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The International Journal of Eating... Feb 2022Our original aim was to validate and norm common eating disorder (ED) symptom measures in a large, representative community sample of transgender adults in the United... (Review)
Review
OBJECTIVE
Our original aim was to validate and norm common eating disorder (ED) symptom measures in a large, representative community sample of transgender adults in the United States. We recruited via Amazon Mechanical Turk (MTurk), a popular online recruitment and data collection platform both within and outside of the ED field. We present an overview of our experience using MTurk.
METHOD
Recruitment began in Spring 2020; our original target N was 2,250 transgender adults stratified evenly across the United States. Measures included a demographics questionnaire, the Eating Disorder Examination-Questionnaire, and the Eating Attitudes Test-26. Consistent with current literature recommendations, we implemented a comprehensive set of attention and validity measures to reduce and identify bot responding, data farming, and participant misrepresentation.
RESULTS
Recommended validity and attention checks failed to identify the majority of likely invalid responses. Our collection of two similar ED measures, thorough weight history assessment, and gender identity experiences allowed us to examine response concordance and identify impossible and improbable responses, which revealed glaring discrepancies and invalid data. Furthermore, qualitative data (e.g., emails received from MTurk workers) raised concerns about economic conditions facing MTurk workers that could compel misrepresentation.
DISCUSSION
Our results strongly suggest most of our data were invalid, and call into question results of recently published MTurk studies. We assert that caution and rigor must be applied when using MTurk as a recruitment tool for ED research, and offer several suggestions for ED researchers to mitigate and identify invalid data.
Topics: Adult; Crowdsourcing; Feeding and Eating Disorders; Female; Gender Identity; Humans; Male; Research Personnel; Surveys and Questionnaires; United States
PubMed: 34562036
DOI: 10.1002/eat.23614 -
The British Journal of General Practice... Sep 2021
Topics: Access to Information; Computer Security; Data Collection; Electronic Health Records; General Practice; Humans
PubMed: 34446415
DOI: 10.3399/bjgp21X717005 -
Clinical and Translational Science Jan 2021Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated... (Review)
Review
Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.
Topics: Big Data; Biomedical Technology; Biometry; Data Collection; Humans; Monitoring, Physiologic; Remote Sensing Technology; Research Design
PubMed: 32770726
DOI: 10.1111/cts.12865 -
Journal of Clinical Epidemiology Nov 2021We aimed to map the resource use during systematic review (SR) production and reasons why steps of the SR production are resource intensive to discover where the largest... (Review)
Review
OBJECTIVE
We aimed to map the resource use during systematic review (SR) production and reasons why steps of the SR production are resource intensive to discover where the largest gain in improving efficiency might be possible.
STUDY DESIGN AND SETTING
We conducted a scoping review. An information specialist searched multiple databases (e.g., Ovid MEDLINE, Scopus) and implemented citation-based and grey literature searching. We employed dual and independent screenings of records at the title/abstract and full-text levels and data extraction.
RESULTS
We included 34 studies. Thirty-two reported on the resource use-mostly time; four described reasons why steps of the review process are resource intensive. Study selection, data extraction, and critical appraisal seem to be very resource intensive, while protocol development, literature search, or study retrieval take less time. Project management and administration required a large proportion of SR production time. Lack of experience, domain knowledge, use of collaborative and SR-tailored software, and good communication and management can be reasons why SR steps are resource intensive.
CONCLUSION
Resource use during SR production varies widely. Areas with the largest resource use are administration and project management, study selection, data extraction, and critical appraisal of studies.
Topics: Humans; Biomedical Research; Data Collection; Research Design; Research Report; Systematic Reviews as Topic
PubMed: 34091021
DOI: 10.1016/j.jclinepi.2021.05.019 -
American Journal of Public Health Aug 2021
Topics: COVID-19; Cause of Death; Data Collection; Disease Outbreaks; Epidemiological Monitoring; Humans; Statistics as Topic
PubMed: 34464190
DOI: 10.2105/AJPH.2021.306403 -
Acta Crystallographica. Section F,... Jun 2022In situ diffraction data collection using crystallization plates has been utilized for macromolecules to evaluate crystal quality without requiring additional sample...
In situ diffraction data collection using crystallization plates has been utilized for macromolecules to evaluate crystal quality without requiring additional sample treatment such as cryocooling. Although it is difficult to collect complete data sets using this technique due to the mechanical limitation of crystal rotation, recent advances in methods for data collection from multiple crystals have overcome this issue. At SPring-8, an in situ diffraction measurement system was constructed consisting of a goniometer for a plate, an articulated robot and plate storage. Using this system, complete data sets were obtained utilizing the small-wedge measurement method. Combining this system with an acoustic liquid handler to prepare protein-ligand complex crystals by applying fragment compounds to trypsin crystals for in situ soaking, binding was confirmed for seven out of eight compounds. These results show that the system functioned properly to collect complete data for structural analysis and to expand the capability for ligand screening in combination with a liquid dispenser.
