Key Dates

Related Announcements

PA-20-185 - NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed)

PA-20-183 - NIH Research Project Grant (Parent R01 Clinical Trial Required)

PA-20-184 - Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required)

PA-20-194 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Required)

PA-20-195 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Not Allowed)

PA-20-200 - NIH Small Research Grant Program (Parent R03 Clinical Trial Not Allowed)

PA-21-259 - PHS 2021-2 Omnibus Solicitation of the NIH, CDC and FDA for Small Business Innovation Research Grant Applications (Parent SBIR [R43/R44] Clinical Trial Not Allowed)

PA-21-260 - PHS 2021-2 Omnibus Solicitation of the NIH and CDC for Small Business Innovation Research Grant Applications (Parent SBIR [R43/R44] Clinical Trial Required)

PA-21-261 - PHS 2021-2 Omnibus Solicitation of the NIH for Small Business Technology Transfer Grant Applications (Parent STTR [R41/R42] Clinical Trial Required)

PA-21-262 - PHS 2021-2 Omnibus Solicitation of the NIH for Small Business Technology Transfer Grant Applications (Parent STTR [R41/R42] Clinical Trial Not Allowed)

PAR-19-274 - Dissemination and Implementation Research in Health (R01 Clinical Trial Optional)

PAR-19-275 - Dissemination and Implementation Research in Health (R21 Clinical Trial Optional)

PAR-21-038 - Stephen I. Katz Early Stage Investigator Research Project Grant (Clinical trial not allowed)

PAR-21-035 - Cancer Prevention and Control Clinical Trials Grant Program (R01 Clinical Trial Required)

PAR-21-190 - Modular R01s in Cancer Control and Population Sciences (R01 Clinical Trial Optional)

PAR-20-052 - NCI Small Grants Program for Cancer Research for Years 2020, 2021, and 2022 (NCI Omnibus R03 Clinical Trial Optional)

PAR-21-341 - Exploratory Grants in Cancer Control (R21 Clinical Trial Optional)

PAR-22-094 - Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R21 Clinical Trial Optional)

PAS-19-317 - Advancing Research on Alzheimer's Disease (AD) and Alzheimer's-Disease-Related Dementias (ADRD) (R41/R42 Clinical Trial Optional)

PAS-19-316 - Advancing Research on Alzheimer's Disease (AD) and Alzheimer's-Disease-Related Dementias (ADRD) (R43/R44 Clinical Trial Optional)

PAR-19-282 - Exploratory Clinical Neuroscience Research on Substance Use Disorders (R61/R33 Clinical Trial Optional)

PAR-21-135 - Development of Psychosocial Therapeutic and Preventive Interventions for Mental Disorders (R61/R33 Clinical Trial Required)

PAR-21-134 - Development of Psychosocial Therapeutic and Preventive Interventions for Mental Disorders (R33 Clinical Trial Required)

PAR-21-132 - Confirmatory Efficacy Clinical Trials of Non-Pharmacological Interventions for Mental Disorders (R01 Clinical Trial Required)

PAR-21-131 - Pilot Effectiveness Trials for Treatment, Preventive and Services Interventions (R34 Clinical Trial Required)

PAR-21-129 - Clinical Trials to Test the Effectiveness of Treatment, Preventive, and Services Interventions (Collaborative R01 Clinical Trial Required)

PAR-21-264 - Computationally-Defined Behaviors in Psychiatry (R21 Clinical Trial Optional)

PAR-21-263 - Computational Approaches for Validating Dimensional Constructs of Relevance to Psychopathology (R01 Clinical Trial Optional)

PA-20-145 - Innovations in HIV Prevention, Testing, Adherence and Retention to Optimize HIV Prevention and Care Continuum Outcomes (R21 Clinical Trial Optional)

PA-20-141 - Formative and Pilot Intervention Research for Prevention and Treatment of HIV/AIDS (R34 Clinical Trial Optional)

PA-20-144 - Innovations in HIV Prevention, Testing, Adherence, and Retention to Optimize HIV Prevention and Care Continuum Outcomes (R01 Clinical Trial Optional)

