students take measures regarding their cardiovascular health and the associated risk factors.
METHOD
Cross-sectional study, online data collection with 413 students in February and March 2021, using specific and validated instruments. Kruskal-Wallis, chi-square and logistic regression were adopted for statistical analyses.
RESULTS
73.3% self-reported that they were healthy. We identified a higher risk for developing cardiovascular diseases in sedentary students (OR = 38.6, p < 0.001), with irregular adherence to physical activity (OR = 16.2, p < 0.001) and with a higher level of perceived stress (OR = 1.12, p < 0.001).
CONCLUSION
Students who take action to promote cardiovascular health showed lower risk compared to those who did not. If students do not value their own health during the education process, this may interfere with their professional performance after graduation.
Topics: Humans; Students, Medical; Cross-Sectional Studies; Students, Nursing; Surveys and Questionnaires; Self Report
PubMed: 37971107
DOI: 10.1590/1983-1447.2023.20230004.en -
Vaccine Mar 2024The U.S. Centers for Disease Control and Prevention (CDC) developed and implemented the CDC COVID-19 Vaccine Pregnancy Registry (C19VPR) to monitor vaccine safety.... (Review)
Review
The U.S. Centers for Disease Control and Prevention (CDC) developed and implemented the CDC COVID-19 Vaccine Pregnancy Registry (C19VPR) to monitor vaccine safety. Potential participants who received a COVID-19 vaccine in pregnancy or up to 30 days prior to their pregnancy-associated last menstrual period were eligible to participate in the registry, which monitored health outcomes of participants and their infants through phone interviews and review of available medical records. Data for select outcomes, including birth defects, were reviewed by clinicians. In certain cases, medical records were used to confirm and add detail to participant-reported health conditions. This paper serves as a description of CDC C19VPR protocol. We describe the development and implementation for each data collection aspect of the registry (i.e., participant phone interviews, clinical review, and medical record abstraction), data management, and strengths and limitations. We also describe the demographics and vaccinations received among eligible and enrolled participants. There were 123,609 potential participants 18-54 years of age identified from January 2021 through mid-June 2021; 23,339 were eligible and enrolled into the registry. Among these, 85.3 % consented to medical record review for themselves and/or their infants. Participants were majority non-Hispanic White (79.1 %), residents of urban areas (93.3 %), and 48.3 % were between 30 and 34 years of age. Most participants completed the primary series of vaccination by the end of pregnancy (89.7 %). Many participants were healthcare personnel (44.8 %), possibly due to the phased roll-out of the vaccination program. The registry continues to provide important information about the safety of COVID-19 vaccination among pregnant people, a population with higher risk of poor outcomes from COVID-19 who were not included in pre-authorization clinical trials. Lessons learned from the registry may guide development and implementation of future vaccine safety monitoring efforts for pregnant people and their infants.
Topics: Female; Humans; Infant; Pregnancy; Centers for Disease Control and Prevention, U.S.; COVID-19; COVID-19 Vaccines; Data Collection; Registries; United States; Vaccination; Vaccines; Adolescent; Young Adult; Adult; Middle Aged
PubMed: 38057207
DOI: 10.1016/j.vaccine.2023.11.061 -
JMIR Human Factors Nov 2023Digital health studies using electronic patient-reported outcomes (ePROs) and wearables bring new challenges, including the need for participants to consistently provide...
Participant Engagement and Adherence to Providing Smartwatch and Patient-Reported Outcome Data: Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Real-World Study.
BACKGROUND
Digital health studies using electronic patient-reported outcomes (ePROs) and wearables bring new challenges, including the need for participants to consistently provide trial data.
OBJECTIVE
This study aims to characterize the engagement, protocol adherence, and data completeness among participants with rheumatoid arthritis enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study.
METHODS
Participants were invited to participate in this app-based study, which included a 14-day run-in and an 84-day main study. In the run-in period, data were collected via the ArthritisPower mobile app to increase app familiarity and identify the individuals who were motivated to participate. Successful completers of the run-in period were mailed a wearable smartwatch, and automated and manual prompts were sent to participants, reminding them to complete app input or regularly wear and synchronize devices, respectively, during the main study. Study coordinators monitored participant data and contacted participants via email, SMS text messaging, and phone to resolve adherence issues per a priori rules, in which consecutive spans of missing data triggered participant contact. Adherence to data collection during the main study period was defined as providing requested data for >70% of 84 days (daily ePRO, ≥80% daily smartwatch data) or at least 9 of 12 weeks (weekly ePRO).
RESULTS
Of the 470 participants expressing initial interest, 278 (59.1%) completed the run-in period and qualified for the main study. Over the 12-week main study period, 87.4% (243/278) of participants met the definition of adherence to protocol-specified data collection for weekly ePRO, and 57.2% (159/278) did so for daily ePRO. For smartwatch data, 81.7% (227/278) of the participants adhered to the protocol-specified data collection. In total, 52.9% (147/278) of the participants met composite adherence.
