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Participate in research with Google Health Studies

A new app by google empowering you to contribute to important health research with leading institutions, simply and securely from your phone..

Health research led by scientists, with help from Google

In partnership with doctors, nurses, and health researchers, Google Health is providing secure technology that can help improve our understanding of health. Our partnerships will always be guided by healthcare experts focused on questions that are important to improving well-being.

Improving the health of your community

Just as individuals have specific healthcare needs, so do individual communities. By participating in Google Health Studies research, you can help leading institutions and researchers develop a better understanding of your community’s specific health issues and needs. Your study participation can impact the health of your region — and even the future of healthcare for all.

Digital wellbeing study

Respiratory health study, contribute to an understanding of digital wellbeing.

The second study available is a digital wellbeing study conducted by the Center for Digital Mental Health at the University of Oregon. If you participate in this study, you’ll provide data to help researchers understand how patterns of smartphone use are associated with mental and physical wellbeing.

Dr. Nicholas Allen, Ann Swindells Professor of Clinical Psychology and Director of the Center for Digital Mental Health at the University of Oregon.

“ Digital technologies are transforming every aspect of modern life, but we need to know more about how they interact with human wellbeing. This innovative study can shed new light on this question. ”

Help researchers better understand respiratory diseases

The first study available is a respiratory health study conducted by Boston Children’s Hospital and Harvard Medical School. If you participate in this study, you’ll provide data to help researchers understand how demographics, health history, behavior, and mobility patterns contribute to the spread of respiratory illnesses.

Dr. John Brownstein, professor at Harvard Medical School and Chief Innovation Officer of Boston Children’s Hospital.

“ Google Health Studies provides people with a secure and easy way to take part in medical research, while letting researchers discover novel epidemiological insights into respiratory diseases. ”

Benefit the public, in private

Protecting your information in the respiratory health study.

Your study data stays on your device

After joining a health study, you’ll begin completing weekly surveys. At all times, your individual survey responses, location history and other personally identifiable data stays on your device.

Your device computes statistics based on your study data

During the study, your device receives different queries, computes and summarizes the results based on your individual study data, and encrypts these results for subsequent aggregation with federated analytics.

Participant data gets aggregated

Encrypted summaries from many devices are combined together, using the federated analytics technology. Google and study partners do not receive any individual study data about you.

Research that values your privacy

Combined insights are sent securely to the researchers conducting the study. You can safely contribute to health research knowing your personally identifiable study data will never be available to Google or third parties.

Protecting your health information

Personal health information is extremely sensitive, which is why Google Health Studies uses privacy-preserving methods to keep your data private and protected. If you choose to participate in research with Google Health Studies, Google does not sell your study data and does not use it to show you ads. You must explicitly consent to the purposes for which it will be used. You can easily unenroll from studies at any time. And if you choose to delete the app, all study data will be deleted from your phone and no new information will be collected.

A step toward representative health research

Google Health Studies makes it faster and easier for leading research institutions to connect with study participants by taking care of the technological infrastructure. If you're interested in adding your study to the platform, get notified when the app is available for more studies.

Less than 10% of the U.S. population participates in clinical research

How does racial participation differ by geographic locations?

Whether in the United States or in the rest of the world, clinical trial participants are mostly White. The majority of Asian trial participants were at non-US sites. The representation of Black or African American participants at US sites is similar to the US general population, which is 13% Black or African American (2011 - 2015 Census)

Participate in a Google Health study today

Take one minute a week to participate in health research led and developed by leading research institutions.

Building tools to advance research and care. One app at a time.

Take your work further with researchkit and carekit..

Care providers and researchers from all over the world are producing new medical discoveries and advancing patient care with apps built using ResearchKit and CareKit – our open source code. With ResearchKit, CareKit and other tools you can develop an app that can drive health science through research at scale, or change the way you deliver care to patients beyond the confines of a lab or doctor's office. Discover how an app could take your work further and join a global community that is transforming health through technology.

ResearchKit Take research out of the lab.

With ResearchKit, you can build apps that enroll more participants and conduct studies at a scale never seen before. Move beyond the lab and reach people wherever they are for a more robust, real-world cohort.

Learn more about ResearchKit

Get started on a ResearchKit app

UNC conducts the largest study ever on postpartum depression.

Carekit get a better picture of your patients’ health..

Apps built with CareKit engage patients in their care and recovery by giving them the opportunity to track and share daily progress from their devices. Now care providers have the chance to connect with patients outside of regular visits, and gather insights to provide better care.

Learn more about CareKit

Get started on a CareKit app

Johns Hopkins reinvents cardiac patient care with Corrie Health.

Researchkit and carekit work together seamlessly..

Each framework has its own customizable modules that can be used to create an app for research or care. Whether your app is used to conduct a research study or to offer better patient care, you can utilize elements of both frameworks to create it.

• Gather informed consent
• Build personalized care plans
• Create surveys
• Visualize trends
• Conduct active evaluations
• Connect with patients

Explore the possibilities.

Capture relevant data at scale and derive valuable insights with a variety of different modules from ResearchKit and CareKit. Here is just a snapshot of what you can do.

ResearchKit

Collect informed consent with simplified consent modules.

Easily build digital questionnaires from a library of survey templates.

Measure hearing thresholds and sound exposure to evaluate hearing health.

Conduct vision tasks to get a look at visual health.

Track daily symptoms and adherence to improve patient outcomes.

Deliver care plans that stay with your patients all day.

Connect with patients outside of regular visits.

View more resources

Check out some apps built with ResearchKit & CareKit.

Corrie health.

An app designed to help patients recover from a heart attack.

Johns Hopkins Digital

An app to help plan your child’s healthcare.

Boston Children’s Hospital

Mom Genes Fight PPD

A study to understand the causes of postpartum depression.

University of North Carolina at Chapel Hill

Concussion Tracker

A research study that tracks recovery from a concussion diagnosis.

NYU Langone Medical Center

A research study of peripheral artery disease.

Stanford University

WebMD Pregnancy

Track a baby’s growth and development, week by week.

Tell us about your project.

The Research and Care community can be a resource for you to learn and share.

Go to submission page

See the latest code-along series on how to build a research and care app

Setup Onboarding

Schedule Tasks

Visualize Progress

Postpartum depression (PPD), is one of the most common complications of childbirth, and yet much remains unknown about this condition. Approximately one in seven women experience PPD and some of its most devastating consequences include maternal suicide, infanticide, and reduced maternal sensitivity leading to adverse neurodevelopmental outcomes for the child.

This led UNC School of Medicine to create Mom Genes Fight PPD (formerly PPD ACT), an iOS app they used to recruit, consent, screen, and enable DNA collection from women with a lifetime history of PPD. Mom Genes Fight PPD was designed to understand why some women are afflicted with postpartum depression and others aren’t based on genetics.

“We were able to use ResearchKit to bring PPD research to a much larger population compared to traditional studies that require participants to visit specific research centers.” — Quote by Samantha Meltzer-Brody, MD, MPH

Since its launch, the app has reached over 17,000 downloads enabling the team to tap into a huge participant population and collect approximately 4,000 genetic samples. The scale of the study also drove genome-wide association studies.

Now, UNC researchers are able to accurately determine reliability of which childbirth preceded the worst episode, participant's age at the worst episode, and duration of the worst episode in a cohort larger than any studied previously. The scale of the study, and the number of participants recruited has the team at UNC hopeful that they are well on their way to understanding the genetic signature of postpartum mood disorders. This critical knowledge will improve detection, prevention, and treatment of postpartum depression.

Modules used:

• ResearchKit Consent
• ResearchKit Surveys

Visit the Mom Genes Fight PPD website

Read the publication

The weeks following a heart attack, or myocardial infarction, are crucial to recovery. In fact, unplanned readmissions after hospitalization for acute myocardial infarction are among the leading causes of preventable morbidity, mortality, and healthcare costs.

The Corrie Health program, centered around an app created by physicians at Johns Hopkins University School of Medicine, is giving patients greater control over their recovery and reducing readmission rates. The app, which leverages ResearchKit, CareKit, and Apple Watch, allows for remote monitoring and continuous data collection so providers can have better insight into their patients’ health status and recovery progress.

This mobile health solution aims to reinvent the cardiac patient’s experience by: helping them improve skills for diet, exercise, and medication habits, connecting them to resources helpful to recovery, introducing them to mindfulness techniques for improving awareness and emotional strength, and recording data on steps, heart rate, and blood pressure. Data are captured with the help of an Apple Watch and Bluetooth blood pressure cuff that writes data directly to the Health app and is shared via HealthKit to the Corrie Health app.

• CareKit Tasks
• CareKit Contacts
• CareKit Care Plan Store

Visit the Corrie Health app website

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Google's Health Studies app lets Android users contribute to research

It's similar to apple's health research app from last year..

Google is making a major push towards medical research with its new Health Studies app for Android users . Available today , the app allows you to sign up for research projects and safely contribute data, either via surveys or sensor readings. The first study, lead by Harvard Medical and the Boston Children's Hospital, focuses on respiratory illnesses like COVID-19 and influenza.

Last year, Apple launched its own Research app for iPhone and Apple Watch users, which similarly focused on opening up health research to consumers. Both Apple and Google are solving some common issues: Medical researchers often have trouble collecting enough viable user data, and it's difficult for average consumers to contribute data.

"With COVID-19 emerging alongside seasonal respiratory pathogens, research is now needed more than ever to develop more effective treatments and mitigation strategies,” Dr. John Brownstein, professor at Harvard Medical School and Chief Innovation Officer of the Boston Children’s Hospital, said in a statement. “Google Health Studies provides people with a secure and easy way to take part in medical research, while letting researchers discover novel epidemiological insights into respiratory diseases.”

Given the potential sensitivity with health research, Google says the data you contribute is encrypted and stored securely on your device . You'll also be able to see exactly what you're contributing, and studies won't be able to access data beyond what you consent. Google also claims your research data won't shared or sold to advertisers, unlike most of its products. The app relies on federated learning and analytics to handle your data, which lets researchers gather aggregated and anonymized data from many devices at once.

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• Published: 03 February 2020

Beyond validation: getting health apps into clinical practice

• William J. Gordon 1 , 2 , 3 ,
• Adam Landman 2 , 3 , 4 ,
• Haipeng Zhang 3 , 5 , 6 &
• David W. Bates 1 , 3  

npj Digital Medicine volume  3 , Article number:  14 ( 2020 ) Cite this article

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Fueled by advances in technology, increased access to smartphones, and capital investment, the number of available health “apps” has exploded in recent years. Patients use their smartphones for many things, but not as much as they might for health, especially for managing their chronic conditions. Moreover, while significant work is ongoing to develop, validate, and evaluate these apps, it is less clear how to effectively disseminate apps into routine clinical practice. We propose a framework for prescribing apps and outline the key issues that need to be addressed to enable app dissemination in clinical care. This includes: education and awareness, creating digital formularies, workflow and EHR integration, payment models, and patient/provider support. As work in digital health continues to expand, integrating health apps into clinical care delivery will be critical if digital health is to achieve its potential.

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What is the clinical value of mHealth for patients?

Digitizing clinical trials

Mobile app validation: a digital health scorecard approach

Digital technology offers tremendous potential for improving the prevention, diagnosis and management of disease. The proliferation of health apps in particular—there are now more than 300,000 which have been developed 1 —is changing how patients interact with the healthcare system. Through apps, patients can have immediate access to their health data, schedule virtual visits with their providers, integrate with devices like blood pressure cuffs, manage medication dosing, improve wellness, and many other health-related activities. Apps are also increasingly accessible especially via smartphones—more Americans now have a smartphone than a desktop or laptop computer. 2

Health apps exist on a spectrum: from consumer facing, non-regulated, non-interventional apps like fitness trackers, to regulated, prescription-only apps like a digital therapeutic to manage substance use disorder. Some are standalone apps, while others require connection to an external device, like an inhaler. Here, we consider apps that are used by individuals, typically on a mobile device such as a smartphone or smartwatch, to support many aspects of health—including, diagnosis, treatment, and monitoring. Similar to the US Food and Drug Administration (FDA) definition of a “Mobile Application,” we define “apps” as software applications that may run on a variety of mobile platforms or be web-based but optimized for mobile devices. 3 There is significant work underway to develop apps, build an evidence base, validate functionality, create standards for development, and design frameworks for app evaluations. 4 , 5 , 6 , 7 , 8 Capital is pouring in—more than $9 billion was invested by venture capital and private equity towards digital health companies in 2018, $2 billion more than 2017. 9 Additionally, the FDA recently launched a Digital Health Innovation Action Plan. 10 A key component of this plan is a new regulatory pathway for certain software devices called “precertification” that is intended to streamline approval by focusing on application developers and processes as opposed to specific products. 11 , 12 In 2018, the World Health Organization published guidelines on digital health interventions, 7 and recently, the UK National Institute for Health and Care Excellence published an evidence framework for digital health technologies. 13

However, while advancements have been made in the development, validation, and regulation of apps, it is less clear how to disseminate appropriate interventions to patients and providers. 14 The sheer number of apps available, each with varying functionality, complexity, impact, and cost, creates substantial barriers to the diffusion of these apps into clinical care. Furthermore, the evidence base supporting the use of even the best apps is scant, and most apps do not deliver value, especially for patients who are sick or have chronic conditions. 15 Some apps may even be harmful. 16 Yet even if high-quality apps are developed, the potential of apps to improve the care and wellbeing of patients can be realized only if the tools are actually used. If they are to be effective, both patients and providers need to gain value from utilizing these tools. It is also critical that they connect with other digital applications such as the electronic health record, yet most do not today.