PAR-19-382 - Patient Activation for Self-Management of Chronic Conditions (R21 Clinical Trial Optional)

PAR-19-381 - Patient Activation for Self-Management of Chronic Conditions (R01 Clinical Trial Optional)

NOT-HD-22-019 - Notice of NICHD Participation in NOT-CA-22-037

Issued by

National Cancer Institute (NCI)

National Heart, Lung, and Blood Institute (NHLBI)

National Institute on Aging (NIA)

National Institute on Drug Abuse (NIDA)

National Institute of Mental Health (NIMH)

National Institute of Nursing Research (NINR)

Eunice Kennedy Shriver National Institute of Child Health and Human Development ( NICHD )

All applications to this funding opportunity announcement should fall within the mission of the Institutes/Centers. The following NIH Offices may co-fund applications assigned to those Institutes/Centers.

Office of Behavioral and Social Sciences Research (OBSSR)

Purpose

The purpose of this Notice of Special Interest (NOSI) is to encourage grant applications to support the evaluation of the utility and validity of digital health and artificial intelligence (AI) tools and technologies in epidemiological, clinical, and intervention research. The intent is to support the addition of new measurement modalities to evaluate existing and recently developed but not yet validated digital health and AI tools such as sensor technologies, smartphone applications, software as a medical device (SaMD), and AI algorithms.

Background

Digital health and AI technologies have grown exponentially in the past two decades. Digital health technologies include mobile devices, health information technology, wearable devices, sensors, telehealth and telemedicine, and internet of things (IoT). AI tools include computational technology integrated within devices or platforms as well as stand-alone AI technologies such as SaMD, risk assessment and prognosis algorithms, and software for informing health care used in the cure, mitigation, treatment, recovery, or prevention of health conditions without being part of a hardware medical device.

The range of health research and practice affected by the technological revolution is quite broad, including use in disease surveillance and public health research as well as for medical screening and diagnostic purposes where they can provide tools that can reach diverse users including individuals living in rural and underserved areas and low- and middle-income countries. However, many of these recently released tools are unregulated and their validity, reliability, and/or efficaciousness for research, practice, and clinical care have not been examined. Adoption of these technologies requires more rigorous evaluation across research and clinical settings, diverse populations, and health contexts. Through this NOSI, NIH strives to capitalize on current research projects that collect outcomes to validate digital health and AI tools.

Research Objectives

Research supported by this NOSI is expected to provide support for validation of recently developed digital health and AI technologies. Digital health and AI technologies are defined broadly to include any health technology leveraging mobile health, health information technology, wearable devices, sensors, telehealth and telemedicine, internet of things (IoT), software as a medical device (SaMD) and/or related AI algorithms and tools to monitor and manage health across the life course. As noted previously, the purpose of this NOSI is not to support development of new technologies.

Applicants should clearly justify the importance and implications of validating digital health and AI tools and technologies. Studies should apply rigorous research methods to evaluate the effectiveness of any proposed digital health and AI applications including the use of gold-standard comparators. Projects that validate digital health or AI tools for use in larger or more specific populations (e.g., low income, minority group) and research projects that evaluate the reliability of the tool and its sensitivity are encouraged.

Studies proposing secondary analysis should address the sufficiency of existing datasets to validate technologies and deepen the evidence base of digital health and AI applications while also addressing potential bias, including plans for risk mitigation that ensure safety, privacy, and effectiveness among diverse individuals and populations.

FDA regulated devices, activities included under this notice should follow FDA guidance and support appropriate activities leading to a future marketing submission to the FDA.

Investigators must carefully review the specific research interests of NIH Institutes and Centers that are participating in this NOSI.

IC Specific Application and Submission Information

Applicants must select the IC and associated FOA to use for submission of an application in response to the NOSI. The selection must align with the IC requirements listed in order to be considered responsive to that FOA. Non-responsive applications will be withdrawn from consideration for this initiative. In addition, applicants using NIH Parent announcements (listed below) will be assigned to those ICs on this NOSI that have indicated those FOAs are acceptable and based on usual application-IC assignment practices.

