Quality Assurance of Health Wearables Data: Participatory Workshop on Barriers, Solutions, and Expectations

Abdolkhani, Robab; Gray, Kathleen; Borda, Ann; DeSouza, Ruth

doi:10.2196/15329

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…Currently, there exist many consumer-grade devices that promise to improve health and wellness without scientific evidence substantiating such claims; hence, there is an urgent need for clinically validated devices and wearables [ 9 , 43 ]. The quality assurance (and measurement accuracy) of technologies has been questioned before and several challenges were identified by previous studies [ 39 , 44 ]. The findings of Abdolkhani et al [ 44 ] indicated that technical and policy standards need to be developed to guarantee the quality of data generated from wearables.…”

Section: Discussionmentioning

confidence: 99%

“…The quality assurance (and measurement accuracy) of technologies has been questioned before and several challenges were identified by previous studies [ 39 , 44 ]. The findings of Abdolkhani et al [ 44 ] indicated that technical and policy standards need to be developed to guarantee the quality of data generated from wearables. Besides the need for such standards, it is important to validate the wearables by “fit-for-purpose validation” [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

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Stakeholder Perspectives on Barriers and Facilitators for the Adoption of Virtual Clinical Trials: Qualitative Study

Coert¹,

Timmis²,

Boorsma³

et al. 2021

J Med Internet Res

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Background Conventional clinical trials are essential for generating high-quality evidence by measuring the efficacy of interventions in rigorously controlled clinical environments. However, their execution can be expensive and time-consuming. In addition, clinical trials face several logistical challenges regarding the identification, recruitment, and retention of participants; consistent data collection during trials; and adequate patient follow-up. This might lead to inefficient resource utilization. In order to partially address the current problems with conventional clinical trials, there exists the need for innovations. One such innovation is the virtual clinical trial (VCT). VCTs allow for the collection and integration of diverse data from multiple information sources, such as electronic health records, clinical and demographic data, patient-reported outcomes, anthropometric and activity measurements, and data collected by digital biomarkers or (small) samples that participants can collect themselves. Although VCTs have the potential to provide substantial value to clinical research and patients because they can lower clinical trial costs, increase the volume of data collected from patients’ daily environment, and reduce the burden of patient participation, so far VCT adoption is not commonplace. Objective This paper aims to better understand the barriers and facilitators to VCT adoption by determining the factors that influence individuals’ considerations regarding VCTs from the perspective of various stakeholders. Methods Based on online semistructured interviews, a qualitative study was conducted with pharmaceutical companies, food and health organizations, and an applied research organization in Europe. Data were thematically analyzed using Rogers’ diffusion of innovation theory. Results A total of 16 individuals with interest and experience in VCTs were interviewed, including persons from pharmaceutical companies (n=6), food and health organizations (n=4), and a research organization (n=6). Key barriers included a potentially low degree of acceptance by regulatory authorities, technical issues (standardization, validation, and data storage), compliance and adherence, and lack of knowledge or comprehension regarding the opportunities VCTs have to offer. Involvement of regulators in development processes, stakeholder exposure to the results of pilot studies, and clear and simple instructions and assistance for patients were considered key facilitators. Conclusions Collaboration among all stakeholders in VCT development is crucial to increase knowledge and awareness. Organizations should invest in accurate data collection technologies, and compliance of patients in VCTs needs to be ensured. Multicriteria decision analysis can help determine if a VCT is a preferred option by stakeholders. The findings of this study can be a good starting point to accelerate the development and widespread implementation of VCTs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Stakeholder Perspectives on Barriers and Facilitators for the Adoption of Virtual Clinical Trials: Qualitative Study

Coert¹,

Timmis²,

Boorsma³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…For instance, for some MTurk experiments, disparities have been evidenced between laboratory and online data collection (Crump et al, 2013 ). Further clarifications about quality, such as consistency or interpretability (Abdolkhani et al, 2020 ), are also needed for data collected using wearables.…”

mentioning

confidence: 99%

Challenges and Opportunities for Human Behavior Research in the Coronavirus Disease (COVID-19) Pandemic

Gentili

Cristea

2020

Front. Psychol.

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“…The main limitation of this framework is that it does not include quality control measures for SDV. Abdolkhani et al [ 46 ] discussed wearable health data solutions for RWD quality control in a workshop format. However, this study only proposed 5 general solutions for the attributes of health data and has not yet formed a complete theoretical framework.…”

Section: Discussionmentioning

confidence: 99%

Clinical Source Data Production and Quality Control in Real-world Studies: Proposal for Development of the eSource Record System

Wang¹,

Lai²,

Jin³

et al. 2022

JMIR Res Protoc

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Background An eSource generally includes the direct capture, collection, and storage of electronic data to simplify clinical research. It can improve data quality and patient safety and reduce clinical trial costs. There has been some eSource-related research progress in relatively large projects. However, most of these studies focused on technical explorations to improve interoperability among systems to reuse retrospective data for research. Few studies have explored source data collection and quality control during prospective data collection from a methodological perspective. Objective This study aimed to design a clinical source data collection method that is suitable for real-world studies and meets the data quality standards for clinical research and to improve efficiency when writing electronic medical records (EMRs). Methods On the basis of our group’s previous research experience, TransCelerate BioPharm Inc eSource logical architecture, and relevant regulations and guidelines, we designed a source data collection method and invited relevant stakeholders to optimize it. On the basis of this method, we proposed the eSource record (ESR) system as a solution and invited experts with different roles in the contract research organization company to discuss and design a flowchart for data connection between the ESR and electronic data capture (EDC). Results The ESR method included 5 steps: research project preparation, initial survey collection, in-hospital medical record writing, out-of-hospital follow-up, and electronic case report form (eCRF) traceability. The data connection between the ESR and EDC covered the clinical research process from creating the eCRF to collecting data for the analysis. The intelligent data acquisition function of the ESR will automatically complete the empty eCRF to create an eCRF with values. When the clinical research associate and data manager conduct data verification, they can query the certified copy database through interface traceability and send data queries. The data queries are transmitted to the ESR through the EDC interface. The EDC and EMR systems interoperate through the ESR. The EMR and EDC systems transmit data to the ESR system through the data standards of the Health Level Seven Clinical Document Architecture and the Clinical Data Interchange Standards Consortium operational data model, respectively. When the implemented data standards for a given system are not consistent, the ESR will approach the problem by first automating mappings between standards and then handling extensions or corrections to a given data format through human evaluation. Conclusions The source data collection method proposed in this study will help to realize eSource’s new strategy. The ESR solution is standardized and sustainable. It aims to ensure that research data meet the attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available standards for clinical research data quality and to provide a new model for prospective data collection in real-world studies.

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Quality Assurance of Health Wearables Data: Participatory Workshop on Barriers, Solutions, and Expectations

Cited by 9 publications

References 14 publications

Stakeholder Perspectives on Barriers and Facilitators for the Adoption of Virtual Clinical Trials: Qualitative Study

Stakeholder Perspectives on Barriers and Facilitators for the Adoption of Virtual Clinical Trials: Qualitative Study

Challenges and Opportunities for Human Behavior Research in the Coronavirus Disease (COVID-19) Pandemic

Clinical Source Data Production and Quality Control in Real-world Studies: Proposal for Development of the eSource Record System

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