One potential solution is to frame apps like non-digital treatment modalities, such as medications. If apps could be “prescribed” to patients through existing workflows, patients and clinicians may be more likely to use them, and patients could be steered through the maze of apps today towards ones which are most likely to be beneficial. Such a model promises better integration of apps into clinical practice, but also raises new issues around awareness, process, technical support, and payment.

In this review, we first describe the current regulatory environment in both the US and Europe around apps and mobile technology. We then discuss the importance of validation, and how to ensure apps that are used in clinical practice have been appropriately validated. We then build a framework for prescribing apps and outline the key issues that need to be addressed—along with potential solutions—to truly enable apps to become a core component of clinical care.

Regulatory environment

Both the US FDA and the EU, largely through the European Medicine Agency (EMA), have long recognized the importance of software’s role in diagnostic and therapeutic devices. 17 , 18 More recently, to address the increasing importance of digital health, the US FDA launched a Digital Health Innovation Action Plan. 10 Run through the Center for Devices and Radiological Health, the plan includes several areas of updated or new guidance, additional personnel and digital health expertise at the FDA, and a “precertification” program to streamline device approvals by focusing on developers and processes as opposed to specific products. The FDA considers two main subsets of device software functions: Software as a Medical Device (SaMD) and Software in a Medical Device (SiMD). Software that ultimately meets the definition of a device can be deployed on a mobile platform, at which point it is defined as a “mobile medical app.” 3 As such, in the US, regulation of mobile apps follows guidelines set by the Federal Food, Drug, and Cosmetic Act (FD&C Act), recently updated by the 21st Century Cures Act to exclude certain software capabilities. In September 2019, the FDA updated its guidance on Device Software Functions and Mobile Medical Applications to further clarify its oversight role for software. 3 Importantly, the guidance documentation indicates that the FDA will not enforce requirements for software (and thus medical apps) that (1) help patients self-manage their disease without suggesting specific treatments, and (2) automate simple tasks for healthcare providers. In the US, additional examples of federal oversight include the Federal Trade Commission’s Health Breach Notification Rule and the Health Insurance Portability and Accountability Act. 19

Similarly, in Europe, there have been multiple efforts to provide frameworks and regulatory best practices in this space. The European Commission eHealth Action Plan 2012–2020, for example, set out a policy roadmap and “digital agenda” for eHealth in Europe. 20 Regulation (EU) 2017/745 on Medical Devices (MDR), which began in 2017 and will fully apply in May 2020, is the most recent EU framework for medical devices. As in the US, “apps” are vehicles for software deployment, and thus whether software qualifies as a device is independent of where it is deployed. 21 The EU General Data Protection Regulation (GDPR), which went into effect in May of 2018, also provides important guidance for app developers, particularly around data protection and privacy.

Clinical value and validation

Traditional regulatory models have an important role in making certain apps available for clinical care, and we anticipate that these regulated apps will also have evidence supporting their use, such as a randomized control trial. However, as noted above, the majority of apps will not be tightly regulated. For these apps, different models of validation will be needed so that clinicians and patients can understand which apps deliver value and which do not—i.e. some measure of quality and safety, like clinical benefit, patient satisfaction, potential adverse effects, provider burnout mitigation, or cost-effectiveness. While some apps have been rigorously studied, there is a general dearth of evidence for health apps, both because a small percentage have been studied, and evidence tends to be low quality for those that have been studied. 8 , 22 , 23 , 24

Numerous strategies have been proposed to improve the validation and trustworthiness of apps, such as having a voluntary accreditation agency that will “certify” apps, provider-based efforts to validate apps, or independent third-party reviewers, among others. 8 , 25 , 26 While consumer ratings of health apps might seem to be a scalable methodology of validating apps, prior work suggests that consumer ratings poorly reflect clinical utility and usability. 15 Given the scale of non-regulated app development, we anticipate that multiple strategies will be needed, and many stakeholders will be involved. Clinicians will play an integral role, particularly given the trust that many patients place in their providers. Additionally, many apps will be marketed directly to patients. For these apps, industry and existing regulations (like the Federal Trade Commission in the US) will be essential for ensuring a base level of quality and minimizing erroneous claims. Researchers will play multiple roles—from providing the primary data to support the clinical utility of an app, to uncovering unvalidated and dangerous apps. Though many groups will be involved, the medical community needs to work with the regulatory authorities to help define best practices and identify robust approaches for app validation.

Education and awareness

Despite the challenges in developing and validating apps, we anticipate that over time more and more apps will clear these hurdles and become appropriate for clinicians to bring into clinical practice. A first step in disseminating apps for clinical use is increasing education and awareness of the available technologies for clinicians. While pharmacology is a core component of medical education, few clinicians receive formal digital health training 27 , though there are efforts to improve this through new initiatives like the American Medical Association’s “Accelerating Change in Medical Education Consortium.” 28 Other educational approaches are needed to reach practicing clinicians, such as formally requiring digital health training as part of ongoing professional education or certification programs, like Continuing Medical Education, Maintenance of Certification, or board certifications.

Education is particularly salient for prescription-only FDA-regulated apps, which may have specific indications or pre-requisites for appropriate use, like an online cognitive behavioral therapy tool to support outpatient substance use disorder programs. 29 There are several potential ways to implement this required training. First, special licensing could be required for prescribers, similar to a practice waiver for buprenorphine therapies. While this would create a substantial barrier to more widespread app prescriptions, it would create an ecosystem of mandatory certification and training, which might be beneficial if an app is particularly complex. Lighter-touch approaches, like vendor or hospital-led educational courses, would be another method of disseminating training. App vendors could maintain registries of “certified” providers, which would have the added benefit of alerting interested patients to clinicians that are familiar with a particular app.

Patients can also benefit from education and awareness. As apps become more prevalent, we expect more direct-to-consumer marketing of apps, which is not surprising given the vast majority of apps are designed for consumer usage. 30 App “stores” represent the primary repository of health apps, and patients rely on how these stores sort and present apps for what is most relevant. However, prior work has also shown that consumer ratings do not correlate with clinical utility. 15 One proposed solution involves using an “app label” (similar to a nutrition label for food) to help consumers understand the technology and data used by the app. 1 Regardless of the strategy, ensuring appropriate messaging will be critical, which may require governmental regulatory action, similar to efforts around traditional medication marketing. 31

Digital formularies

Traditional formularies are lists of drug products that enable a provider organization, pharmacy, or payer to distinguish between preferred or non-preferred drugs based on several factors including cost and clinical value. 32 In some settings, formularies dictate what medications are available to be prescribed; in other settings, formularies are used to get lower prices—for example, preference for a particular proton-pump inhibitor based on negotiation. Given the sheer number of available apps, a formulary for health apps—a “digital formulary”—could be an important mechanism for enabling apps to disseminate into clinical care, and would be valuable for the entire spectrum of apps—monitoring, diagnosis, therapeutics, etc. Express Scripts, a large US-based pharmacy benefits manager (PBM), recently announced a digital health formulary. 33 CVS Health also recently announced a product that would enable its PBM customers to manage third-party health products. 34 Outside the US, the United Kingdom’s National Health Service has launched an “Apps Library,” which is a curated list of health apps, with pricing information, for patients and providers to search for apps which may benefit them.

Digital formularies serve multiple purposes. First, they provide a short list of available apps, and providers could search these formularies and know what was available for a specific diagnosis or purpose—far more manageable than sifting through 300,000-plus apps in an app store. Second, from a safety perspective, a digital formulary could utilize a higher bar for listing an app than the app store, and could for example include apps which have received regulatory approval, or apps with clear evidence supporting their use. Third, digital formularies could enable streamlined coverage and pricing workflow, like traditional medication formularies. Fourth, digital formularies could provide a mechanism for patients and providers to know which apps would be supported by the provider organization and might for instance interoperate with the EHR they use. Organizations could even offer online, telephone, or in-person support for listed apps.

Digital formularies have two main risks. First, while formularies should overall result in lower pricing for patients, there could also be situations where prices go up, for example, through exclusivity deals, or simply because the digital formulary contains an older, more expensive app, and a newer, cheaper one is available. Second, while narrowing the set of available apps will be helpful, digital formularies could also slow diffusion of apps into practice. If providers and patients rely on these formularies as their primary repository, newer, more effective apps will need to overcome the hurdle of getting into a digital formulary to be used. Thus, one of the main advantages of apps—the speed at which they can be developed and propagated—could be limited by digital formularies. However, this would likely also incent app developers to perform trials of their technologies which could make it easier to identify which are beneficial.

Workflow and EHR integration

Many providers spend less than 20 min with a patient per outpatient visit, with numerous competing interests for what gets addressed during these visits. 35 , 36 Since managing apps will be another competing interest, the process of prescribing apps to patients must be integrated into current provider workflow in order to scale—otherwise, usage will be limited to early champions who dedicate time to learning how to disseminate these apps to their patients, but won’t be accessible to the majority of clinicians who may not prescribe apps because that process exists outside their current workflow.

There are several steps that must be accomplished to integrate apps into provider workflow. First, apps should be searchable (for example, from a digital formulary), and then orderable, from the EHR, just like a medication. Second, apps should be integrated with clinical decision support systems to ensure appropriateness. Third, providers should have the ability to note the indication for the app—why it is being prescribed for that patient—so that other providers understand why the app is being used. This information could also be brought into provider documentation for that patient, either in the medication history or in a new “digital tools” section of a patient note. Fourth, providers should be able to enter the “sig”—or label for the prescription. Fifth, providers should be able to prescribe parameters for the app, which can be loaded automatically once the patient downloads the app. Sixth, the app prescription should be visible in the EHR like other prescriptions, so that other providers know this app has been prescribed and ideally whether or not it is being used by the patient. Seventh, data generated by the app should be accessible to patients and providers, ideally through existing communication channels like patient portals or other interoperability channels. The prescribing provider might need to commit to following any app-related output, for example, through a service-level agreement. Finally, there should be mechanisms to de-prescribe an app, for example if the app is no longer supported, used or indicated. Table 1 highlights each of these steps, with a clinical example.

The implementation of workflow-based app EHR integration should follow existing paradigms that are in use for traditional medications, as outlined in the smoking cessation app example (Table 1 ). Exceptions include app-specific concerns that will require more customization, like how to add parameters to a prescription. Enabling bidirectional data flow (so that the app can provide data back to providers and vice versa) would require new processes and new data integrations. At our institution, for example, we work with a vendor that aggregates all incoming device data into a separate patient-specific area of our EHR. Additionally, federally certified EHRs, as a result of national incentive programs and the 21st Century Cures Act, are now required to allow patients to download their data directly through Application Programming Interfaces (APIs), which provides an important, provider-independent mechanism for data exchange. 37 , 38 , 39

Many apps may be useful for a short period, like preceding a colonoscopy or a surgical procedure. Others may be prescribed for chronic diseases—for the latter both the patient and provider should be able to track how often the patient is engaging with the app and how this relates to control of their illness. Importantly, many apps will be used by patients independent of provider recommendation or prescription (similar to an over-the-counter medication). There should also be mechanisms for providers to indicate in the EHR that a patient is using an app, for what indication, and for how long. Ideally this could be done automatically by a patient. Technologies to enable some of these features are starting to emerge; some examples include vendors such as Xealth and Rx.Health. We anticipate that EHRs will increasingly enable this as core functionality as well.

Payment models

Apps, like other diagnostics and therapeutics, will need to be paid for. In the simplest model, app fees are paid by patients. However, as apps become more complex, and pass through regulatory hurdles, we expect prices to increase, and patients will seek payment coverage (through public or private payers as appropriate) for these apps. Without reimbursement models, app adoption will struggle as many patients will be unable to pay for them.

Establishing clear reimbursement pathways for apps is critical for several reasons. First, if a pathway for reimbursement is not established, apps could be free, but rely on advertisements or data mining as primary revenue sources. In addition to significant privacy concerns, this will also make apps less useable by patients and create suboptimal incentives for app development. Second, given that apps are usually optimized for smartphones, there is risk of exacerbating a digital divide between patients that have smartphones and those that do not—apps that then are inaccessible due to cost will only deepen that divide. Third, without clear reimbursement models, app makers may be less incentivized to innovate and create new technologies. Finally, apps present unique reimbursement challenges, for example, how updates or new software versions are managed and paid for.

Fortunately, there are also opportunities to innovate. For example, apps could have “trial” periods that would allow a patient to try it out before committing. Payment could also be used to incentivize usage—for example, removing copays if patients demonstrate app usage or achieve app-related outcomes. Accountable care organizations might pay for apps for at-risk patients if the app could reduce total medical expense for those patients. Other apps could follow traditional payment models, like using CPT codes that are reimbursed by insurers. 40 , 41 , 42 Apps could also be packaged with other medical products, like drugs or devices. Regardless, establishing clear payment and reimbursement models will be essential for apps to become a larger part of medical care.