• PA-20-185- NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed)

All instructions in the SF424 (R&R) Application Guide and the listed funding opportunity announcements must be followed, with the following additions:

• For funding consideration, applicants must include NOT-CA-22-037 in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

National Cancer Institute (NCI)

The National Cancer Institute is interested in the validation of digital health tools and AI technologies that are currently or have the potential to be adopted and implemented in real-world settings across the cancer continuum including: cancer risk assessment, screening, early detection and prevention; diagnosis and treatment; cancer control and epidemiology including assessing cancer incidence, prevalence, prognosis, survival, and health disparities. Appropriate topics include, but are not limited to the following:

• Validating digital health technologies and algorithms for measuring cancer-related risk factors across the lifespan. Risk factors include but are not limited to physical activity, sleep, sedentary behavior, weight management, diet/nutrition, sun exposure, and alcohol use.
• Assessing validity and reliability of digital technologies (e.g., sensors, devices, wearables) and AI/ML technologies developed for general purpose but not validated for use in cancer-related contexts and/or in demographic populations impacted by cancer.
• Validating digital health, aglorithms, or AI platforms used in cancer screening, risk assessment, early detection, diagnosis, and treatment
• Validating digital health and/or AI technologies that monitor treatment related adverse events, improvements, and outcomes
• Validating digital and AI platforms that collect, harmonize and aggregate cancer incidence prevalence, and survival
• Validating digital and AI platforms used in cohort identification for clinical studies
• Evaluating and improving AI/ML algorithms to eliminate bias and reduce cancer disparities.
• Validating computational models and AI methods related to measurement error in the assessment and monitoring of cancer risk factors and outcomes.
• Assessing the validity and reliability of biomarkers derived from digital and AI technologies such as for monitoring cancer risk factors, prediction and diagnosis of cancer and early recurrence, and identification and monitoring of symptoms and/or toxicities.
• Validating digital tools, platforms, and technologies to enhance equity and access to cancer care, support clinical decision-making, and evaluate effects on cancer outcomes.

NCI’s FOAs for this NOSI include the following or their subsequent reissued equivalents:

National Heart, Lung, and Blood Institute (NHLBI)

The NHLBI encourages research that addressesthe NHLBI Strategic Vision/Research Priorities and is aligned with these recommended areas for domestic and global HLBS research.Appropriate topics include, but are not limited to following mentioned below:

• Devices and AI/ML algorithms developed for general purpose but not validated for clinical use or in specific disease or demographic populations impacted by HLBS diseases.
• Assessing the validity and reliability of a general-purpose computing technology to diagnose a HLBS condition or recommend treatment using on-board hardware (e.g., the tri-axial accelerometer that simultaneous measurements in three orthogonal directions, for actigraphy or biophysical measurements) or other sensor(s) that operates with embedded processor(s) of a consumer digital device (e.g., a camera, a smart watch, or a wearable device).
• Evaluation and improvement of AI/ML algorithms, including for the identification and elimination of bias, and on the robustness and resilience of these algorithms to withstand changing clinical inputs and conditions.
• Evaluation of AI/ML algorithms used for cardiovascular image reconstruction and image post-processing.
• Evaluation of AI/ML image pattern recognition tools and for the detection and diagnosis of lung and cardiovascular disease.
• Validation of AI/ML algorithms for computer assisted diagnosis.
• Use of point-of-care testing and smart device remote monitoring and management of sickle cell anemia.
• Validation of computational approaches to monitoring, diagnosing and/or managing HLBS disease
• Assess and validate sleep-wake scoring using machine learning approaches for the detection and diagnosis of sleep disordered breathing, and other sleep disorders, in adult and pediatric populations.

• Use of home monitoring and wearable devices to study the relationship between activity level and sleep health.
• Use of wearables and digital tools to study the relationship between glycemic state, physical activity, and sleep health in patients with Type 1 and Type 2 diabetes.

