mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

Nebeker, Camille

doi:10.1177/1073110520917029

Cited by 13 publications

(15 citation statements)

References 39 publications

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“…The risks associated with big data projects also increase due to the variety of actors involved in research alongside university researchers (e.g., private companies, citizen science associations, bio-citizen groups, community workers cooperatives, foundations, and non-profit organizations) [70,71]. The novel aspect of health-related big data research compared with traditional research is that anyone who can access large amounts of data about individuals and build predictive models based on that data, can now determine and infer the health status of a person without directly engaging with that person in a research program [72].…”

Section: Novel Weaknesses: Purview Weaknessesmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Novel Weaknesses: Purview Weaknessesmentioning

confidence: 99%

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

et al. 2021

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“…And via its connection to secondary mobile devices that tether with apps, a smartphone forms an expandable platform to connect app-navigated health devices and plug-ins, such as glucose meters, electrocardiograms, ultrasound, pulse oximetry, and heart rate monitors. 36…”

Section: Mobile Devicesmentioning

confidence: 99%

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

Rothstein¹,

Wilbanks²,

Beskow³

et al. 2020

J. Law. Med. Ethics

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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.

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“…The development of technologies to support healthy aging efforts are on the rise. These technologies include wearable and remote pervasive sensors, voice activated systems, and predictive analytics, including digital phenotyping (11)(12)(13)(14)(15)(16). Digital tools and strategies are increasingly applied to health promotion, disease prevention, and treatment efforts yet, are not always tested with diverse populations.…”

Section: Introductionmentioning

confidence: 99%

“…We employed the Digital Health Checklist (DHC) and Framework ( 19 ) to guide our analysis of participant's perceptions about the use of mHealth. The DHC framework was developed in 2019 in response to uncertainties that the mHealth research community (i.e., technologists, researchers, ethicists, regulators, institutions, participants involved in the digital health research process, and other stakeholders) voiced specific to navigating new privacy, data management, and risk assessment challenges ( 11 ). The DHC is grounded in four accepted ethical principles in biomedical and behavioral research: respect for persons, beneficence, justice, and respect for law and public interest ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

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

Nebeker

Zlatar

2021

Front. Public Health

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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.

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mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

Cited by 13 publications

References 39 publications

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

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

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

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

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