Patient and provider support

Finally, as apps become part of clinical workflow, we anticipate an increasing need for user support. Most medications require minimal training—patients are given pills, with instructions on when to take them, with occasional nuance around timing (for example, taking thyroid replacements 60 min before breakfast). Some medications require more education. Inhalers, for example, are commonly used incorrectly, which can lead to worse outcomes. 43 Pharmacists represent an additional expert resource available to patients with questions about medications. Apps may require even more education. Providers will need to know how to use an app they are prescribing and will need to stay abreast of updates. We expect patients to reach out to clinicians for assistance with prescribed apps, so providers and office staff will need new workflow for managing these types of interactions. Additionally, entirely new professional groups could form with expertise in this area. The recently formed Digital Medicine Society 44 is one step in this direction.

Similarly, patients will need resources for support. The Ochsner health system created the “O Bar”—a physical space that patients can go to obtain recommended digital interventions as well as troubleshoot digital devices. The O Bar includes mechanisms to test apps and receive digital devices, like Bluetooth-enabled glucose monitors or wireless scales. 45 Similar efforts have been implemented at MedStar Health in Maryland, Morristown Medical Center in New Jersey, and Sibley Memorial Hospital in Washington D.C. 46 App vendors also play a role here, and many have their own dedicated support functions. Another possibility is to use the support structure that hospitals have in place for their patient portals. Regardless, building an infrastructure to support installation and usage of apps will be essential as apps become more commonly used.

Digital health, and “apps” in particular, holds tremendous potential for improving health outcomes. But while hundreds of thousands of apps have been developed, most of them to date are rarely used, not clinically validated, and have not been integrated into practice on a broad scale. We outline five key areas that need further development for apps to be integrated into clinical practice and to bridge the divide between the potential of apps and actual clinical use.

We have focused on apps, but there is a broader world of digital health that will face many of the same challenges. Examples include hardware that are dependent on apps, like an app-based portable EKG, or continuous glucose monitors that feed results back to an app. Similarly, the FDA is exploring its role in regulating Prescription Drug-Use Related Software (software that is used in conjunction with a medication, which could be used to send administration reminders, track intake, etc.) 47 Data aggregators, like Apple’s Health Records on iPhone 48 enable other apps to utilize EHR data, and could become requirements for specific types of apps to function. In all cases, careful consideration as to how these technologies go from validation to use will be essential.

Limitations

While apps remain a promising new care delivery tool, there are additional factors that need to be addressed. First, apps usually require smartphones or tablets. Patients who are unable to afford, access, or use these technologies will be unable to benefit from this technology. There is strong evidence that certain populations already experience this digital divide—for example, patients with low health literacy are less likely to use health information technology, 49 minorities and patients of lower socioeconomic status are less likely to use patient portals, 50 and users of mobile technology specifically are more likely to be younger, higher educated, male, and reside in zip codes with higher median income, 51 , 52 though there is evidence that smartphone ownership is increasing in low-income and low-education populations. 53 Further work is needed to understand how to close this divide further so that all populations are able to benefit from new digital health tools. Another important consideration is how these apps will impact clinician burnout, which has become an increasing concern amidst evidence of harm to both clinicians and patients. 54 , 55 EHRs are often linked to physician burnout, 56 , 57 so more digital tooling could exacerbate this concern. However, health apps could also lessen physician burnout, if, for example, apps streamlined communication or made follow up less time intensive. Regardless, more work is needed to understand how apps will affect clinician burnout.

As work in digital health continues to expand, we expect more apps to become available, some of which will have evidence of efficacy and regulatory approval. Development and validation are just the first steps. For apps to be used, they must be integrated into clinical practice. We have outlined some of the key areas that will need to be addressed: education and awareness, digital formularies, workflow integration, payment models, and patient/provider support. Integrating apps into routine clinical practice will be essential for digital health to achieve its full potential.

Data availability

Data available on request from the authors.

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Google launches health research app

The first study will look at respiratory illnesses like covid-19.

By Nicole Wetsman

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Google announced Wednesday that it is launching a new research app for Android phones, which would allow anyone with a device to participate in medical studies. The first study run through the app, called Google Health Studies, will look at respiratory illnesses like the flu and COVID-19.

Participants in the study will use the app to report any respiratory symptoms, the precautions they’re taking to prevent disease, and whether they’ve been tested for COVID-19 or the flu. The app will collect demographic data , like age, gender, and race as well. “Researchers in this study can examine trends to understand the link between mobility (such as the number of daily trips a person makes outside the home) and the spread of COVID-19,” Google wrote in a press release .

The app will send data to researchers using a technique called federated learning, which will batch aggregated trends from multiple devices, rather than pull information from each participant individually.

Health Studies is Google’s answer to Apple’s Research app , which runs on iOS devices. Last year, it launched studies on menstrual cycles, mobility and heart health, and hearing. Apple also lets researchers build their own iPhone apps through its ResearchKit program .

Studies run through the app will come with the same caveats as research from other commercial wearable products: they can only enroll people who can purchase products like an Android phone. Aggregated data is a good way to protect privacy, but it means researchers aren’t able to take a granular look at information.

Android phone users have a lower median income than iPhone users, which might be a benefit for Google Health Studies. “Android represents probably a more diverse dataset [than iPhone]. We’re pretty excited about the ability to leverage that,” John Brownstein, a chief innovation officer of Boston Children’s Hospital working on the study with Google, told Stat News .

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Navigating the complexities of mobile medical app development from idea to launch, a guide for clinicians and biomedical researchers

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With today’s pace of rapid technological advancement, many patient issues in modern medicine are increasingly solvable by mobile app solutions, which also have the potential to transform how clinical research is conducted. However, many critical challenges in the app development process impede bringing these translational technologies to patients, caused in large part by the lack of knowledge among clinicians and biomedical researchers of “what it takes” to design, develop, and maintain a successful medical app. Indeed, problems requiring mobile app solutions are often nuanced, requiring more than just clinical expertise, and issues such as the cost and effort required to develop and maintain a well-designed, sustainable, and scalable mobile app are frequently underestimated. To bridge this skill set gap, we established an academic unit of designers, software engineers, and scientists that leverage human-centered design methodologies and multi-disciplinary collaboration to develop clinically viable smartphone apps. In this report, we discuss major misconceptions clinicians and biomedical researchers often hold regarding medical app development, the steps we took to establish this unit to address these issues and the best practices and lessons learned from successfully ideating, developing, and launching medical apps. Overall, this report will serve as a blueprint for clinicians and biomedical researchers looking to better benefit their patients or colleagues via medical mobile apps.

Peer Review reports

Smartphones are driving a technological and patient-centric revolution in healthcare [ 1 , 2 ]. The technological sophistication and ubiquitous use of modern smartphones are increasingly enabling app-based solutions to clinical problems [ 3 , 4 , 5 ]. For example, smartphones are commonly used to enhance the patient-physician relationship by allowing patients to send images to their physician remotely for condition tracking and monitoring [ 6 , 7 , 8 ]. This increase in mobile medical solutions is apparent in the literature, where an exponential increase in academic publications available on PubMed can be seen (> 5000 manuscripts published in 2021 alone) [ 9 ]. However, there are currently major systemic barriers in place in the field of academic medicine that hinder the development of more sophisticated solutions [ 10 ].

Misconceptions in mobile medical app development

Despite the advances in medical technology and the increased availability of mobile app solutions in medicine, there are major misconceptions among clinicians involved in building a mobile app (Fig.  1 ).

Common misconceptions of medical mobile app development

Misconception #1—the investigator fully understands the problem they are trying to solve and the users they intend to help

Before the development process can start, the competitive landscape should be assessed to confirm that there is not already an identical or similar goal solution. Next, the intended users must be interviewed to ensure that the solution solves a relevant problem for them. This user research should be incorporated throughout the development process to assure that the users are continually being considered. An effective team must also be formed with the required expertise to handle every phase of the development lifecycle, from clinical problem identification, user research, design, development, and product release. Finally, a strategy must be developed to market the product so that users discover and adopt the app.

Misconception #2—app development does not require much effort

A common phrase we hear among our clinical colleagues when we are approached with an idea for a mobile medical solution is “…and I’d like to build an app.” This statement can be interpreted as, “I would like to hire a contractor to turn my idea into an app.” This line of reasoning underestimates the investigator commitment required to turn an idea into a mobile app. Throughout the development process, the investigator must play an active role in the development team. This process frequently takes months of user research, design, development, and testing in an iterative fashion.

Misconception #3—the app should easily integrate into the electronic health records (EHR) system

The electronic medical records in the USA and many countries are highly fragmented, with individual hospital systems using their own 3rd party vendors to manage their records [ 11 , 12 ]. Accessing a single institution’s EHR requires multiple levels of approvals at the principal investigator and administrative levels and then requires significant technical collaboration with the institution’s information technology department to connect. While Fast Healthcare Interoperability Resources (FHIR) standard has been adopted to facilitate a consistent format of healthcare information for simple transmission and sharing of data, the lack of a consistent EHR framework significantly impacts the ability of a 3rd party app to integrate into the EHR’s of a geographically diverse user base [ 13 ].

Misconception #4—app development is a single-step “one and done” process

The development process is not complete when the app is launched. A continuity plan must be in place to maintain and update the app throughout its lifecycle. Technology changes rapidly, operating systems update, and smartphone manufacturers develop and release multiple new phone models annually. An app must be maintained to adapt to these advancements. The request to “build an app” does not accurately convey that app development is a process that does not end with an app launch.

These misconceptions underscore a clear need for a liaison between clinicians and the app development community to facilitate the development of robust, maintainable, mobile apps. To address these misconceptions, we set up an academic sub-unit within the Georgia Clinical and Translational Science Alliance (Georgia CTSA), which we call the AppHatchery, with the goal of supporting clinical investigators in their endeavors to create and clinically translate their mobile medical app ideas. Care was taken to structure the AppHatchery to enable this end goal, with a mix of relevant expertise and experience. This initiative is led by a physician scientist who runs a laboratory that has a proven track record of developing and publishing medical apps as well as conducting clinical research using apps. We hired a product manager from the industry with expertise in managing mobile app development teams for a large organization. We have a designer on the team who specializes in user research and design thinking. Finally, we have multiple full-time developers to carry out mobile development work. We structured the group this way to ensure that we had members of the team with expertise in each phase of medical app development, specifically (1) clinical research expertise, (2) design, and (3) mobile app development. Given these diverse skillsets with every team member having some exposure to both clinical science and app development, we have created the perfect team to bridge the skillset gap between clinicians and software developers and develop mobile medical apps. Furthermore, the existence of team members fully dedicated to this initiative allows us to maintain continuity throughout each stage of the project at hand.

We first rely on internal marketing for project solicitations. When a project is onboarded, we perform a multi-disciplinary research process where we identify and explore the clinical and practical need for the given solution and its place within the current regulatory landscape and market. We use a combination of design thinking (DT) and human-centered design (HCD) framework to tackle app development challenges [ 14 ] from ideation through delivery. The design research process begins by conducting a deep dive into the problem as it pertains to end users and key stakeholders. Target users are interviewed to better understand their challenges, and we identify and/or verify the important features and aspects of the proposed app and get feedback on the design requirements that our research has identified. Simultaneously, we begin working with the principal investigator (PI) to craft the research study that will determine the efficacy of the tool we develop. Next, we begin the app development process, beginning with the design of wireframes and culminating with software development, where we conduct user research throughout the process to iteratively improve the app. We finally transition to launch and support, where we work with the principal investigator to release the app and support the clinical validation study. In this manuscript, we will describe the lessons learned from our journey and make recommendations for how other medical app developers should operate to successfully develop and translate clinical app ideas.

App development principles and structure

Design approach.

What makes our approach to building digital tools unique in the clinical space is our emphasis on both DT and HCD (Fig.  2 ). We use DT to focus on understanding who the users are, uncovering their needs, and empathizing with their situation first before kicking off the product design and development process [ 15 ]. In addition, we strategically consider user needs in relation to clinical objectives to form design recommendations [ 16 , 17 ]. We then kick off the development phase which incorporates HCD fundamentals, bringing the end user’s voice and opinions into the development process. Collaborative strategies are noted as vital to sustaining growth in mobile health applications [ 18 ]. HCD has been demonstrated to be an effective innovation tool in the digital clinical and health space where a focus on end users (patients, clinicians, or others) during the design process improves adoption rates [ 19 , 20 , 21 ] as well as patient engagement and satisfaction [ 22 ].

