Advancing Digital Biomarkers for Alzheimer's and Related Dementias




The Diagnostics Accelerator is accepting applications on a rolling basis.

Application Instructions

Recent innovations in technology now provide novel ways to collect, track, and analyze patient data. These digital tools can provide quantifiable indicators or biomarkers of a patient's physiological and/or behavioral state.

Digital biomarkers have the potential to add significant value to clinical trials, reach broader swaths of the population, empower patients and caregivers, and greatly improve treatment outcomes.

Decades of research have identified a variety of symptom domains that correlate with the different stages of Alzheimer's disease and related dementias. These symptoms include changes in speech, cognitive impairment such as loss of memory and executive function, sensory/motor function, as well as impairment in higher order and daily functions. By leveraging advances in computational capabilities, coupled with readily available sensors in consumer electronics, we can begin to identify and monitor subtle, yet pertinent changes caused by neurodegenerative disease with digital biomarkers. Importantly, these tests can provide objective, passive, continuous, longitudinal, and/or quantifiable physiological and behavioral data with the potential increase in the statistical power of clinical trials, thus reducing cost, improving the ability to detect outcomes, and facilitating personalized therapeutic approaches.

In addition to improving clinical trials, digital biomarkers will revolutionize patient treatment and care. Primary care physicians and caregivers could soon have the technological capability to capture essential information around a patient's prognosis, reaction to treatment, and general quality of life, all of which are currently hard to accurately evaluate with sporadic in-clinic assessments. Given the ubiquitous nature of consumer electronics such as wearables and mobile devices along with powerful computational platforms, digital biomarkers have the potential to serve as patient engagement, compliance, and monitoring tools.

Various technological platforms (mobile phones, wearables, tablets, and cloud-based tools) provide monitoring capabilities with limited additional cost and operational burden on the healthcare system, clinical researcher, caregiver, or patient. While promising, these approaches require extensive technical and clinical validation to be fully accepted into traditional clinical practice. The development of digital biomarkers will depend on leveraging the data garnered from these different technologies. The Diagnostics Accelerator initiative will fast-track the development of these new technologies and the digital biomarkers using the data generated from these technologies.



The Diagnostics Accelerator is a partnership of funders dedicated to accelerating the development of affordable and accessible biomarkers for Alzheimer's disease, frontotemporal degeneration, and other related dementias. The Diagnostics Accelerator supports research and development through translational research awards and access to consulting support from industry experts. The current RFP is soliciting projects to develop and validate digital biomarkers for Alzheimer's disease and related dementias. Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected, measured and analyzed by means of digital devices such as portables, wearables, or ambient sensors. Digital biomarkers range from computerized or app-based versions of traditional neurocognitive tests to novel technology platforms that combine multiple complex data sources into a phenotypic signature.

Proposals addressing a range of potential clinical uses are of interest, especially technologies for early assessment and those aiding in diagnosis and monitoring treatment response or rate of disease progression. Creative approaches to leverage new and existing software and hardware are encouraged. Importantly, the use of the digital technology should be driven by 1) an unmet patient or scientific need for a better assessment and/or 2) providing a more cost-effective, efficient, and less burdensome approach to diagnosis and monitoring in clinical practice and clinical trials.

Funding Priorities

Platforms: A variety of digital platforms such as portables, sensors, or software are encouraged. The proposed platform should have the potential to be easily deployed at scale.

Examples of digital approaches include, but are not limited to:

  • Wearables devices (e.g., smart watch)
  • Mobile/tablet apps
  • Smart home systems
  • Virtual and augmented reality platforms
  • Desktop/web apps

Note: Diagnostic hardware for traditional digital imaging platforms (e.g. optical coherence tomography, neuroimaging) will not be considered as part of this RFP.

Symptom Domains: The RFP encourages digital biomarkers emerging from one or more of the symptom domains below. Proposed approaches will be evaluated based on the existing evidence around the biological link of the symptom domain to disease and how measuring the proposed symptom domain will improve current screening or monitoring methods in patients.

Symptom domains of interest include, but are not limited to:

  • Cognition (e.g. memory, processing speed, executive function, or geolocation)
  • Speech and Language (e.g. written text or vocal features)
  • Activities of Daily Living (instrumental basic activities or higher order activities)
  • Motor function (e.g. gait, body motion, or fine motor skills including tapping, swiping, and tracing on touchscreens)
  • Sensory Acuity (e.g. hearing, smell)
  • Affect (e.g. mood, facial expression)
  • Sleep Patterns and Characteristics
  • Oculomotor (e.g. eye movement)
  • Pain Assessment
  • Autonomic Nervous Function (e.g. heart rate, galvanic skin response)

Combinations of these or other symptom domains with a clear link to the disease are also encouraged.


Three stages of projects will be supported through this RFP:

  1. Exploratory awards will support pilot studies that aim to test the utility of an existing digital technology for the first time in an Alzheimer's disease or related dementia population. These projects should already have preliminary human data from another disease indication. For example, a pilot study would test a wearable gait monitoring device that has been tested in subjects with multiple sclerosis and is now being proposed to test in patients at risk for Alzheimer's. Only proposals with evidence demonstrating their technology or prototype can reliably capture, process, store, and transfer data from a clinical population will be considered. A limited number of awards will be considered in this category.

    Generally, projects at this stage will be awarded up to approximately $250,000 based on stage and scope of research. However, this is not a cap and higher funding levels will be considered if the proposed budget is well justified.

    Note: Investigative teams planning to develop algorithms or employ machine learning methods must demonstrate the ability to successfully complete this type of project and that the team has appropriate permissions to access the data.

