mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

A methodology for preprocessing structured big data in the behavioral sciences

The characteristics of big data, including high volume, increased variety, and velocity, pose special challenges for data analysis. As these characteristics generally preclude manual data inspection and processing, researchers must often use computational methodologies to deal with this type of data; techniques that may be unfamiliar to nonspecialists, including behavioral scientists. However, previous data analytics methodologies within the field of computer science, developed to handle the generic tasks of data collection, preprocessing, and analysis, can be appropriated for use in other disciplines. These methodologies involve a sequential pipeline of quality checks to prepare data sets for analysis and application. Building upon these methodologies, this paper describes the Big Data Quality & Statistical Assurance (BDQSA) model, applicable for researchers in the behavioral sciences. It involves a series of data preprocessing tasks, to achieve data understanding, as well as data screening, cleaning, and transformation. These are followed by a statistical quality phase, which includes extraction of the relevant data subset, type conversions, ensuring sample representativeness when appropriate, and assessing statistical assumptions. The resulting model thereby provides methodological guidance for the preprocessing of behavioral science big data, aimed at ensuring acceptable data quality before analysis is undertaken. Sample R code snippets demonstrating the application of this model are provided throughout the paper.

Lessons Learned: Beta-Testing the Digital Health Checklist for Researchers Prompts a Call to Action by Behavioral Scientists

Digital technologies offer unique opportunities for health research. For example, Twitter posts can support public health surveillance to identify outbreaks (eg, influenza and COVID-19), and a wearable fitness tracker can provide real-time data collection to assess the effectiveness of a behavior change intervention. With these opportunities, it is necessary to consider the potential risks and benefits to research participants when using digital tools or strategies. Researchers need to be involved in the risk assessment process, as many tools in the marketplace (eg, wellness apps, fitness sensors) are underregulated. However, there is little guidance to assist researchers and institutional review boards in their evaluation of digital tools for research purposes. To address this gap, the Digital Health Checklist for Researchers (DHC-R) was developed as a decision support tool. A participatory research approach involving a group of behavioral scientists was used to inform DHC-R development. Scientists beta-tested the checklist by retrospectively evaluating the technologies they had chosen for use in their research. This paper describes the lessons learned because of their involvement in the beta-testing process and concludes with recommendations for how the DHC-R could be useful for a variety of digital health stakeholders. Recommendations focus on future research and policy development to support research ethics, including the development of best practices to advance safe and responsible digital health research.

Learning From Older Adults to Promote Independent Physical Activity Using Mobile Health (mHealth)

Background: Healthy aging is critically important for several reasons, including economic impact and quality of life. As the population of older adults rapidly increases, identifying acceptable ways to promote healthy aging is a priority. Technologies that can facilitate health promotion and risk reduction behaviors may be a solution, but only if these mobile health (mHealth) tools can be used by the older adult population. Within the context of a physical activity intervention, this study gathered participant's opinions about the use of an mHealth device to learn about acceptance and to identify areas for improvement.

Methods: The Independent Walking for Brain Health study (NCT03058146) was designed to evaluate the effectiveness of a wearable mHealth technology in facilitating adherence to a physical activity prescription among participants in free-living environments. An Exit Survey was conducted following intervention completion to gauge participant's perceptions and solicit feedback regarding the overall study design, including exercise promotion strategies and concerns specific to the technology (e.g., privacy), that could inform more acceptable mHealth interventions in the future. The Digital Health Checklist and Framework was used to guide the analysis focusing on the domains of Privacy, Access and Usability, and Data Management.

Results: Participants (n = 41) were in their early 70's (mean = 71.6) and were predominantly female (75.6%) and White (92.7%). Most were college educated (16.9 years) and enjoyed using technology in their everyday life (85.4%). Key challenges included privacy concerns, device accuracy, usability, and data access. Specifically, participants want to know what is being learned about them and want control over how their identifiable data may be used. Overall, participants were able to use the device despite the design challenges.

Conclusions: Understanding participant's perceptions of the challenges and concerns introduced by mHealth is important, as acceptance will influence adoption and adherence to the study protocol. While this study learned from participants at studycompletion, we recommend that researchers consider what might influence participant acceptance of the technology (access, data management, privacy, risks) and build these into the mHealth study design process. We provide recommendations for future mHealth studies with older adults.

Navigating the ethics of remote research data collection

COVID-19 has accelerated broad trends already in place toward remote research data collection and monitoring. This move implicates novel ethical and regulatory challenges which have not yet received due attention. Existing work is preliminary and does not seek to identify or grapple with the issues in a rigorous and sophisticated way. Here, we provide a framework for identifying and addressing challenges that we believe can help the research community realize the benefits of remote technologies while preserving ethical ideals and public trust. We organize issues into several distinct categories and provide points to consider in a table that can help facilitate ethical design and review of research studies using remote health instruments.

Ethics review of big data research: What should stay and what should be reformed?

BACKGROUND

Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts.

MAIN TEXT

In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC's scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC's way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science.

CONCLUSIONS

We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.

Realizing Present and Future Promise of DIY Biology and Medicine through a Trust Architecture

The speed and scale of the COVID-19 pandemic has highlighted the limits of current health systems and the potential promise of non-establishment research such as "DIY" research. We consider one example of how DIY research is responding to the pandemic, discuss the challenges faced by DIY research more generally, and suggest that a "trust architecture" should be developed now to contribute to successful future DIY efforts.

Unregulated Health Research Using Mobile Devices: Ethical Considerations and Policy Recommendations

Mobile devices with health apps, direct-to-consumer genetic testing, crowd-sourced information, and other data sources have enabled research by new classes of researchers. Independent researchers, citizen scientists, patient-directed researchers, self-experimenters, and others are not covered by federal research regulations because they are not recipients of federal financial assistance or conducting research in anticipation of a submission to the FDA for approval of a new drug or medical device. This article addresses the difficult policy challenge of promoting the welfare and interests of research participants, as well as the public, in the absence of regulatory requirements and without discouraging independent, innovative scientific inquiry. The article recommends a series of measures, including education, consultation, transparency, self-governance, and regulation to strike the appropriate balance.