Design thinking framework

A recent narrative review of 82 health innovation papers mentioning HCD found most (70%) discussed using an HCD approach and all included a focus on users’ needs and the participatory and iterative nature of the design process [ 14 ]. More salient limitations to the HCD approach include sampling bias and small sample size as users must opt into the research to participate in the design process and are not necessarily the profile of users who may need the app the most. This may lead to a misalignment of the design with the intended user’s needs. Finally, few apps have been tested in randomized controlled trials, and so limited evidence is available on the long-term benefits of app products arising from HCD approaches [ 23 ]. Specifically, in using HCD, there are instances where the approach did not lead to significant improvement in the intervention outcomes. For example, Gallagher et al. conducted a study using a game-based app that was co-designed to improve cardiovascular risk factors and lifestyle behaviors and found no improvement after the intervention [ 24 ]. Similarly, Haque et al. evaluated a user-centered mHealth app in promoting physical activity and found very low compliance at the end of the study which limits the generalizability of the results [ 25 ]. Furthermore, alternatives to DT and HCD such as user experience (UX) design frequently focus on the way users interact with apps and strive to provide the most convenient use for the people who actually use the app, rather than the ideal users that HCD and DT strive for [ 26 ]. In light of these findings, it is important to note that HCD methodology allows for subjectivity in terms of which aspects of user experience to prioritize, which can result in varied outcomes. Additionally, as previously discussed in the literature, HCD is susceptible to sampling bias, which can impact the apparent effectiveness of an intervention. Thus, it is crucial to consider both the strengths, limitations, and alternatives to DT and HCD and to employ a multi-disciplinary approach that considers various perspectives and factors in the design and evaluation process. This design strategy accurately defines the user, their needs, the problem most important to solve for that user, how those needs intersect with research goals, and which design directions are most effective at maintaining engagement and delivering results. It provides the foundation for the team who then focuses on the users and their needs throughout the app project development process.

Building a team

A development team must be created to begin the development process. A software development team has three distinct yet overlapping roles, the product manager, the designer, and the developer. In addition to these key roles, multiple supporting roles may be created for handling regulatory requirements necessary in medical app development.

Product manager

The product manager is responsible for the overall strategy of the mobile app. This includes the macro strategy of why and how to build the app in the first place as well as a more detailed strategy about which features to build, what order to build these features, and how to measure if the features are a success. The product manager is also responsible for creating the requirements in sufficient detail so the remainder of the team can execute their jobs effectively. These might be technical requirements, design requirements, or requirements from the clinical team. The product manager is also responsible for the lifecycle of the mobile app. This includes releasing the app through testing platforms, releasing the app publicly, working through approval processes at the institution, and maintaining the app’s public information on the app stores.

The product manager is responsible for ensuring the app meets guidelines. This is important especially for medical apps that may need to comply with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) [ 27 ]. In our experience, projects are hindered at the beginning stages by focusing on these compliance issues instead of focusing on the core value of the mobile app. Compliance needs to be addressed at the beginning of a project when architecture is being designed and before the app is released publicly to ensure it meets all the required standards for privacy and security. The product manager is responsible for analytics and measuring the mobile app experience. These analytics are tied to the goals and objectives of the app (e.g., reducing missed follow-up visits) but also user experience metrics such as app crashes, screen views, and user interactions.

Software developer

The developer is responsible for writing the software code that will control the mobile app. This role is critical as there will be no software created without team members who can code the mobile app.

The designer is the empathizer in chief; it is their responsibility to always be the voice of the user throughout the app development process. This is a critical role as academic medical apps are often conceived without direct consultation of patients or users.

Other team roles

Developing mobile apps within an academic medical center requires knowledge of how Institutional Review Boards (IRB) review requests to conduct clinical research. It is useful to have an IRB expert on the team to answer nuanced questions about data collection and privacy that may differ from IRB to IRB and can be unique to clinical research involving mobile apps. While not critical in the early clinical research phases, it can be useful to have a regulatory expert on the team who is familiar with the relevant regulation, specifically around HIPAA and managing protected health information (PHI).

Full time vs part-time staff

Many academic medical centers operate on a shared time model where the staff dedicate a portion of their time to projects. While this can work, we have found it is better to have full-time staff dedicated to the development of mobile apps. Likewise, many software development projects are outsourced to other groups. While this can work, we have found it is best to work cohesively as a team throughout the development process to streamline communication and align expectations throughout.

Applying the app development process

Competitive and regulatory landscape survey and literature review.

Before accepting any new project, it is important to meet with the PI to understand their clinical and application vision before conducting an extensive search of the major app stores to see what similar apps currently exist, as well as related platforms, tools, books, and/or educational materials in the space. After the search is completed, the list of these competing resources should then be presented to the PI to discuss how or if the value proposition of their idea merits the effort to build a homegrown solution. If the concept meets the acceptance criteria set by the team (i.e., is novel, addresses a real need, and the development team has capacity and knowledge), the project can proceed (Fig.  3 ).

App project development process

When researching and deciding the intended use case of the app, research into the regulatory pathway required to market the product must be conducted. In the United States of America (USA), the US Food and Drug Administration (FDA) approves medical devices for sale in the US market. FDA guidance regarding software as a medical device (which includes medical apps) is governed specifically by the 21st Century Cures Act [ 28 ]. Section 3060 of this legislation defines what software the FDA considers to be a medical device that requires regulatory approval via the 510(k) premarket notification pathway, versus lifestyle tools that can immediately enter the market [ 28 ]. Ultimately, the distinction is determined based on the function and intended use of the software. Software becomes a medical device when the intention and function are to diagnose, cure, treat, prevent, or mitigate a specific disease or condition [ 28 ].

Should the app require premarket notification, multiple steps must be taken to satisfy the complex risk management strategies and regulatory requirements set by the FDA to release the app publicly. Rigorous clinical testing must be conducted to ensure that the device performance (1) is in line with the claimed performance and use case and (2) is similar in performance to the most analogous device currently approved and on the market (the claimed predicate device that must be identified for FDA submission). In addition to performance, the app must apply appropriate human factors and usability engineering processes, which must be assessed in standalone usability studies with at least 15 users in the desired user population [ 29 ]. Finally, a quality management system (QMS) must be in place to ensure that procedures and processes enable a reliable, safe, and effective software tool [ 30 ]. The QMS begins with the leadership and management plan to ensure that relevant stakeholders manage the medical app throughout its lifecycle. The support plans for the system must also be defined, with appropriate documentation of all development and design, disaster recovery plans, and processes for regularly updating the app for performance, usability, cybersecurity, and technology modernization. The implementation must be then considered, with regular validation, cybersecurity monitoring, and maintenance of all key infrastructure, to ensure that end users can interact with a stable version of the medical app throughout the duration of its lifecycle as a medical device.

Design strategy

Once engaged on a project, a development team should schedule a design strategy session with the investigator and project team to gain a full understanding of the desired goal of the app from the perspective of all stakeholders (Fig.  4 ). While investigators often have a passionate idea about what problem they wish to solve with their app, additional examination can help dial into precisely who will use their tool and what problems their app user(s) seek to solve. To prepare for this session, which is usually 1–2 h, the team conducts secondary research from published academic papers, review articles, and meta-analyses to familiarize themselves with the outcome goals of the project and specific aims of the research. The team generates clarifying questions for the investigator and research team to respond to. Sample questions may include the following:

Who will use this app? Is the patient or a family member the primary user?

What benefit will the user get out of using this app?

What data (if any) do you expect the app to collect?

Collaborative design strategy discussions set the parameters of the project. Left: a design strategy session in person. Right: results of a design strategy session using an online collaboration tool like Miro

Post-It notes or a collaborative whiteboard digital tool such as Miro (Miro, Inc., San Francisco, CA) may be used to capture ideas that can be sorted and prioritized as seen in the image below. This allows the team to gather insights from the session to form a user research approach to test assumptions, write and vet a discussion guide, and then recruit 5–15 individuals from the intended user group to interview. Insights across all interviews are pooled together and sorted to surface themes and areas of importance for the user. As the themes and key features emerge from the insights, the team can refine the insights into design recommendations.

Once the research team has a clear idea of the needs and wants of the users, designs for the app should be created (Fig.  5 ). It is very important that these designs come from actual user insights, rather than from pre-conceptions the research team had prior to beginning the work, unless these pre-conceptions had been validated by the user research.

From sketches to wireframes. Left: pictures of preliminary sketches of the layout of the application. Right: functional (clickable) wireframes of an app

Given the number of apps that exist already, it is worth spending some effort looking to find similar apps that incorporate design patterns that could be implemented in the app. This is a separate step from the competitive analysis where rather than looking at apps that perform a similar function, you are looking for individual features (i.e., how search functions, how do other apps offer content layout).

Taking from the design recommendations generated in the prior phase. The designer can begin creating low-fidelity sketches, which they can turn into generalized flows and wireframes of the app (Fig.  5 , left). The key is the use of wireframing and prototyping as an early way to evaluate whether the tool being created meets users’ expectations. To evaluate those wireframes, standard usability testing techniques can be used such as remote or in-person unmoderated observation sessions or task-based evaluations where the user is given tasks within the app to accomplish.

App development

The most common app development methodologies are the waterfall and agile methods [ 31 ]. The agile methodology is an iterative approach that leverages frequent releases that provide incremental functionality improvements that can be tested and refined in the production environment. The waterfall methodology completes the holistic build which then moves into a testing phase and then a launch phase, yielding a polished product. We prefer to utilize the agile methodology as the iterative development process enables more testing and feedback, which matches our design philosophy.

When an app development methodology has been selected, there are 2 major mobile app development frameworks: natively on each of the major operating system software tools (iOS or Android), or cross-platform (i.e., using React Native (Meta Platforms Inc., Menlo Park, CA), Flutter (Google LLC, Mountain View, CA), etc.) which supports one code base for both iOS and Android platforms (Table 1 ). The decision on what to use will largely depend on the expertise of the available developers in the team. This is the fundamental choice an app developer must make before developing an app.

Native development has the advantages of dedicated functions and features specific to the hardware you are developing for (iOS vs Android), with the downside that effort must be duplicated to support both iOS and Android devices. Also, native development tends to be more well-maintained, as it is linked directly to the mobile devices operating system. This skillset is also much more readily available in the software developer talent pool. While cross-platform development is typically much faster and more efficient with respect to developer resources, it also relies on a much smaller developer community. Furthermore, many 3rd party tools common in app development (e.g., analytics, advertisements) have limited support for cross-platform development. The development framework should be chosen to suit the needs of the specific project.

Throughout the development process, it is generally good practice to beta test the app to ensure that what is being built is meeting the user’s expectations. To do this, it is best to test in a controlled environment to monitor who is using your app. To do this you will use either TestFlight for iOS (Apple In., Cupertino, CA) or Google Play Store Internal Testing for Android (Google LLC, Mountain View, CA).

Once the app is launched, it must be closely monitored for bugs, unforeseen issues for users, and ever-increasing software versions. Both iOS and Android release beta versions of the updates in advance of a public release so that developers have a chance to test the compatibility of their apps against these updates. Nevertheless, there are many apps that are no longer supported by new OS versions that plague the app stores, a situation that should certainly be avoided by a medical app developer! Some thought should be put towards maintaining support for the users that may not have updated to newer operating system (OS) versions. It is typical to set a supported version that dates back at least a few years to give users time to upgrade their devices.

Updates and maintenance

Finally, app maintenance must be considered. Funds must be allocated to support the continuity of the app development project before it begins. As described by Siegel et al. [ 10 ], there are a multitude of unsupported apps on the app stores that are born out of research projects and then abandoned, likely due to the lack of personnel to support them, lack of funds, or poor design and execution leading to a lack of traction to justify a larger time/monetary investment.

Simultaneously with the design thinking process, it is important to work with the PI to help design a research study and secure IRB approval for the study protocol that will be used to clinically validate the app upon release. This validation study typically occurs in 2 phases: Phase 1 is focused on usability and feasibility. In this phase, user feedback of the app is collected and used to iteratively improve the app. This can be done by identifying users to enroll in the clinical study and conducting a rolling enrollment process where a version of the app is developed and given to the users to collect their feedback, adjust the app, and then repeat. Phase 2 is a clinical trial where the app’s performance is analyzed in terms of a clinical endpoint. In this phase, a randomly controlled trial should be planned to assess the app in terms of a clinical endpoint of interest, i.e., a control (no app) and test (has the app). These results can then be published in a medical journal to provide credibility to the app as a medical tool.

Lessons learned: roadblocks, pivots, and best practices

App development costs.

Funding for academic biomedical mobile apps can be difficult in our experience. The most common source of funding is through traditional National Institutes of Health (NIH) grants. These grants allow for setting aside resources in the budget to support mobile app development but do have some pitfalls. First, grants typically have a very long application and funding cycle, in the order of 12–18 months, which can delay a project. Second, funding amounts set aside for development tend to be small. In our experience, this has been $10,000–25,000 which is insufficient for the necessary costs associated with designing and developing an application (Tables 2 and 3 ). Beyond grants, academic medical centers often offer access to other seed or startup funds in the form of innovation awards, local organizations that offer awards or seed funds, and, as a last resort, projects can be funded through department funds if the funds and scope of the project are aligned.

Staffing an academic medical center app development project is very different from a commercial software development project. The key differences are (1) the budget of the project is typically far lower (by an order of magnitude, typically), (2) the goal of the app is often non-commercial (e.g., it does not have a business model attached), and (3) the goals of the PI may include education and training for the full team, beyond only executing on the creation of the software. Therefore, staffing for mobile app projects will use a mix of resources available within academic medical centers and their affiliates. These include graduate or undergraduate students who may work on a project for free as part of a classroom learning experience, students who are hired outright or work through graduate research assistant positions, shared staff with other programs (e.g., bioinformatics), and partnership with other campus-based groups who are funded to support clinical research.