  1. Proof-of-principle awards will support projects that demonstrate feasibility and/or verify that a certain approach has potential for use in Alzheimer's disease or related dementias. Preliminary data from human subjects with the proposed indication is expected. For example, the disruption of sleep is an early change seen in Alzheimer's disease and a proof-of-principle project testing a device that is capable of detecting the stages and fragmentation of sleep would build on preliminary data acquired in the proposed patient population and proposed context of use, in addition to expanding the number of patients (e.g. ~100-200 subjects) tested with the sleep tracking device. Data must be provided demonstrating that the technology or prototype is capable of the reliable capture, processing, storage, and transfer of valid data to test in the clinical population

    Generally, projects at this stage will be awarded up to approximately $500,000 based on stage and scope of research. However, this is not a cap and higher funding levels will be considered if the proposed budget is well justified.

  1. Validation awards will support projects that require testing at a larger scale and access to patients of varying demographic diversity to demonstrate clinical relevance. The technology must be verified and validated. Data quantifying the accuracy, precision, consistency, and uniformity of the technology must be provided. Applicants will be required to address scalability considerations, clinical integration plans, and anticipated regulatory considerations and commercialization. Data sharing policies and standards, intellectual property restrictions, and standard operating procedures should be well defined. Validation studies should prioritize comparisons using existing gold standard approaches to diagnose and monitor such as neuroimaging and/or CSF measurements, as well as clinical tests such as cognition, but this is dependent on the context of use. These studies will require a comprehensive experimental plan with larger sample size (e.g. 500-1000+ subjects based on power analyses).

    Award amounts will be based on the stage and scope of the research.


Projects that succeed in the exploratory or proof-of-principle stage may be eligible for follow-on funding in the form of a validation award.


Discussion on how the proposed digital biomarker would fit into the current clinical landscape, and how it would benefit clinical trials, patient care, or caregiver burden should be included.

All proposals will be evaluated on scientific and technical merit, level of innovation, and investigator and organizational capabilities. All of the following criteria should be addressed.

  • Context of Use (COU): The COU, as defined by the FDA, is a concise description of the biomarker's specified use in drug development. COU examples include diagnostic, monitoring, predictive, prognostic, and susceptibility/risk biomarkers. The expected context of use must be appropriate for the stage of disease and be fully described in the application. Applicants should specify the patient population in which the technology will be used in addition to the targeted user (i.e. primary care physician in clinic, patient in clinical trial, etc.).
  • Correlations with Underlying Clinical Phenotype: Demonstrate a rational biological connection of the measured data to the disease pathophysiology.
  • Supporting Information: Includes technology functional characteristics such as feasibility and performance including accuracy, precision, consistency, and uniformity. Data on human interaction with the technology (i.e. has this been tested in human subjects or in the target population) and any supporting literature should be included.
  • Experimental Design: Includes the proposed clinical population, outcome measures, and statistical analysis, as well as plans on how the technology will be implemented, maintained, and how the data will be read out should be included.
  • Data Collection Policies: What raw data and meta-data will be produced, and how will this data will be handled? The proposal must include plans for data sharing and highlight data protection policies. All data collected must adhere to CDISC standards, if available. If CDISC standards are not available, CDISC-compliant standards should be considered. Data used to train and validate the technology should be obtained from well-characterized cohorts and when possible, should include individuals from minority and disparity populations.
  • Scalability Considerations: All projects should discuss how the proposed technology will be translated into clinical use. Validation projects should include additional details on manufacturing, distribution, the regulatory pathway, clinical integration plans, expected cost (to patients, payers or healthcare providers) and qualitative descriptions of anticipated burden addition or reduction on the healthcare system (e.g., additional test in the system or replaces a test in the system).
  • Intellectual Property (IP) Considerations: Includes any pre-competitive development efforts and current IP status.
  • Potential for Commercial Translation: The path to commercialization should be considered for all applications. The applicant should articulate where in the path to commercialization the study falls and what is the proposed plan forward. Clear milestones and go/no-go decision points should be provided. Validation projects must clearly outline their strategy. Identification of potential future commercial partners is encouraged.
  • Inclusion of Clinical Team Members: Digital biomarker tests are expected to have clinical utility and therefore collaboration with a physician and/or a neuropsychologist with extensive experience with Alzheimer's disease or related dementias is required. A physician is required for all projects recruiting patients and these studies must be approved by an institutional review board.


Funding is open to researchers and clinicians worldwide at:

  • Academic medical centers and universities or nonprofits
    Industry partnerships are strongly encouraged.
  • Biotechnology companies
    Funding is provided through mission-related investments that require return on investment. Existing companies and new spinouts are both eligible.

Access to Consultants

In addition to providing funding, the initiative will provide support from a network of consultants with industry and regulatory expertise. Consultants will be made available to both academic and for-profit programs receiving funding from this award. Where appropriate, consultants will help awardees refine study designs in the pre-funding stage and meet critical milestones post-award. Consultants will also help to identify follow-on funding opportunities and partners that will advance technologies towards commercialization.

Data Sharing

It is the intention of the Diagnostics Accelerator that all data generated or used in each project should be made accessible to the research community through an ADDF/Gates Venture digital data platform (under development). The funder may use these data to evaluate the validity of the digital biomarker. In cases where data sharing is not allowed due to regulatory issues or limitations related to informed consent, the investigator must provide a data sharing proposal describing the limitations on sharing and providing an alternative data sharing plan.

Application Submissions

Review the Application Instructions for steps on applying.


For program-related inquiries, please contact:
Nicole Bjorklund, PhD, Assistant Director, Scientific Affairs

For application submission inquiries, please contact:
Grants and Mission-Related Investments Team