CONCLUSIONS
Compared with other digital health rheumatoid arthritis studies, a short run-in period appears useful for identifying participants likely to engage in a study that collects data via a mobile app and wearables and gives participants time to acclimate to study requirements. Automated or manual prompts (ie, "It's time to sync your smartwatch") may be necessary to optimize adherence. Adherence varies by data collection type (eg, ePRO vs smartwatch data).
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
RR2-10.2196/14665.
Topics: Humans; Arthritis, Rheumatoid; Data Collection; Electronic Mail; Mobile Applications; Patient Reported Outcome Measures
PubMed: 37934559
DOI: 10.2196/44034 -
Scientific Reports Jan 2024Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection...
Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.
Topics: Humans; Artificial Intelligence; Algorithms; Data Collection; Health Facilities; Internet of Things
PubMed: 38195834
DOI: 10.1038/s41598-024-51344-9 -
BMC Research Notes Dec 2023Drone image data set can be utilized for field surveying and image data collection which can be useful for analytics. With the current drone mapping software, useful 3D...
OBJECTIVE
Drone image data set can be utilized for field surveying and image data collection which can be useful for analytics. With the current drone mapping software, useful 3D object reconstruction is possible. This research aims to learn the 3D data set construction process for trees with open-source software along with their usage. Thus, we research the tools used for 3D data set construction, especially in the agriculture field. Due to the growing open-source community, we demonstrate the case study of our palm and coconut data sets against the open-source ones.
RESULTS
The methodology for achieving the point cloud data set was based on the tools: OpenDroneMap, CloudCompare, and Open3D. As a result, 40 palm trees and 40 coconut tree point clouds were extracted. Examples of the usages are provided in the area of volume estimation and graph analytics.
Topics: Trees; Cocos; Software; Data Collection
PubMed: 38066648
DOI: 10.1186/s13104-023-06647-x -
Nursing ResearchOnline surveys have proven to be an efficient method to gather health information in studies of various populations, but these are accompanied by threats to data...
BACKGROUND
Online surveys have proven to be an efficient method to gather health information in studies of various populations, but these are accompanied by threats to data integrity and quality. We draw on our experience with a nefarious intrusion into an online survey and our efforts to protect data integrity and quality in a subsequent online survey.
OBJECTIVES
We aim to share lessons learned regarding detecting and preventing threats to online survey data integrity and quality.
METHODS
We examined data from two online surveys we conducted, as well as findings of others reported in the literature, to delineate threats to and prevention strategies for online health surveys.
RESULTS
Our first survey was launched inadvertently without available security features engaged in Qualtrics, resulting in a number of threats to data integrity and quality. These threats included multiple submissions, often within seconds of each other, from the same internet protocol (IP) address; use of proxy servers or virtual private networks, often with suspicious or abusive IP address ratings and geolocations outside the United States; and incoherent text data or otherwise suspicious responses. After excluding fraudulent, suspicious, or ineligible cases, as well as cases that terminated before submitting data, 102 of 224 (45.5%) eligible survey respondents remained with partial or complete data. In a second online survey with security features in Qualtrics engaged, no IP addresses were associated with any duplicate submissions. To further protect data integrity and quality, we added items to detect inattentive or fraudulent respondents and applied a risk scoring system in which 23 survey respondents were high risk, 16 were moderate risk, and 289 of 464 (62.3%) were low or no risk and therefore considered eligible respondents.
DISCUSSION
Technological safeguards, such as blocking repeat IP addresses and study design features to detect inattentive or fraudulent respondents, are strategies to support data integrity and quality in online survey research. For online data collection to make meaningful contributions to nursing research, it is important for nursing scientists to implement technological, study design, and methodological safeguards to protect data integrity and quality and for future research to focus on advancing data protection methodologies.
Topics: Humans; Female; Child; Infant; United States; Surveys and Questionnaires; Health Surveys; Risk Factors; Research Design; Cognition
PubMed: 37625181
DOI: 10.1097/NNR.0000000000000671 -
BMJ Open Sep 2023Data are essential for tracking and monitoring of progress on health-related sustainable development goals (SDGs). But the capacity to analyse subnational and granular...
OBJECTIVE
Data are essential for tracking and monitoring of progress on health-related sustainable development goals (SDGs). But the capacity to analyse subnational and granular data is limited in low and middle-income countries. Although Pakistan lags behind on achieving several health-related SDGs, its health information capacity is nascent. Through an exploratory qualitative approach, we aimed to understand the current landscape and perceptions on data in decision-making among stakeholders of the health data ecosystem in Pakistan.
DESIGN
We used an exploratory qualitative study design.
SETTING
This study was conducted at the Aga Khan University, Karachi, Pakistan.
PARTICIPANTS
We conducted semistructured, in-depth interviews with multidisciplinary and multisectoral stakeholders from academia, hospital management, government, Non-governmental organisations and other relevant private entities till thematic saturation was achieved. Interviews were recorded and transcribed, followed by thematic analysis using NVivo.