Managing investigator expectations

We often encounter requests that are in essence to “just build an app” from clinical PIs. It is important to diagnose this way of thinking early as it could reveal the propensity of a PI to disengage from the project prematurely, as it becomes clear the amount of effort and collaboration it takes to foster a successful partnership. PIs must be engaged often and richly with the team. The cadence of meetings will be project dependent, but we have found a weekly update, whether a meeting or email, maintain project members active and engaged.

Having a disengaged PI can lead to multiple points where the project can fail: (1) the problem is not defined appropriately, (2) the appropriate user research is not conducted (which can lead to the app not addressing all relevant user needs thus impacting adoption and use), and (3) usability/feasibility testing is not conducted in a robust and iterative fashion (which can lead to usability issues that will prevent users from adopting the app, even if the app has a strong value proposition that could significantly improve the life of the user). In addition, it taxes the rest of the team who must make clinical research decisions and navigate an unfamiliar research structure.

Understanding the users

It can be challenging to identify and access users to solicit feedback from. Clinician investigators typically have frequent interactions with the target user population. They have familiarity and a relationship built already (which is how the ideas for projects usually originate). However, they usually lack the time to conduct thorough user research that goes beyond the problem discovery. On the other hand, developers and designers do have the time and knowledge to conduct user research, albeit they lack access to patients. Bridging this gap, often by facilitating access to clinics and hospitals for the design team, can drastically improve a project’s chance of success. In one of our projects with Grady Memorial Hospital, we established a cadence of going to the hospital twice a week to enroll new subjects and check in on existing subjects. Furthermore, fostering a relationship with the clinical care teams of the subjects facilitates subject enrollment, a process that is much slower when only investigators are involved in subject recruitment. Additionally, by fostering that relationship with the clinical care of the subjects, it is possible to conduct user research with stakeholders through observation sessions and contextual inquiry. Which can help mitigate some of the overreliance on user input [ 32 ].

Beta testing recruitment

Recruitment can take place in person or virtually. Online recruitment tends to require less apparent overhead (although depending on the clinic setting it may be hard to capture patients online) while in-person recruitment has a higher overhead but tends to yield more numbers and more upfront engagement. Most of the recruitment that we have done has taken place in person at the hospital. Interestingly, we have found that subjects recruited in a clinical setting will remember everything about the app and the study until they leave the hospital/clinic, when study participation significantly wanes. To increase the chances of success, you must find the best method to follow up with your subject/users which could be via email, text, or phone calls so that you can maintain engagement with your study. This will be very study specific and dependent on the app you build. This is one area where sampling bias can impact the project as subjects who are more interested are more likely to respond and be engaged. That was our experience with that project, and we had very low compliance from the users we were trying to target, those who leave the hospital and do not return.

Beta testing process

Testing an app with users frequently is crucial to ensure the best possible version of the app is developed. In an in-patient setting, we have found it necessary to introduce users to the app in one session, and then follow up within a week, to see if the patient interacted with the app after the visit. There are multiple steps that can be taken to foster engagement:

Delay the follow-up: A week might not be enough time for the user to have a need to open the app.

Include push notifications: This system allows researchers to send messages through the app to specific segments of the study group (i.e., users who have not opened the app recently).

The goal of beta testing is to find encounter any bugs and errors and more importantly if the intended user audience finds the app useful. It is very challenging to design a research study surrounding a tool that will fit into a user’s life outside of the study. This is because study coordinators are actively looking for subjects to enroll, creating unnatural motivators to utilize the tool (i.e., participate in research, monetary compensation, curiosity), as opposed to an organic app search where users are looking for something. Thus, it is important to design an app such that the app fills a clearly defined and currently unmet need for a group of people (i.e., a medical reference guide, an appointment booking system, a prescribed training program). Otherwise, you run the risk of aiming to change a given behavior which has been identified as a limitation of traditional HCD and follows a different design process [ 32 ].

Furthermore, mobile apps have standardized evaluation frameworks such as the System Usability Scale (SUS) which provide a quantitative evaluation of the usability of a software tool. Note the difference between usability: Can somebody use the app without encountering errors that worsen their experience, and feasibility? Does the app effectively cover an unmet need for a user? Usability is an easier metric to quantify, and mobile apps for research should follow industry standards to validate the tools created are usable. These frameworks can be employed to glean valuable feedback during the beta testing process.

Conclusions

Overall, we have developed and validated an organization within a clinical research institution that fostered communication between clinicians and app developers and enabled the creation of robust, sustainable medical apps that are currently available on the App Stores and being tested in clinical settings. This organization is the first of its kind and can be used as a blueprint for research institutions to use to facilitate the translation of medical app research and transform the digital health landscape worldwide.

Availability of data and materials

Not applicable.

Abbreviations

Electronic health records

Fast Healthcare Interoperability Resources

Clinical and Translational Science Alliance

• Design thinking
• Human-centered design

Principal investigator

User experience

Health Insurance Portability and Accountability Act of 1996

Institutional Review Board

Protected health information

United States

United States Food and Drug Administration

Quality management system

Operating system

National Institutes of Health

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This work was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR002378.

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Robert G. Mannino, Santiago J. Arconada Alvarez, Morgan Greenleaf, Maren Parsell & Wilbur A. Lam

Emory University School of Medicine, Atlanta, USA

Robert G. Mannino, Morgan Greenleaf, Maren Parsell & Wilbur A. Lam

School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA

Santiago J. Arconada Alvarez

Global Health Informatics Institute, Lilongwe, Malawi

Comfort Mwalija

Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, USA

Wilbur A. Lam

Aflac Cancer and Blood Disorders Center of Children’s Healthcare of Atlanta, Atlanta, USA

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Contributions

R.G.M., S.J.A.A., M.G., M.C.P., C.M., and W.A.L. wrote and edited the manuscript and participated in various stages of the app development and design process. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Robert G. Mannino or Wilbur A. Lam .

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Apple Introduces Powerful New Medical Research Apps

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Using digital technologies in clinical trials: current and future applications

Carmen rosa.

1 National Institutes of Health, National Institute on Drug Abuse, Bethesda, MD, USA

Lisa A. Marsch

2 Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, USA

Erin L. Winstanley

3 West Virginia University, School of Medicine and Rockefeller Neuroscience Institute, Department of Behavioral Medicine and Psychiatry, Morgantown, West Virginia, USA

4 West Virginia University, School of Medicine, Department of Neuroscience Morgantown, West Virginia, USA

Meg Brunner

5 Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, USA

Aimee N. C. Campbell

6 New York State Psychiatric Institute, Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, NY, USA

In 2015, we provided an overview of the use of digital technologies in clinical trials, both as a methodological tool and as a mechanism to deliver interventions. At that time, there was limited guidance and limited use of digital technologies in clinical research. However, since then smartphones have become ubiquitous and digital health technologies have exploded. This paper provides an update to our earlier publication and an overview of how technology has been used in the past five years in clinical trials, providing examples with varying levels of technological integration and across different health conditions. Digital technology integration ranges from the incorporation of artificial intelligence in diagnostic devices to the use of real-world data (e.g., electronic health records) for study recruitment. Clinical trials can now be conducted entirely virtually, eliminating the need for in-person interaction. Much of the published research demonstrates how digital approaches can improve the design and implementation of clinical trials. While challenges remain, progress over the last five years is encouraging, and barriers can be overcome with careful planning.

1.0. INTRODUCTION

Who remembers checking the yellow pages to find a local plumber? Or making a collect call home? Today most of us own at least one digital device (desktop computer/laptop, cell phone/smartphone, tablet, smartwatch) and use the Internet to communicate, seek information, and conduct many other activities throughout a typical day. Further, a growing number of individuals are using smartphones to stay online almost constantly, and social media is the means by which many people connect, socialize, and share news ( Pew Research Center, 2019b & 2019c ). In the last decade, a variety of digital technologies (electronic technologies that utilize mobile devices and/or the Internet) have emerged that facilitate the delivery of healthcare around the world ( Bhavnani et al., 2016 ; Agrawal & Prabakaran, 2020 ; Trifan et al., 2019 ). Development and use of digital tools, such as mobile health (mHealth), wearable devices, telehealth, and other information technologies, continues to grow and offer innovative approaches to health care. This has been particularly pronounced during the current COVID-19 pandemic where the use of weekly virtual health care visits for Medicare beneficiaries increased from 13,000 before the COVID-19 pandemic to 1.7 million in April 2020 ( Verma, 2020 ).

Randomized controlled trials (RCTs) are considered the gold standard for providing evidence of the effectiveness of clinical interventions for the prevention and treatment of human conditions and diseases where there is a need for pharmacologic, behavioral, or combination approaches. RCTs using traditional in-person approaches are often highly complex, expensive, time-consuming, and burdensome for both staff and participants ( Califf & Rutherford, 2018 ). Five years ago, we provided an overview of the use of digital technologies in clinical trials, specifically in relation to participant recruitment and retention, delivery of interventions, and data collection ( Rosa et al., 2015 ). We outlined the promise of digital technologies in designing and implementing clinical trials (e.g., to reach a broader population of potential participants and make trials more efficient and less costly) but concluded that most researchers were not incorporating digital technology into clinical trials at that time. Challenges to technology adoption in clinical trials were largely related to concerns about participant privacy/confidentiality, ensuring an adequate infrastructure, confirming participant identity, and assuring the accuracy of data. Despite these challenges, we noted that access to the Internet and mobile technologies was increasing steadily and that most individuals of all ages were using technology in their daily lives, providing strong support for leveraging technology to advance clinical trial conduct.

In the five years since our overview was published, digital health technologies have exploded. The majority of the world now has access to mobile devices, totaling about 8 billion mobile phone subscriptions worldwide ( Jonsson et al., 2019 ), and access is high even among the most traditionally underserved and vulnerable populations ( Collins et al., 2016 ; Naslund et al., 2017 ). Hence, it is not surprising that digital technologies are increasingly being integrated into clinical trials operations. The integration of digital technologies into clinical trials ranges from the incorporation of artificial intelligence in diagnostic devices ( Abramoff et al., 2018 ) to the use of real-world data (e.g., electronic health records (EHR)) to trial outcomes ( Ma, et al 2019 ). A growing number of clinical trials can also be conducted completely virtually, without any in-person interactions ( DeFrancesco, 2013 ; Turakhia et al., 2019 , Steinhubl et al., 2019 ; Inan et al., 2020 ). Challenges to technology adoption (such as issues with participant privacy/confidentiality and data accuracy) still remain, however some these challenges have been addressed through the emergence of ethical, regulatory, privacy, and data standards, as well as new technology that addresses data integrity. Many Institutional Review Boards (IRBs) and regulatory agencies have issued guidance on the use of digital tools for clinical studies. Also, guidance is available for investigators interested in, and utilizing data from, existing registries and administrative databases ( James et al., 2015 ; U.S. FDA, 2018 ; U.S. FDA, 2016 ).

In this paper, we use the term “clinical trial” as it is defined by clinicaltrials.gov : a study that involves human research participants to add to medical knowledge (including both interventional and observational studies) ( U.S. National Library of Medicine, n.d. ). We conducted a review of select published clinical trials within the last five years that used and/or discussed some form of digital tool to illustrate progress in incorporating digital technologies, as well as highlighting ongoing challenges. We also considered publications from observational studies, literature reviews, and expert perspectives that we believe will be beneficial to investigators planning to use digital technologies in their trials. The purpose of this paper is to provide a general overview of the use of digital technologies in clinical trials and other clinical research conducted in the past five years and to provide a foundation for the application of these technologies for clinical researchers interested in incorporating them into their work. The objectives are to (1) present a range of examples of how technology is currently being integrated into clinical trials, highlighting innovative digital tools and practices that could advance their utilization in clinical trials processes; (2) provide an overview of ethical considerations and data standards in the conduct of clinical trials using digital therapeutics; and (3) discuss the potential advantages and limitations of digital technologies in clinical trials research.

2.0. INTEGRATING DIGITAL TECHNOLOGIES INTO CLINICAL TRIAL DESIGN AND OPERATIONS

Clinical trials are the cornerstone for generating evidence, however current trials are still overly costly, complex, and lengthy ( Lauer et al., 2017 ; Califf & Rutherford, 2018 ). Digital technologies offer opportunities and solutions that can lead to reductions in cost, minimize complexity, and reduce burden ( NASEM, 2019 ; Steinhubl et al., 2019 ; Ali et al. 2020 ). In this section, we discuss digital technologies that could be used for clinical trial procedures such as recruitment and enrollment, participant consent, delivery of interventions, data collection, and data management. We provide some examples from recent studies of largely phase III clinical trials and observational studies that utilize digital technologies. These examples cover multiple health conditions and include studies that are completely virtual (i.e., no in-person procedures), as well as those that use a combination of digital and traditional approaches for trial operations. Traditional approaches usually require participants to travel to study clinics for recruitment, delivery of study interventions, data collection, and other study procedures.