Testing and validations of interventions must use the NHLBI FOAs to address the safety, efficacy, and effectiveness of preventive, therapeutic, and services interventions, selecting the one most appropriate for their application based on the stage of development of the intervention. PIs are strongly encouraged to contact NHLBI Scientific/Research staff well in advance of submitting applications to discuss the match to NHLBI priorities and requirements.

NHLBI’s FOAs for this NOSI include the following or their subsequent reissued equivalents:

National Institute on Aging (NIA)

NIA supports the evaluation of digital health and artificial intelligence (AI) tools and technologies to ensure robustness and reliability of AI methodologies across different datasets with variable resolution, quality of data, and heterogeneous data types. NIA support includes research at: molecular and cellular levels; social, behavioral, psychological, and economic research on processes at the individual and societal levels; and clinical and translational research across the human lifespan. NIA also encourages research that addresses the Alzheimer’s Disease and Related Dementias Milestones (see Milestones 11C and 11D under Biomarkers & Diagnosis ). NIA seeks to facilitate improvement of digital tools to signal the onset and/or progression of AD/ADRD and to allow remote monitoring of real-time data for clinical research.

Research may emphasize, but is not limited to, the following areas:

• Optimization and validation of digital technologies (e.g., Fitbit, accelerometer, digital pen) for collection of data on biometrics, sleep, physical activity, emotion or social activity, cognitive functions in population/epidemiological studies
• Development and validation of non-invasive digital technologies for real time measurement of caloric intake, energy expenditure as clinical endpoints and metabolite profiles as biomarkers in clinical studies
• Evaluations and validations of the clinical effectiveness of digital technologies for management of persons with multiple chronic conditions, particularly for self-management, in care coordination, and when applied in algorithms to support clinical decision-making
• Development and/or validation of digital technology or AI methods for stratifying all individuals at-risk for AD/ADRD across diverse populations.
• Validate digital neuro-markers in MCI and people with dementia to establish sensitivity and specificity and their ability to transmit a detectable signal.
• Assessing the validity, reliability (rigor and reproducibility) and effectiveness of digital health and AI technologies on data substantiating molecular, cellular, and physiological mechanisms that regulate aging.
• Improving the robustness and objectivity of digital health or AI systems that focus on the identification and specificity of aging and longevity biomarkers.
• Thoroughly test digital health technologies and their associated AI tools in pre-clinical animal studies and patient-oriented, phase I or phase II trials.
• Assessing the validity, reliability, and effectiveness of collecting data using smartphones and mobile devices and other sensors in order to reduce respondent burden, with a focus on needs of older adults.
• Developing and improving digital and AI tools to measure social and environmental determinants of health in real time (e.g., geocoding, air quality measures)
• Determining the utility and validity of digital health or AI tools for use in longitudinal and cross-sectional studies (and inter-applicability of cross-sectional and longitudinal data) to measure dynamically changing parameters in big and/or heterogeneous types of datasets

NIA’s FOAs for this NOSI include the following or their subsequent reissued equivalents:

National Institute on Drug Abuse (NIDA)

NIDA encourages research that addresses the institute’s Priority Focus Areas and is specifically interested in the validation of digital health tools and AI technologies that can be used for prevention, diagnosis, and treatment of drug use and its health and social consequences across the spectrum, from occasional use to problematic use and substance use disorders (SUDs). NIDA SBIR/STTR applications will be accepted, if market need is clearly defined and the proposed product has potential for commercialization. PIs are strongly encouraged to contact NIDA Program staff well in advance of submitting applications to discuss the match to NIDA priorities and requirements.

