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Leveraging mobile health applications for biomedical research and citizen science: a scoping review.

ABSTRACT: Objective:This systematic review aims to analyze current capabilities, challenges, and impact of self-directed mobile health (mHealth) research applications such as those based on the ResearchKit platform. Materials and Methods:A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. English publications were included if: 1) mobile applications were used in the context of large-scale collection of data for biomedical research, and not as medical or behavioral intervention of any kind, and 2) all activities related to participating in research and data collection methods were executed remotely without any face-to-face interaction between researchers and study participants. Results:Thirty-six unique ResearchKit apps were identified. The majority of the apps were used to conduct observational studies on general citizens and generate large datasets for secondary research. Nearly half of the apps were focused on chronic conditions in adults. Discussion:The ability to generate large biomedical datasets on diverse populations that can be broadly shared and re-used was identified as a promising feature of mHealth research apps. Common challenges were low participation retention, uncertainty regarding how use patterns influence data quality, need for data validation, and privacy concerns. Conclusion:ResearchKit and other mHealth-based studies are well positioned to enhance development and validation of novel digital biomarkers as well as generate new biomedical knowledge through retrospective studies. However, in order to capitalize on these benefits, mHealth research studies must strive to improve retention rates, implement rigorous data validation strategies, and address emerging privacy and security challenges.

SUBMITTER: Schmitz H

PROVIDER: S-EPMC7647150 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Leveraging mobile health applications for biomedical research and citizen science: a scoping review.

Schmitz Hannah H Howe Carol L CL Armstrong David G DG Subbian Vignesh V

Journal of the American Medical Informatics Association : JAMIA 20181201 12

<h4>Objective</h4>This systematic review aims to analyze current capabilities, challenges, and impact of self-directed mobile health (mHealth) research applications such as those based on the ResearchKit platform.<h4>Materials and methods</h4>A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. English publications were included if: 1) mobile applications were used in the context of large-scale collection of dat ...[more]

PMID: 30445467

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Project description:BackgroundCitizen engagement in research is an emerging practice that involves members of the general public in research processes such as priority setting, planning, decision-making, research conduct, implementation, evaluation, and dissemination. Engaging citizens in research, particularly health research, increases the relevance of study findings, minimizes waste by facilitating stewardship over resources, and builds public trust in the research. While several existing frameworks guide the application of citizen engagement principles to health research, it is unclear how citizen engagement can be utilized to maximize benefits and minimize risks and challenges in health research. To address the gaps in knowledge around citizen engagement in health research, we propose a scoping review to synthesize the state of knowledge on methods to incorporate and evaluate citizen engagement in research. A protocol is presented in this manuscript.MethodsThe methodology for our scoping review is guided by Arksey and O' Malley's framework for scoping reviews, and additional recommendations by Levac and colleagues. We will include peer-reviewed and gray literature that report on citizen engagement in health research (including biomedical, clinical, health systems and services, and social, cultural, environmental and population health) and report method(s) to conduct, measure, or evaluate citizen engagement. We will systematically search electronic databases (MEDLINE, EMBASE, CINAHL, JSTOR, PsycINFO, Scopus, and Science Direct) from inception onwards and search relevant organizations' websites for additional studies, frameworks, and reports on citizen engagement. Title and abstract and full-text citations will be screened independently and in duplicate. Data will be extracted independently and in duplicate, including document characteristics, citizen engagement definitions and goals, and outcomes of citizen engagement (e.g., barriers, facilitators).DiscussionThis review will synthesize the definitions, goals, methods, outcomes, and significance of citizen engagement in health research, as well as any potential barriers, facilitators, and challenges outlined in existing literature. The findings will provide an evidence-based foundation for developing new or improved guidance for citizen engagement in health research. Overall, we anticipate that our scoping review will be a preliminary step to meaningful engagement of citizens in research and strengthen the relationship between the scientific community and the public through transparency and collaboration.Systematic review registrationOpen Science Framework https://osf.io/hzcbr .

Project description:BackgroundData provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research.ObjectiveThe aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption.MethodsFollowing a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures.ResultsWe identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV.ConclusionsThe heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.

Project description:Citizen engagement, or partnering with interested members of the public in health research, is becoming more common. While ongoing assessment of citizen engagement practices is considered important to its success, there is little clarity around aspects of citizen engagement that are important to assess (i.e., what to look for) and methods to assess (i.e., how to measure and/ or evaluate) citizen engagement in health research. In this scoping review, we included peer-reviewed literature that focused primarily on method(s) to measure and/or evaluate citizen engagement in health research. Independently and in duplicate, we completed title and abstract screening and full-text screening and extracted data including document characteristics, citizen engagement definitions and goals, and methods to measure or evaluate citizen engagement (including characteristics of these methods). Our search yielded 16,762 records of which 33 records (31 peer-reviewed articles, one government report, one conference proceeding) met our inclusion criteria. Studies discussed engaging citizens (i.e., patients [n = 16], members of the public [n = 7], service users/consumers [n = 4], individuals from specific disease groups [n = 3]) in research processes. Reported methods of citizen engagement measurement and evaluation included frameworks, discussion-based methods (i.e., focus groups, interviews), survey-based methods (e.g., audits, questionnaires), and other methods (e.g., observation, prioritization tasks). Methods to measure and evaluate citizen engagement commonly focused on collecting perceptions of citizens and researchers on aspects of citizen engagement including empowerment, impact, respect, support, and value. We found that methods to measure and/or evaluate citizen engagement in health research vary widely but share some similarities in aspect of citizen engagement considered important to measure or evaluate. These aspects could be used to devise a more standardized, modifiable, and widely applicable framework for measuring and evaluating citizen engagement in research. Two citizen team members were involved as equal partners in study design and interpretation of its findings. Open Science Framework (10.17605/OSF.IO/HZCBR).