The authors of these papers provide their experiences, best practices, recommendations, and/or limitations that could be useful to other researchers making decisions about the use of digital tools in their studies. We also reviewed important resources that provide guidance, recommendations, and best practices including Clinical Trials Transformation Initiative (CTTI) Digital Health Trials Program ( CTTI, n.d. ) with recommendations for the use of mobile technology and electronic records and programs from regulatory agencies, such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) ( U.S. FDA, 2020 ; U.S. FDA, 2018 ; U.S. FDA, 2017 ; U.S. FDA, 2017b ; U.S. FDA, 2016 ; EMA, n.d. ).

This literature underscores the need for study teams to plan as carefully and as early as possible, as there is no single best approach that applies to all types of clinical trial design. The goal is not to simply transform processes from paper to digital form, but rather to rethink the overall design and conduct of a trial from a digital perspective and re-engineer aspects of the trial related to participants ( Steinhubl et al., 2019 ). It is important to recognize the need for appropriate infrastructure to support these digital trials and to consider the cost of investing in such infrastructure. We provide considerations and some best practices when planning a clinical trial that uses digital technology in Table I . These considerations are a combination of our own perspectives after reviewing the selected literature and elements adapted from CTTI Digital Health Trials Program white papers and recommendations ( CTTI, n.d. ).

Best Practice Considerations When Desgining Trials Using Digital Technologies 1

One key aspect to consider when planning a trial is the recruitment and retention of participants. Recruitment is considered the main cause of delays in a clinical trial, and difficulties in recruitment cause about 30% of study failures ( Ali et al., 2020 ; Blatch-Jones, 2020 ). Fewer than 10% of patients in the U.S. enroll in a clinical trial, and rates of enrollment are even lower among some racial/ethnic communities ( Thompson & O’Regan, 2018 ). A recently published paper ( Frampton et al., 2020 ) provides a systematic map of digital tools used in the last 10 years for the purpose of recruitment and retention. Their review of 105 studies in the U.S and other countries provided characteristics of the digital technologies used and features of the studies that evaluated the effectiveness of these digital tools. Internet sites and social media were the tools most frequently used, largely for the purpose of raising awareness of the trials and assisting investigators in the identification of potential participants. Even though the authors concluded that more rigorous research is needed regarding the efficiency of the tools, their paper provides information regarding the types of technologies and approaches that may be useful to investigators when planning their clinical trials. Another study conducted in UK ( Blatch-Jones, 2020 ) found that database-screening tools were the most widely used and effective for recruitment. Similar tools (social media, text messaging, direct e-mails) were also utilized for recruitment and retention, with various levels of effectiveness.

2.1. Mobile technologies

In our previous publication ( Rosa, et al, 2015 ) we discussed a variety of digital tools that researchers have adopted for recruitment and retention, such as Internet, direct e-mail, texting and social media. Since then, researchers are increasingly using social media in clinical trials to improve outreach, and education ( Watson, et al 2018 , Poblete & Nieto, 2020 ; Maggio et al., 2019 ) as well as delivery of interventions ( Park, 2019 & 2020 , Radovic et al. (2017) .

The general population is increasingly comfortable with engaging in a variety of activities on smartphones, including downloading and using health and wellness mobile apps ( Carlo et al., 2019 ). Mobile technology (smartphone applications, wearable devices, telehealth) offers an opportunity to enhance the efficiency and reach of clinical trials processes, including remote consenting, evaluation of interventions, and collection of outcome data ( Perry et al., 2018 ; Marra et al., 2020 ).

Electronic Consent.

Mobile technology is a promising tool for the informed consent process. It provides a platform that is easy to use and allows for broad reach of participants while preserving the informed consent principles ( Simon, et al, 2018 , Lunt, et al, 2019 ). A 2017 systematic review ( Moore et al., 2017 ) was conducted on the use of electronic consenting (eConsent) for research apps, including information presented and shared, and privacy policies. The review summarizes the eConsent process and procedures to safeguard privacy in 34 app-based studies. Doerr and colleagues (2017) examined participant experience using a self-administered eConsent in the mPower study for Parkinson’s disease and identified useful lessons that could be incorporated as best practices. In a more recent publication, Doerr and colleagues describe creating an eConsent procedure in the All of Us study that takes into consideration participant perspectives as well as federal and local regulations ( Doerr et al., 2019 ).

Intervention.

Several recent publications illustrate the use of mobile technology as a tool to deliver interventions and monitor study and medication adherence ( Marsch et al., 2020 ; Blatch-Jones et al., 2020 ; Steinhubl et al., 2019 ; Holtyn et al., 2019 ). Many researchers are studying mental health, addiction, or other types of health behavior interventions delivered using mobile apps. As one example, Nomura et al. (2019) and Masaki et al. (2020) evaluated the use of an app, paired with an external device, for smoking cessation in a clinical trial. In their study, they compared the CureApp Smoking Cessation (CASC) system, which included the CASC smartphone app, web-based management software, and a mobile carbon monoxide checker, to a control group that received a minimally supportive app. Both groups (584 participants) received standard face-to-face counseling and pharmacological smoking cessation treatment for 12 weeks. The study concluded that the CASC system, combined with standard of care, improved continuous tobacco abstinence rates long-term. Similar promising outcomes from mobile interventions targeting other health domains have been well-documented. For example, Wilhelm and colleagues (2020) describe ways to use the Internet, phones, and apps to provide cognitive-behavioral therapy to those with mental health disorders.

Data Collection.

Mobile technologies offer an opportunity to capture, store, manage, and transmit large volumes of data, as well as for the collection of outcomes measured by mobile devices. However, as illustrated by Perry et al. (2018) , investigators are not widely adapting technologies to collect outcome data in clinical trials. In their review, they categorize and consolidate information on using mobile devices to collect and assess outcomes. In addition, Cornet & Holden (2018) conducted a systematic review of smartphone-based passive sensing data (data captured directly from smartphone sensors) and reported benefits as well as challenges of using these tools. They also provide recommendations for investigators planning to use these approaches for data collection. Finally, Russell et al. (2020) discuss statistical and other considerations when selecting mobile technologies to measure outcomes.

Other studies are using innovative combinations of different mobile technologies to conduct trials, or combinations of technology and traditional in-person approaches for more hybrid designs. For example, the Adaptable study ( Johnston et al., 2016 ; Pfaff et al., 2018 ) compared the effectiveness of two doses of aspirin in preventing ischemic adverse events, combining digital approaches (smartphone, mobile apps, EHR data source) with traditional in-person visits for recruitment and data collection. In contrast, in the Apple Heart study ( Perez et al., 2019 ; Turakhia et al., 2019 ) used smartphones, mobile apps, sensors, wearable devices, and telehealth to conduct entirely remote operations and data collection with 420,000 individuals with atrial fibrillation.

Similarly, the mPower study ( Bot et al., 2016 ) utilized a study app paired with wearable devices to conduct an observational trial with over 9,000 participants with Parkinson’s disease. The Million Veteran Program ( Gaziano et al., 2016 ) and the All of Us study (Denny et al., 2019) are very large observational cohorts (1 million participants) that are using mobile technology for all study procedures and data collection. Once established, investigators could leverage this same structure and resources for future RCTs to answer critical questions and generate additional evidence. These mobile technology approaches are also being used to collect data for regulatory Phase IV trials ( Li et al., 2016 ; Foroughi et al., 2018 ).

2.2. Electronic health records (EHR), registries, and administrative databases

The FDA’s definition of real world data (RWD), data relating to patient health status and/or the delivery of health care, notes a range of sources, including electronic health records (EHR), claims and billing databases, product and disease registries, and patient-generated data ( U.S. FDA, n.d. ). Though they were originally designed to support health care delivery, these sources provide rich opportunities for clinical trials ( Cowie et al., 2017 ). RWD have mostly been used for observational studies, pragmatic comparative effectiveness trials, and surveillance studies ( Kibbelaar et al, 2017 ). An advantage of RWD is that it provides quick and low-cost access to large amounts of clinical data with the potential to complement RCTs and help expedite RCT recruitment ( Bartlett et al, 2019 , Obeid et al., 2017 ).

EHRs and other sources of RWD can help improve the efficiency of clinical trials ( Cowie et al., 2017 ; Lauer et al., 2017 ). Kibbelaar et al. (2017) and Foroughi et al. (2018) discussed the utility of registries (e.g., cancer registries, population-based registries) and advanced EHRs in providing high-quality data for observational oncology trials as well as proposing trials embedded in EHR and randomized registry trials. Studies previously mentioned (Adaptable, All of Us, and the Million Veteran Program) also utilized EHR data ( Johnston et al., 2016 ; Denny et al., 2020 ; Gaziano et al., 2016 ). For the Adaptable study, longitudinal EHR data were matched with commercial claims data to improve objective study outcome measurement of service utilization, such as hospitalizations and deaths ( Ma et al., 2019 ). Challenges to using EHR and other RWD may include variable selection, missing data, and/or poor data quality (see Section 4.0 below).

2.3. Artificial Intelligence (AI)

AI refers to the use of computers to simulate human brain processes for acquiring information, using the information, making interpretations or conclusions, and learning from the experience. Mak & Pichika (2019) describe AI as using technology tools to mimic human behavior. Sub-fields of AI are machine learning (ML) and deep learning (DL), where statistical methods are used for learning ( Mak & Pichika, 2019 ). AI methodology has been applied to many areas of health care, including surgery, imaging, and disease diagnosis and treatment, and could be another tool for enhancing personalized medicine. AI can also be used in clinical trial design and patient identification, as well as trial monitoring ( Jang, 2019 ). A few highlighted current applications of AI: (1) Lin et al. (2019) compared AI technology with provider assessment to determine AI’s efficacy in diagnosis and treatment decision-making for children with cataracts; (2) Roggeveen et al. (2019) plan to conduct a superiority trial using an AI system for antibiotic dosing, compared to standard dosing, for critically ill septic patients; and (3) Wijnberge et al. (2020) tested an AI application compared to standard protocols to reduce intraoperative hypotension.

2.4. Blockchain technology

Blockchain technology, originally associated with cryptocurrencies like Bitcoin, uses a decentralized, peer-to-peer computer network that enables databases to store encrypted time-stamped records and documents. Each server, or “node,” in the network processes and verifies each data entry, then archives all transactions, along with the history of every other transaction ever recorded to the network, creating a secure and immutable “chain” of content “blocks.” Applications of this technology in the health sciences are being explored to address some of the common threats to data integrity and security. In a 2018 review of over 300 studies on the use of blockchain technology in healthcare, Mayer and colleagues posit that moving EHRs from independent systems to a blockchain network could make health information sharing more efficient and secure, enabling better collaborative clinical decision-making and more patient control over privacy (Mayer, et al 2018). Blockchain is also being considered as a platform for clinical trials data management for many of the same reasons. Osipenko (2019) describes it “as a way of managing data that prevents its manipulation” and argues that it offers a technical solution that could improve transparency, security, quality, and efficiency of clinical trials. Others have discussed the potential of using this technology to improve clinical trials processes, such as adverse events reporting, trial data management, endpoints adjudication, adherence to reporting requirements, and data sharing ( Benchoufi & Ravaud, 2017 ; Maslove, et al., 2018 ; Wong, et al., 2019 ; Zhuang et al., 2018 ).

3.0. ETHICAL, REGULATORY, PRIVACY, AND DATA STANDARDS CONSIDERATIONS

The primary ethical, human subjects, and legal concerns related to the use of digital tools in clinical research are ensuring proper informed consent procedures with electronic materials and protecting participant privacy/confidentiality when using digital technology ( Jacobson et al., 2020 ). These important challenges and limitations require careful consideration and management. An important guide recently developed by the CTTI provides recommendations for addressing many of these data quality and privacy issues. ( Herrington et al., 2018 ; CTTI, 2018 ). In addition, McKay and colleagues (2018) produced a systematic review technology effectiveness and quality of mobile technology data and Eagleson and colleagues (2017) describe ethical and privacy concerns.

3.1. Regulatory and ethical considerations

Just five years ago, there were few Institutional Review Board (IRB) guidelines for researchers related to the use of technology in research studies. We are pleased to report that numerous IRBs have now posted procedures and best practices with specific guidance on this subject. For example, Indiana University provides guidance for study investigators on the use of digital tools to facilitate recruitment, informed consent, and data collection ( Indiana University, n.d. ). Similarly, several other organizations have posted guidelines on the use of social media, mobile devices, and other digital tools. For example, the Center for Technology and Behavioral Health at Dartmouth College offers resources on informed consent ( Center for Technology and Behavioral Health, n.d. ). As previously noted, the FDA has also provided guidance on the use of electronic consent ( U.S. FDA, 2016 ). To assist both IRBs and researchers, Torous and Nebeker (2017) describe the Connected and Open Research Ethics (CORE) initiative, a group intended to work on best practices and guidelines for the use of technology in clinical research. Further, Bloss and colleagues (2016) described their ideas on redesigning the current human research protection system.