Some examples of areas of interest include:

• Using technology to inform our understanding of both behavioral and neurobiological components of drug use that are strongly influenced by diverse environmental and social factors
• The development and validation of technologies to help individuals gather, manage, and use data and information related to drug use and their personal health
• Diagnostic/monitoring tools and technology platforms to optimize drug use interventions
• The validation of commercializable digital health tools in both research and real-world settings that either have an already developed functional prototype for SUD indication or have been previously developed for other indications and might be repurposed for the SUD population (a strong rationale supporting feasibility and commercialization potential in the SUD field should be provided, as well as a validation plan appropriate to the SUD-specific indication)
• The validation of activities for AI/ML as they relate to drug use and consequences across the spectrum

NIDA’s FOAs for this NOSI include the following or their subsequent reissued equivalents:

National Institute of Mental Health (NIMH)

NIMH encourages research that addresses institute priorities and is aligned with these recommended areas for domestic, global, and AIDS-relatedmental health research. Research may include, but is not limited to, the following areas of interest:

• Validating virtual or augmented reality (VR/AR) systems that use neuro-behavioral feedback to improve patients functioning along specific mental health dimensions
• Develop and validate gamified neuro-behavioral technologies for mental health assessment and wellbeing.
• Evaluating the validity of post-processing analyses of neuro-behavioral recordings for use in developing objective assessment tools, including utility in biomarker development.
• Testing the validity and reliability of a general-purpose computing platform to assess a mental health condition or to predict treatment using on-board hardware (e.g., the tri-axial accelerometer) or other sensor(s) that operates on the embedded processor of a consumer digital device (e.g., a camera, a microphone, a smart watch, or a wearable device).
• Evaluation and improvement of mental health algorithms, including for the identification and elimination of bias, and on the robustness and resilience of these algorithms to withstand changing clinical inputs and conditions.
• Research validating digital health and AI tools and technologies to increase our understanding of the role of social, economic, and structural factors (e.g., structural racism and discrimination, health care access) on the uptake of HIV testing, preexposure prophylaxis (PrEP) and other biomedical HIV prevention strategies among individuals from high-incidence populations.
• Validating digital health tools and AI algorithms, including the identification, mitigation, and elimination of biases, used to optimize PrEP access, uptake, and retention in hard-to-reach populations.
• Validating digital health technologies and AI applications used to assess, monitor, and support antiretroviral adherence and treatment that considers privacy, confidentiality, and equitable access for people with HIV.
• Validating and refining AI algorithms and tools to promote the use of mental health services in low- and middle-income countries (LMICs).
• Assessing the validity of digital technologies to improve the availability and accessibility of mental health services in LMICs.
• Testing and validating AI algorithms and tools to improve the cost-effectiveness and utilization of mental health services in LMICs.

Testing and validations of interventions must use the NIMH FOAs to address the safety, efficacy, and effectiveness of preventive, therapeutic, and services interventions, selecting the one most appropriate for their application based on the stage of development of the intervention, including: PAR-21-135, PAR-21-134, PAR-21-132, PAR-21-131, or PAR-21-129. All other applicants should refer to NOT-MH-18-031 for guidance regarding appropriate funding opportunity announcements.

Note: NIMH has established additional trial design requirements for testing/validating interventions. As such, PIs are strongly encouraged to contact NIMH Scientific/Research staff well in advance of submitting applications to discuss the match to NIMH priorities and requirements.

NIMH’s FOAs for this NOSI include the following or their subsequent reissued equivalents:

National Institute of Nursing Research (NINR)

The NINR encourages the validation of digital health tools and AI technologies that can be used for prevention, diagnosis, treatment, and care of diverse populations across the lifespan. This includes their use in a variety of settings, including hospitals and clinics, but also people’s homes, schools, workplaces, long-term care facilities, and the communities in which they live.

NINR’s FOAs for this NOSI include the following or their subsequent reissued equivalents:

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

NICHD encourages research that addresses the institute’s Strategic Plan and is interested in the validation of digital health tools and AI technologies that have been developed or have the potential to be adopted and implemented in real-world settings across the life span for our population of interest (pregnant and lactating women, children, adolescent, and people with disabilities).