RESULTS
Thematic analysis of 15 in-depth interviews revealed three major themes: (1) institutions are collecting data but face barriers to its effective utilisation for decision-making. These include lack of collection of needs-responsive data, lack of a gender/equity in data collection efforts, inadequate digitisation, data reliability and limited analytical ability; (2) there is openness and enthusiasm for sharing data for advancing health; however, multiple barriers hinder this including appropriate regulatory frameworks, platforms for sharing data, interoperability and defined win-win scenarios; (3) there is limited capacity in the area of both human capital and infrastructure, for being able to use data to advance health, but there is appetite to improve and invest in capacity in this area.
CONCLUSIONS
Our study identified key areas of focus that can contribute to orient a national health data roadmap and ecosystem in Pakistan.
Topics: Humans; Data Collection; Pakistan; Reproducibility of Results; Needs Assessment
PubMed: 37734897
DOI: 10.1136/bmjopen-2023-071616 -
Healthcare Policy = Politiques de Sante Oct 2023
Topics: Humans; Appendix; Policy; Data Collection
PubMed: 37850700
DOI: 10.12927/hcpol.2023.27186 -
American Journal of Preventive Medicine Sep 2023Social determinants are structures and conditions in the biological, physical, built, and social environments that affect health, social and physical functioning, health...
INTRODUCTION
Social determinants are structures and conditions in the biological, physical, built, and social environments that affect health, social and physical functioning, health risk, quality of life, and health outcomes. The adoption of recommended, standard measurement protocols for social determinants of health will advance the science of minority health and health disparities research and provide standard social determinants of health protocols for inclusion in all studies with human participants.
METHODS
A PhenX (consensus measures for Phenotypes and eXposures) Working Group of social determinants of health experts was convened from October 2018 to May 2020 and followed a well-established consensus process to identify and recommend social determinants of health measurement protocols. The PhenX Toolkit contains data collection protocols suitable for inclusion in a wide range of research studies. The recommended social determinants of health protocols were shared with the broader scientific community to invite review and feedback before being added to the Toolkit.
RESULTS
Nineteen social determinants of health protocols were released in the PhenX Toolkit (https://www.phenxtoolkit.org) in May 2020 to provide measures at the individual and structural levels for built and natural environments, structural racism, economic resources, employment status, occupational health and safety, education, environmental exposures, food environment, health and health care, and sociocultural community context.
CONCLUSIONS
Promoting the adoption of well-established social determinants of health protocols can enable consistent data collection and facilitate comparing and combining studies, with the potential to increase their scientific impact.
Topics: Humans; Social Determinants of Health; Quality of Life; Phenotype; Data Collection; Research Design
PubMed: 36935055
DOI: 10.1016/j.amepre.2023.03.003 -
International Journal of Medical... May 2024Health and Wellbeing Living Labs are a valuable research infrastructure for exploring innovative solutions to tackle complex healthcare challenges and promote overall...
BACKGROUND
Health and Wellbeing Living Labs are a valuable research infrastructure for exploring innovative solutions to tackle complex healthcare challenges and promote overall wellbeing. A knowledge gap exists in categorizing and understanding the types of ICT tools and technical devices employed by Living Labs.
AIM
Define a comprehensive taxonomy that effectively categorizes and organizes the digital data collection and intervention tools employed in Health and Wellbeing Living Lab research studies.
METHODS
A modified consensus-seeking Delphi study was conducted, starting with a pre-study involving a survey and semistructured interviews (N=30) to gather information on existing equipment. The follow-up three Delphi rounds with a panel of living lab experts (R1 N=18, R2 - 3 N=15) from 10 different countries focused on achieving consensus on the category definitions, ease of reading, and included subitems for each category. Due to the controversial results in the 2nd round of qualitative feedback, an online workshop was organized to clarify the contradictory issues.
RESULTS
The resulting taxonomy included 52 subitems, which were divided into three levels as follows: The first level consists of 'devices for data monitoring and collection' and 'technologies for intervention.' At the second level, the 'data monitoring and collection' category is further divided into 'environmental' and 'human' monitoring. The latter includes the following third-level categories: 'biometrics,' 'activity and behavioral monitoring,' 'cognitive ability and mental processes,' 'electrical biosignals and physiological monitoring measures,' '(primary) vital signs,' and 'body size and composition.' At the second level, 'technologies for intervention' consists of 'assistive technology,' 'extended reality - XR (VR & AR),' and 'serious games' categories.
CONCLUSION
A common language and standardized terminology are established to enable effective communication with living labs and their customers. The taxonomy opens a roadmap for further studies to map related devices based on their functionality, features, target populations, and intended outcomes, fostering collaboration and enhancing data capture and exploitation.
Topics: Humans; Delphi Technique; Cognition; Surveys and Questionnaires; Self-Help Devices
PubMed: 38492408
DOI: 10.1016/j.ijmedinf.2024.105408