Regulatory and human subject protections agencies in the U.S. and abroad have released guidance(s) on the use of digital technologies in clinical research. The FDA has developed guidance for sponsors, researchers, and IRBs on the use of electronic informed consent (2016) and using EHRs/electronic signatures (2017). The 2016 FDA guidance on using electronic informed consent was developed jointly with the U.S. Office for Human Research Protections (OHRP). OHRP also revised the human subject protection regulations (referred to as the Common Rule) that could be relevant to some areas of secondary research ( U.S. OHRP, 2017/2018 ). In 2018, the FDA created a “real-world evidence” initiative to evaluate the potential of using RWD to support regulatory decisions for drugs and biological products ( U.S. FDA, 2018 ). As part of this initiative, they provided useful definitions and examples of clinical studies and trials using registries and EHRs to generate evidence. The CTTI has also published recommendations on using RWD in clinical trials, especially focused on of study design and recruitment ( CTTI, 2019 ). The FDA has published a Digital Health Innovation Plan for medical devices that outlines their approach to digital health products ( U.S. FDA, 2020 ).

Outside of the U.S., the European Medicines Agency (EMA) released a report with recommendations regarding the use of social media and mobile health data for regulatory purposes ( Donegan et al., n.d. ). Additionally, in 2015 they established an initiative that provides regulatory recommendations on the use of patient registries in research ( European Medicines Agency, n.d. ; Olmo et al., 2019 ). Cave et al. (2019) also published recommendations for using RWD for regulatory purposes in Europe.

3.2. Privacy and data standards

The protection of participants’ data privacy and confidentiality has been an ongoing topic of concern ( Thompson & O’Regan, 2018 ), and recent scandals surrounding the use of social media data are not inspiring confidence among the scientific community on the use of technology in clinical research. Apps do not always include a privacy policy or terms of agreement, and even when they do, the policy language is often at an inappropriate reading level and uses excessive jargon (Robillard et al. 2019). Furthermore, many app privacy policies are not transparent, leaving users unsure of how their data are shared with third parties ( Grundy et al. 2019 ; Huckvalle et al., 2019 ). When using apps in RCTs, investigators should carefully review the app privacy and data sharing policies and ensure participant understanding during the consent process.

Data protections more broadly have also seen improved guidance in recent years. In 2017, Eagleson et al. provided recommendations on data protections, based on their experience using mobile technology, to include security measures against outside risks. Pagoto and Nebeker (2019) caution that there are few regulations for protecting participants’ data in research using social media, and users are usually unfamiliar with social media privacy policies. The authors propose the engagement and coordination of diverse disciplines/stakeholders to develop ethical standards, best practices, and resources for researchers. One particularly important stakeholder group is potential participants, given that their perspective could be different from that of individuals who conduct research. For example, somewhat surprisingly, a study conducted with social media users found that many do not view the monitoring of their accounts for the purposes of RCT recruitment as a violation of their privacy ( Reuter et al., 2019 ). Finally, as mentioned previously, the CTTI (2018) provides recommendations on the use of mobile technology in clinical trials and includes strategies to ensure data security and data management. Blockchain technology, as noted above, could also be a useful solution to improve data security.

Regarding data standards, the Clinical Data Interchange Standards Consortium (CDISC) created definitions, models, and specifications for data representation in research ( CDISC, n.d. ). Regulatory agencies, such as the FDA, have adopted these standards, but adoption by the research community has been slower, especially when using data from EHRs or other RWD sources instead of regular electronic data capture systems. EHRs were created for clinical, billing, and operational purposes, not for research, and thus can present challenges when used in clinical trials, including data quality and validation issues, heterogeneity between health systems, data security, and privacy ( Cowie et al., 2017 ). Ethier and colleagues (2017) discussed a solution to capture data from EHRs using CDISC standards to improve data quality for regulatory purposes. Finally, Agrawal & Prabakaran (2020) , provide recommendations for analyzing large-scale unstructured data (“Big Data”) generated by digital technologies.

Data interoperability, particularly in countries where health care is privatized, continues to be problematic in multisite RCTs, and adoption of interoperability standards has been slow ( Nordo et al., 2018 ). In the U.S., there have been efforts to encourage the adoption of Fast Healthcare Interoperability Resources (FHIR) standards for exchanging healthcare information electronically ( Nordo et al., 2018 ). Several recent initiatives have focused on FHIR, including a review of the use of FHIR in digital health and identification of the main categories of use (e.g., EHRs, mobile and web applications, data protection) ( Lehne et al., 2019 ) and the development of an approach for creating automated data capture from EHRs using FHIR standards ( Zong et al., 2020 ).

4.0. ADVANTAGES, CHALLENGES, AND LIMITATIONS OF USING DIGITAL TECHNOLOGIES IN CLINICAL TRIALS

As detailed in our previous paper ( Rosa et al., 2015 ), and by many of the authors referenced throughout this paper (e.g., Li et al., 2016 ; Lauer et al., 2017 ; Foroughi et al., 2017; Steinhubl et al., 2019 ), technology offers numerous advantages for clinical trials design and implementation efficiency. Digital technologies are currently used to assess study feasibility, facilitate participant recruitment and retention, enable access to diverse populations, streamline data collection, and facilitate data management. Advantages include lower cost, rapid enrollment, and more complete follow-up data resulting in greater generalizability of results. On the other hand, several challenges previously noted in our earlier publication and others (e.g., Cowie et al., 2017 ; Eagleson et al., 2017 ; Huckvalle et al., 2019 ; Pratap et al., 2020 ; Frampton et al., 2020 ) remain. Some of the main challenges include ethical and privacy issues as well as issues related to data quality. A list of the advantages and challenges are summarized below.

4.1. Advantages

• reducing costs by reducing the time required for study enrollment,
• increasing participation, including that of underrepresented racial/ethnic or socially disadvantaged groups, by increasing trial visibility, targeting online recruitment efforts to specific sub-populations, and reducing burden of travel to sties,
• decreasing intervention delivery burden and supervision,
• improving intervention fidelity when using smart apps and AI algorithms (i.e., Clinical Decision Support or CDS),
• reducing data collection burden (for both participants and study staff) by utilizing data capture models with existing platforms (i.e., EHRs, registries, administrative databases, smartphones),
• improving data quality, reproducibility, privacy and sharing when including Blockchain technology algorithms that facilitate data accuracy and security ( Benchoufi 2017 ; Osipenko, 2019 ; Maslove et al., 2018 ),
• improving analysis of big data when using AI and ML algorithms ( Mak & Pichika, 2019 ).

An advantage increasingly being realized is the potential for enrolling a more representative sample for trials, as the “digital divide” continues to decrease (see Figures 1 & 2 ). Data from 2018 show that more older individuals, as well as more individuals in rural communities, have access to the Internet via broadband or smartphones ( Winstanley et al., 2018 ; Pew Research Center, 2019d ). Arguably, the two most beneficial developments since our last report five years ago are the influx of available digital tools (e.g., AI advances and blockchain) that address some of the concerns related to data accuracy and participant privacy, as well as the advancement of clearer guidance from ethics and regulatory groups on incorporating digital technology approaches in the conduct of clinical trials.

% of U.S. Adults Who Use the Internet, By Age Source: Pew Research Center, 2019d

% of U.S. Adults Who Use the Internet, By Community Type Source: Pew Research Center, 2019d

4.2. Challenges and Limitations

• maintaining privacy/confidentiality of participant data obtained by digital methods, including inadvertently sharing data with unknown third parties,
• ensuring that regulatory guidance reflects the latest technological advances,
• ensuring access to adequate infrastructure, resources, and staff expertise,
• keeping participants engaged and increasing retention,
• considering user attitudes, practices, expectations, and preferences, as potential participants may view using their data as a violation of their privacy,
• ensuring data quality/accuracy given that, although standards and guidance/best practices have emerged, there are still concerns with data quality, completeness, and accuracy (particularly as digital tools are usually developed for use in other areas and need to be adapted for use in research, and most are not interoperable),
• overcoming the complexities surrounding big data (ensuring mobile technologies capture adequate data and selecting specific outcome data from the digital sources).

Another challenge that remains is recruiting adequate, representative samples and avoiding sample bias. As mentioned in section 4.1 , the “digital divide” has decreased in the last five years, however individuals from underserved groups and residing in rural areas of the U.S. are still underrepresented in clinical research and less likely to have home broadband access or own a smartphone than those residing in urban or suburban areas ( Pew Research Center, 2019 & 2019c ; Noonan & Simmons, 2020 );

In addition, challenges may emerge as new strategies are implemented to protect data. For example, firewalls to protect data may prohibit Internet-connected statistical software packages from downloading updates and local security policies could conflict with funding agency requirements. As health care systems adopt more rigorous strategies to safeguard protected health information, these same strategies may severely restrict access and analysis for clinical studies.

4.3. Using digital tools during a health crisis

As we finish writing this article, the COVID-19 pandemic has been a catalyst to the expansion of the use of telehealth and other digital technologies ( Keesara et al., 2020 ). Regulations prohibiting or limiting the delivery of in-person health care have forced providers to overcome barriers to technology adoption ( Winstanley et al., 2020 ) and increasingly consider novel digital tools ( Iyengar et al., 2020 ). This crisis has also disrupted clinical trials operations in the U.S and around the world. Most investigators and/or sponsors have temporarily suspended operations or redesigned them to take advantage of available digital tools for recruitment or delivering study procedures, when possible ( Noonan & Simmons, 2020 ). The FDA and the EMA have both issued guidance recommending the use of alternative approaches to in-person visits (including remote and/or virtual tools) to maintain participant safety and to minimize risks to trial integrity ( U.S. FDA, 2020 ; EMA, 2020). Noonan and Simmons (2020) argue that adoption of digital tools could be scaled up to continue research operations during the current crisis, and investigators should consider maintaining these tools and methods once restrictions on trials are lifted. AI approaches are also currently being applied to rapidly identify molecules that are the most promising for COVID-19 drug and vaccine development. In a matter of weeks, a global collaborative team that included engineers and physicists using supercomputers analyzed 1 billion possible molecules and selected the 30 most promising ones for further drug development consideration ( University of Texas at Austin, 2020 ). This public health crisis has further underscored the opportunities for leveraging digital technologies in clinical research and practice.

5.0. CONCLUSIONS

The selected literature cited in this paper illustrates how digital technologies can improve certain aspects of the design and implementation of clinical trials, and we encourage continued innovation in this area to optimize clinical trial efficiency. The rapid integration of digital approaches into clinical care and research during the COVID-19 crisis reflects the progress in this arena and presents an opportunity to better evaluate the value of digital tools used by researchers to enable continuation of study operations during the pandemic. Many challenges remain, including: (1) the impact of the persisting digital divide on recruitment of adequate, representative individuals; (2) privacy and confidentiality of health information shared over the Internet; and (3) technical and cultural barriers regarding data integrity and technology advances. In addition, some clinical trial procedures and operations may not be ready for a digital approach.

We recognize that this paper is not a systematic review of the literature on this topic and that the efficiency, advantages, and limitations of using digital tools should be rigorously studied in order to evaluate their actual performance. We instead provide an update to our publication in 2015 on the value of using digital technologies in clinical research, as well as continued challenges and limitations. The selected research published in the last five years demonstrates the successful application of digital approaches to research, covering a wide spectrum of diseases/conditions and providing useful experiences and best practices, as well as methods that can be used to optimize the design and conduct of clinical trials.

Acknowledgments

This work was supported by grants from the National Institute on Drug Abuse: UG1DA040309; UG1DA013035 and P30DA029926

Conflicts of interest:

Author LAM is affiliated with Pear Therapeutics, Inc., HealthSim, LLC, and Square2 Systems, Inc. Conflicts of interest are extensively managed by her academic institution, Dartmouth College.

CR, ELW, MB and ANCC report no conflicts of interest.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Advertisement

Frozen human brain tissue can now be revived without damage

Using a new approach, scientists have successfully frozen and thawed brain organoids and cubes of brain tissue from someone with epilepsy, which could enable better research into neurological conditions

By Christa Lesté-Lasserre

15 May 2024

Thawed brain organoids shown via an imaging technique called immunofluorescence staining

Weiwei Xue et al.

A new technique has allowed scientists to freeze human brain tissue so that it regains normal function after thawing, potentially opening the door to improved ways of studying neurological conditions.

Brain tissue doesn’t usually survive freezing and thawing, a problem that has significantly hindered medical research. In an effort to overcome this, Zhicheng Shao at Fudan University in Shanghai, China, and his colleagues used human embryonic stem cells to grow self-organising brain samples, known as organoids, for three weeks — long enough for the development of neurons and neural stem cells that can become different kinds of functional brain cells.

The researchers then placed these organoids — which measured 4 millimetres across on average — in different chemical compounds, such as sugars and antifreeze, that they suspected might help keep the brain cells alive while frozen and able to grow after being thawed.

Restoring the brain’s mitochondria could slow ageing and end dementia

After storing these organoids in liquid nitrogen for at least 24 hours, the team thawed them and looked for cell death or the growth of neurites — the “branches” of nerve cells — over the following two weeks.

Based on the rates of cell death and growth associated with each compound, the researchers chose their top compound candidates, trying different combinations during freezing and thawing tests on a new set of organoids.

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The combination that led to the least cell death and most growth was a blend of chemical compounds called methylcellulose, ethylene glycol, DMSO and Y27632 — which the scientists named “MEDY”. They suspect MEDY interferes with a pathway that otherwise programs cellular death.