Topics: Crystallization; Crystallography, X-Ray; Data Collection; Ligands; Macromolecular Substances
PubMed: 35647681
DOI: 10.1107/S2053230X22005283 -
PloS One 2022Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private... (Review)
Review
INTRODUCTION
Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals.
METHODS
The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches-an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions.
RESULTS
The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5).
DISCUSSION
Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.
Topics: Humans; Health Services; Government Programs; Hospitals, Private; Policy Making; Surveys and Questionnaires; India
PubMed: 36508431
DOI: 10.1371/journal.pone.0276399 -
World Journal of Emergency Surgery :... Jan 2021Trauma is a significant public health problem in Latin America (LA), contributing to substantial death and disability in the region. Several LA countries have...
BACKGROUND
Trauma is a significant public health problem in Latin America (LA), contributing to substantial death and disability in the region. Several LA countries have implemented trauma registries and injury surveillance systems. However, the region lacks an integrated trauma system. The consensus conference's goal was to integrate existing LA trauma data collection efforts into a regional trauma program and encourage the use of the data to inform health policy.
METHODS
We created a consensus group of 25 experts in trauma and emergency care with previous data collection and injury surveillance experience in the LA. region. Experts participated in a consensus conference to discuss the state of trauma data collection in LA. We utilized the Delphi method to build consensus around strategic steps for trauma data management in the region. Consensus was defined as the agreement of ≥ 70% among the expert panel.
RESULTS
The consensus conference determined that action was necessary from academic bodies, scientific societies, and ministries of health to encourage a culture of collection and use of health data in trauma. The panel developed a set of recommendations for these groups to encourage the development and use of robust trauma information systems in LA. Consensus was achieved in one Delphi round.
CONCLUSIONS
The expert group successfully reached a consensus on recommendations to key stakeholders in trauma information systems in LA. These recommendations may be used to encourage capacity building in trauma research and trauma health policy in the region.
Topics: Capacity Building; Data Collection; Delphi Technique; Humans; Latin America; Traumatology; Wounds and Injuries
PubMed: 33516227
DOI: 10.1186/s13017-021-00347-2 -
Journal of Medical Internet Research Jan 2021The broad availability of smartphones and the number of health apps in app stores have risen in recent years. Health apps have benefits for individuals (eg, the ability... (Review)
Review
BACKGROUND
The broad availability of smartphones and the number of health apps in app stores have risen in recent years. Health apps have benefits for individuals (eg, the ability to monitor one's health) as well as for researchers (eg, the ability to collect data in population-based, clinical, and observational studies). Although the number of health apps on the global app market is huge and the associated potential seems to be great, app-based questionnaires for collecting patient-related data have not played an important role in epidemiological studies so far.
OBJECTIVE
This study aims to provide an overview of studies that have collected patient data using an app-based approach, with a particular focus on longitudinal studies. This literature review describes the current extent to which smartphones have been used for collecting (patient) data for research purposes, and the potential benefits and challenges associated with this approach.
METHODS
We conducted a scoping review of studies that used data collection via apps. PubMed was used to identify studies describing the use of smartphone app questionnaires for collecting data over time. Overall, 17 articles were included in the summary.
RESULTS
Based on the results of this scoping review, there are only a few studies that integrate smartphone apps into data-collection approaches. Studies dealing with the collection of health-related data via smartphone apps have mainly been developed with regard to psychosomatic, neurodegenerative, respiratory, and cardiovascular diseases, as well as malign neoplasm. Among the identified studies, the duration of data collection ranged from 4 weeks to 12 months, and the participants' mean ages ranged from 7 to 69 years. Potential can be seen for real-time information transfer, fast data synchronization (which saves time and increases effectivity), and the possibility of tracking responses longitudinally. Furthermore, smartphone-based data-collection techniques might prevent biases, such as reminder bias or mistakes occurring during manual data transfers. In chronic diseases, real-time communication with physicians and early detection of symptoms enables rapid modifications in disease management.
CONCLUSIONS
The results indicate that using mobile technologies can help to overcome challenges linked with data collection in epidemiological research. However, further feasibility studies need to be conducted in the near future to test the applicability and acceptance of these mobile apps for epidemiological research in various subpopulations.
Topics: Data Collection; Epidemiologic Studies; Humans; Longitudinal Studies; Mobile Applications
PubMed: 33480850
DOI: 10.2196/17691 -
Frontiers in Public Health 2022Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical...
Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.
Topics: Data Collection; Humans; Smartphone; Telemedicine
PubMed: 36187627
DOI: 10.3389/fpubh.2022.926234