Appropriate topics include, but are not limited to the following validating technologies:

• Screening for children with developmental disorders, co-morbidities associated with IDD such as sleep apnea, obesity, and/or thyroid dysfunction
• Enhance prevention, diagnosis treatment and recovery of injury and illness in NICHD’s population of interest
• Support physical rehabilitation, health management, and community participation for people living in settings with limited resources and clinical support. This may include remote support of therapeutic exercise, assistive devices, periodic feedback on key clinical outcome measures, monitoring of progress, and interactive communication between therapists and the caregiver of the person.
• Improving transition of adolescents to adult healthcare providers by improving health literacy, enabling self-management and encouraging adherence to existing treatments among adolescents.
• Using digital health and/or AI technologies that monitor treatment related to adverse events, improvements, and outcomes in pregnant and lactating women, children, adolescent and people with disabilities
• Using digital technologies (e.g., Fitbit, accelerometer, digital pen) for collection of data on biometrics, sleep, physical activity, cognitive functions in population/epidemiological studies in pregnant and lactating women, children, adolescent, and people with disabilities
• Using digital technologies to evaluate the integration of health care transition processes (e.g., record transfer) and outcomes (e.g., transition completion) with electronic health records or other patient data (such as patient registries, data warehouses, billing data, pharmacy records, administrative records) at the point of care, including effective data sharing and care coordination.
• Using digital health and AI tool and technologies to better understand the uptake of HIV testing and other biomedical HIV prevention strategies, as well as improve the availability and accessibility of HIV services, among adolescents and young adults.
• For preconception health care and natural cycles in women including predicting and monitoring of menstrual cycle and hormone levels and ovulation
• For assessing men’s fertility through at-home technologies to track sperm function
• For image analysis and predicting embryo viability and live birth in IVF
• For digital health tools and AI algorithms to assess, monitor and mitigate risk for infertility disorders and treatments.
• For diagnosis and management of polycystic ovary syndrome.
• For digital health and/or AI technologies to monitor gynecologic health and/or screen/diagnose/treat gynecologic conditions including, but not limited to, endometriosis, uterine fibroids, gynecologic pain syndromes, and pelvic floor disorders.
• For digital health as well as AI tools and technologies to improve clinical decision-making and enhance/accelerate pediatric drug discovery and development, drug repurposing and clinical trials.

PIs are strongly encouraged to contact NICHD Scientific/Research staff well in advance of submitting applications to discuss the match to NICHD priorities and requirements.

NICHD’s FOAs for this NOSI include the following or their subsequent reissued equivalents:

Application and Submission Information

This notice applies to due dates on or after March 7, 2022 and subsequent receipt dates through March 9, 2024.

• For funding consideration, applicants must include NOT-CA-22-037 (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

Investigators planning to submit an application in response to this NOSI are strongly encouraged to contact and discuss their proposed research/aims with Program staff/Scientific Contacts listed on this NOSI well in advance of the application receipt date to better determine appropriateness and interest of the relevantInstitute.

Applications nonresponsive to terms of this NOSI will be withdrawn from consideration for this initiative.

Applications nonresponsive to terms of this NOSI will be withdrawn from consideration for this initiative.

Inquiries

Inquiries

Please direct all inquiries to:

Dana Wolff-Hughes, PhD
National Cancer Institute (NCI)
Telephone: 240-620-0673
Email: dana.wolff@nih.gov

John Haller, PhD
National Heart, Lung, and Blood Institute (NHLBI)
Email: john.haller@nih.gov

Nalini Raghavachari, PhD
National Institute on Aging (NIA)
Telephone: 301-496-6942
Email: nalini.raghavachari@nih.gov

Susan Wright, PhD
National Institute on Drug Abuse (NIDA)
Telephone: 301-402-6683
Email: susan.wright@nih.gov

Kristopher Bough, PhD
National Institute of Nursing Research (NINR)
Telephone: 301-496-2604
Email: kristopher.bough@nih.gov

Michele Ferrante, PhD
National Institute of Mental Health (NIMH)
Telephone: 301-828-7365
Email: michele.ferrante@nih.gov

(For NIMH Non-AIDS applications)

Lori A. J. Scott-Sheldon, Ph.D.
National Institute of Mental Health
Telephone: 301-792-2309
Email: lori.scott-sheldon@nih.gov

(For NIMH AIDS and Global Mental Health applications)

Sujata Bardhan, PhD
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Telephone: 301-435-0471
Email: sujata.bardhan@nih.gov