Shao and his colleagues tested MEDY through a series of experiments involving brain organoids ranging from 28 days old to more than 100 days old. The team placed the organoids in MEDY, before freezing — usually for 48 hours — and thawing them. The researchers then observed their growth in the laboratory for up to 150 days post-thawing.

They found that the thawed organoids’ appearance, growth and function were highly similar to those of organoids of the same age that had never been frozen, even among those that had been frozen in MEDY for 18 months. The team also observed similar results for organoids representing different regions of the brain.

Finally, the researchers took 3-millimetre cubes of brain tissue from a 9-month-old girl with epilepsy and placed them in MEDY, before freezing and thawing them. The tissue maintained its pre-freezing structure and continued to remain active in a laboratory culture for at least two weeks after thawing.

We are finally starting to understand brain fog and how to treat it

Being able to freeze human brain tissues could lead to better investigations of brain development in the lab for health research, says Roman Bauer at the University of Surrey in the UK.

João Pedro Magalhães at the University of Birmingham in the UK says he is impressed that the team’s method successfully prevented cell death and preserved function. “We know brain cells are very fragile and sensitive to stress,” he says.

With significantly more research and the use of larger tissues, the work could one day lead to freezing entire brains, says Magalhães. “Thinking decades or centuries ahead, we can imagine patients being cryopreserved when they have a terminal condition or astronauts being cryopreserved in order to travel to other star systems,” he says. MEDY may represent “one small step” towards that goal, says Magalhães.

Journal reference:

Cell Reports Methods DOI: 10.1016/j.crmeth.2024.100777

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Math discovery provides new method to study cell activity, aging, MSU research shows

Contact: Meg Henderson

STARKVILLE, Miss.—New mathematical tools revealing how quickly cell proteins break down are poised to uncover deeper insights into how we age, according to a recently published paper co-authored by a Mississippi State researcher and his colleagues from Harvard Medical School and the University of Cambridge.

Galen Collins, assistant professor in MSU’s Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, co-authored the groundbreaking paper published in the Proceedings of the National Academy of Sciences, or PNAS, in April.

“We already understand how quickly proteins are made, which can happen in a matter of minutes,” said Collins, who is also a scientist in the Mississippi Agricultural and Forestry Experiment Station. “Until now, we’ve had a very poor understanding of how much time it takes them to break down.”

The paper in applied mathematics, “ Maximum entropy determination of mammalian proteome dynamics ,” presents the new tools that quantify the degradation rates of cell proteins—how quickly they break down—helping us understand how cells grow and die and how we age. Proteins—complex molecules made from various combinations of amino acids—carry the bulk of the workload within a cell, providing its structure, responding to messages from outside the cell and removing waste. 

The results proved that not all proteins degrade at the same pace but instead fall into one of three categories, breaking down over the course of minutes, hours or days. While previous research has examined cell protein breakdown, this study was the first to quantify mathematically the degradation rates of all cell protein molecules, using a technique called maximum entropy.

“For certain kinds of scientific questions, experiments can often reveal infinitely many possible answers; however, they are not all equally plausible,” said lead author Alexander Dear, research fellow in applied mathematics at Harvard University. “The principle of maximum entropy is a mathematical law that shows us how to precisely calculate the plausibility of each answer—its ‘entropy’—so that we can choose the one that is the most likely.”

“This kind of math is sort of like a camera that zooms in on your license plate from far away and figures out what the numbers should be,” Collins said. “Maximum entropy gives us a clear and precise picture of how protein degradation occurs in cells.”

In addition, the team used these tools to study some specific implications of protein degradation for humans and animals. For one, they examined how those rates change as muscles develop and adapt to starvation.

“We found that starvation had the greatest impact on the intermediate group of proteins in muscular cells, which have a half-life of a few hours, causing the breakdown to shift and accelerate,” Collins said. “This discovery could have implications for cancer patients who experience cachexia, or muscle wasting due to the disease and its treatments.”

They also explored how a shift in the breakdown of certain cell proteins contributes to neurodegenerative disease.

“These diseases occur when waste proteins, which usually break down quickly, live longer than they should,” Collins said. “The brain becomes like a teenager’s bedroom, accumulating trash, and when you don’t clean it up, it becomes uninhabitable.”

Dear affirmed the study’s value lies not only in what it revealed about cell protein degeneration, but also in giving scientists a new method to investigate cell activity with precision.

“Our work provides a powerful new experimental method for quantifying protein metabolism in cells,” he said. “Its simplicity and rapidity make it particularly well-suited for studying metabolic changes.”

Collins’s post-doctoral advisor at Harvard and a co-author of the article, the late Alfred Goldberg, was a pioneer in studying the life and death of proteins. Collins noted this study was built on nearly five decades of Goldberg’s research and his late-career collaboration with mathematicians from the University of Cambridge. After coming to MSU a year ago, Collins continued collaborating with his colleagues to complete the paper.

“It’s an incredible honor to be published in PNAS, but it was also a lot of fun being part of this team,” Collins said. “And it’s very meaningful to see my former mentor’s body of work wrapped up and published.”

Since 1914, PNAS has been one of the most authoritative publications of high-impact research in the biological, physical and social sciences. More information and past issues can be found at www.pnas.org . The Mississippi Agricultural and Forestry Experiment Station conducts research that improves human health and well-being. Learn more at www.mafes.msstate.edu .  

Mississippi State University is taking care of what matters. Learn more at  www.msstate.edu .

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Risk of ‘genetic discrimination’ by insurance companies is ruining people’s trust in vital medical science

Lecturer in psychology., Swinburne University of Technology

Disclosure statement

Brad Elphinstone received funding from a Medical Research Future Fund grant.

Swinburne University of Technology provides funding as a member of The Conversation AU.

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Should you be denied life insurance or have to pay extra if you have a genetic risk for certain diseases? Should insurance companies even have access to your genetic data in the first place? This is known as “genetic discrimination”, a practice already banned in some countries, including Canada .

The Australian Treasury is currently working on a review of the relevant legislation, with insurance industry bodies arguing a total ban would raise life insurance bills.

But this legislation doesn’t just have implications for genetic discrimination. Genetic testing underpins vital branches of medical science. Our research shows the question of who can assess a person’s genomic data directly influences public trust in future genomic research in Australia.

What is genomic research?

Human genomic research holds promise for the development of cures and treatments for cancers and heritable diseases. To achieve this, researchers rely on people willingly donating their genomic data. This is your DNA code derived from something like a blood sample. Genomic data is particularly useful when linked with lifestyle – diet, exercise, habits – and health records.

If researchers have access to this data from thousands of people, they can look for patterns to see if certain genes might be linked to certain illnesses or diseases. Treatments or cures can then be developed to target the gene or genes involved.

To assist with making genomic research viable and accessible for researchers, national-level biobanks exist, such as in the United Kingdom . These biobanks can store data from hundreds of thousands of people.

Australia does not yet have a national biobank, but some researchers in Australia do conduct studies that involve the collection of genomic data.

Can we trust biobanks?

Previous research has found people are generally supportive of genomic research and biobanks. They recognise the potential for new treatments or cures such research can bring.

However, trust in biobanks decreases substantially if there is any commercial involvement in biobank management or research. This poses a problem, as commercial involvement in biobanking is increasingly likely. Running these repositories, conducting research and bringing new treatments to market is expensive.

People who express such distrust often cite concerns that profit will be put ahead of the public good. One common issue is the perceived unfairness of “big pharma” hypothetically making large profits from freely donated genomic data.

Another primary concern, that is often a dealbreaker when it comes to hypothetically donating data, is that data will be sold to insurance companies who will then deny cover or increase premiums.

If people are unwilling to donate to biobanks due to the perceived risk of genetic discrimination from insurance companies, the scope of genomic research may suffer.

People are most trusting of biobanks if they are managed by universities and hospitals, who then also conduct the research. This is because these types of public institutions are not typically seen to be profit driven.

Would Australians trust a biobank?

Our research explored the required conditions for a trusted Australian national biobank. Specifically, we examined what Australians think about genomic research and sources of distrust. We also examined different legal safeguards that could be implemented to enhance trust and willingness to donate.

We started by surveying a statistically representative sample of 1,000 Australians. We found four groups Australians can be categorised into based on their attitudes towards genomic research:

• highly supportive and willing to donate to a national biobank (approximately 23% of the population if you extrapolate from our sample)
• supportive and open to donating but wary of commercial involvement (37%)
• supportive and open to donating but wary of commercial and governmental involvement (26%)
• completely unwilling to donate under any circumstances (14%)

In a follow-up study we interviewed 39 people from these groups. Across the four groups, including those who were willing to donate, there were clear concerns about genetic discrimination from insurers or employers. Concern about corporate profiteering was also widespread. However, respondents maintained a pragmatic view that pharmaceutical companies necessarily need to make some profit.

Based on the interviews, and a third experimental survey, it was clear a national biobank should be managed by a public institution. Additionally, we should have a data access committee comprising relevant experts.

This committee would assess applications from researchers attempting to access the data. For example, data access would be allowed only for researchers from established commercial or public organisations. Additionally, researchers would be compelled to only use data for ethical human health research and make no effort to identify donors.

Overall, Australians generally do support genomic research – they recognise its potential to give us much-needed new medical treatments and even cures.

But this support is undermined if people feel that genetic discrimination is a likely risk for themselves or their blood relatives.

Legislation that reduces this risk targets a main source of distrust that can make people unwilling to donate genomic data. A law preventing genetic discrimination could therefore indirectly benefit genomic research and support for a national biobank, should one exist in the future.

The author would like to acknowledge research collaborators Jarrod Walshe from Swinburne University of Technology, Dianne Nicol from the University of Tasmania and Mark Taylor from the University of Melbourne. The research project was based on a Medical Research Future Fund grant that was awarded to Professor Christine Critchley who sadly passed in 2020.

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May 20, 2024

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Why autistic people must be at the heart of autism research

by Gemma L. Williams, Aimee Grant and Willow Caroline Holloway, The Conversation

When the term "social model of disability" was coined by British sociologist Mike Oliver in 1983, it helped form the basis of the disability rights movement.

To mark the birth of that movement, as well as the 30th anniversary of the autistic rights movement, our new report reflects on why it is vital that autistic people are always at the center of autism research .

During the 1980s, the term "social model of disability" challenged how society largely regarded disability as a personal tragedy. Disablement was seen as something that belonged to individual disabled people, with heavily medicalized personal "impairments."

But Oliver argued that disablement came from how society treated disabled people and from the systemic lack of equitable access. The social model of disability ultimately informed disability awareness and equality training.

Not long after, in the early 1990s, as people began having access to the internet, autistic people began finding one another on chat boards and email lists.

Around the same time, autobiographical texts written by autistic people began to be published online. They helped introduce the voices of autistic people to neurotypical people for the first time. However, reflecting the perspective of that period, much of the tone of the writing conveyed the message that autism was a "tragedy" that needed to be mourned.

It was against this backdrop that the American autistic rights activist Jim Sinclair gave a seminal speech at the 1993 International Conference on Autism in Toronto, Canada. Addressing parents of autistic children, his "Don't Mourn For Us" speech called for a move away from a parental perspective that "grieves" the disabled child. Instead, Sinclair advocated for an empowered, autistic perspective. The speech helped spur the autistic rights movement.

Autism research

Historically, autism research has been conducted by non-autistic researchers. The consequence of this has been a pathologizing and often dehumanizing take on autism.

For example, in one 2019 study, autistic people were found to be more generous . But instead of seeing this as something that is advantageous to society, the researchers interpreted the findings as an example of how autistic people struggle to tell the difference between themselves and other people.

Likewise, despite the progress that has come from a shift towards a social model of disability, funding for research into autism still tends to be given to non-autistic researchers . This is often for research that doesn't address the needs of autistic people or tackle our significant health and well-being inequities.

Even when the stated aims of research appear to be about cultivating autistic well-being, there are often glaring concerns. One example of this was the Spectrum 10K project , which was launched in 2021 by researchers at the University of Cambridge, the Wellcome Sanger Institute and the University of California Los Angeles.

The project planned to generate a large autism DNA database, which was something that alarmed many autistic people. It provoked protests over the potential for it to lead to eugenics , where gene pools are altered according to which people are deemed to be superior or inferior. The study is currently paused , with a long-awaited consultation report overdue.

In recent years, there has been an increasing call for research that meaningfully involves autistic people in all stages of research, from design, through delivery to dissemination.

Collaboration

Building on this, our new report describes how we are working together as a mutually supportive, fully autistic team on the Autism: From Menstruation to Menopause project . This project was set up to address the knowledge gap about autistic reproductive experiences.

Our first task was to recruit an autistic community council. We wanted to ensure that it included people who were usually underrepresented in research. Once it had been established, the next step was for academic researchers and community council members to work together to develop accessible recruitment materials for our study's 100 autistic participants.

Although it is early days, we anticipate that our community council will have an important role in helping us interpret our findings and in preparing our reports.

We aren't suggesting that good autism research can't be done by non-autistic researchers. But autistic people should always be involved in research in a meaningful way and should be listened to at every stage of a project. By doing that, researchers can avoid inadvertently doing research that is either harmful to autistic people, or which misinterprets the findings.

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