Project description:Partnering with people most affected by mental health problems can transform mental health outcomes. Citizen science as a research approach enables partnering with the public at a substantial scale, but there is scarce guidance on its use in mental health research. To develop best practise guidelines for conducting and reporting research, we conducted a systematic review of studies reporting mental health citizen science research. Documents were identified from electronic databases (n = 10), grey literature, conference proceedings, hand searching of specific journals and citation tracking. Document content was organised in NVIVO using the ten European Citizen Science Association (ECSA) citizen science principles. Best practise guidelines were developed by (a) identifying approaches specific to mental health research or where citizen science and mental health practises differ, (b) identifying relevant published reporting guidelines and methodologies already used in mental health research, and (c) identifying specific elements to include in reporting studies. A total of 14,063 documents were screened. Nine studies were included, from Australia, Belgium, Canada, Denmark, Netherlands, Spain, the UK, and the United States. Citizen scientists with lived experience of mental health problems were involved in data collection, analysis, project design, leadership, and dissemination of results. Most studies reported against some ECSA principles but reporting against these principles was often unclear and unstated. Best practise guidelines were developed, which identified mental health-specific issues relevant to citizen science, and reporting recommendations. These included citizen science as a mechanism for empowering people affected by mental health problems, attending to safeguarding issues such as health-related advice being shared between contributors, the use of existing health research reporting guidelines, evaluating the benefits for contributors and impact on researchers, explicit reporting of participation at each research stage, naming the citizen science platform and data repository, and clear reporting of consent processes, data ownership, and data sharing arrangements. We conclude that citizen science is feasible in mental health and can be complementary to other participatory approaches. It can contribute to active involvement, engagement, and knowledge production with the public. The proposed guidelines will support the quality of citizen science reporting.

Project description:BackgroundThe novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.ObjectiveOur aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.MethodsWe searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.ResultsWe identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.ConclusionsWe see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

Project description:BackgroundMobile health (mHealth) interventions are increasingly being designed to facilitate health-related behavior change. Integrating insights from behavioral science and design science can help support the development of more effective mHealth interventions. Behavioral Design (BD) and Design Thinking (DT) have emerged as best practice approaches in their respective fields. Until now, little work has been done to examine how BD and DT can be integrated throughout the mHealth design process.ObjectiveThe aim of this scoping review was to map the evidence on how insights from BD and DT can be integrated to guide the design of mHealth interventions. The following questions were addressed: (1) what are the main characteristics of studies that integrate BD and DT during the mHealth design process? (2) what theories, models, and frameworks do design teams use during the mHealth design process? (3) what methods do design teams use to integrate BD and DT during the mHealth design process? and (4) what are key design challenges, implementation considerations, and future directions for integrating BD and DT during mHealth design?MethodsThis review followed the Joanna Briggs Institute reviewer manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Studies were identified from MEDLINE, PsycINFO, Embase, CINAHL, and JMIR by using search terms related to mHealth, BD, and DT. Included studies had to clearly describe their mHealth design process and how behavior change theories, models, frameworks, or techniques were incorporated. Two independent reviewers screened the studies for inclusion and completed the data extraction. A descriptive analysis was conducted.ResultsA total of 75 papers met the inclusion criteria. All studies were published between 2012 and 2021. Studies integrated BD and DT in notable ways, which can be referred to as "Behavioral Design Thinking." Five steps were followed in Behavioral Design Thinking: (1) empathize with users and their behavior change needs, (2) define user and behavior change requirements, (3) ideate user-centered features and behavior change content, (4) prototype a user-centered solution that supports behavior change, and (5) test the solution against users' needs and for its behavior change potential. The key challenges experienced during mHealth design included meaningfully engaging patient and public partners in the design process, translating evidence-based behavior change techniques into actual mHealth features, and planning for how to integrate the mHealth intervention into existing clinical systems.ConclusionsBest practices from BD and DT can be integrated throughout the mHealth design process to ensure that mHealth interventions are purposefully developed to effectively engage users. Although this scoping review clarified how insights from BD and DT can be integrated during mHealth design, future research is needed to identify the most effective design approaches.

Dataset Information

Leveraging mobile health applications for biomedical research and citizen science: a scoping review.

Publications

Leveraging mobile health applications for biomedical research and citizen science: a scoping review.

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