Todd Manini - US grants

DESCRIPTION (provided by applicant): Pre-clinical disability is an early warning system in the disablement process as it is characterized by selecting to perform everyday tasks less often and compensating for those tasks still being performed. This phase serves as an ideal target for preventative strategies because treatments can be designed for individuals on the verge of disability thus interrupting the occurrence of outright disability. One such strategy that optimizes the transfer of adaptations to real-life situations is task-specific exercise (TSE). This type of intervention holds promise to determine how pre-clinically disabled older adults might interrupt the disablement process and instead begin an enablement process and thus lead us to better interventions to treat and prevent disability from occurring. However, because of the complexity of the disablement process, it has been extremely challenging to objectively identify outcomes that represent changes in selection, optimization, and compensation of tasks (the SOC domains). The objective of the current application is first, to ask what are the short and long-term responses of TSE in the pre-clinically disabled older adults (aged 60+ years) using a single-masked randomized controlled design. Second we seek to refine and validate our outcomes using non-invasive monitoring of SOC domains with the Intelligent Device for Energy Expenditure and Activity (IDEEA). Thirdly, we will shed exciting new light on whether TSE alters neuromechanical and psychological factors. We want to know this, in part, from a mechanistic perspective to gain insight into the processes by which TSE improves disablement outcomes. Also, this will help us to better understand how to enhance the TSE intervention to treat pre-clinically disabled patients. Thus, we offer two hypotheses: Hypothesis #1: TSE increases selection and optimization of everyday tasks, while reducing compensation to achieve task performance. Hypothesis #2: TSE mediates changes in SOC domains through both neuromechanical (decrease variability of movement and increase muscle work capacity) and psychological (increased confidence to perform tasks and increased executive function) properties. These data are expected to guide us in designing a randomized controlled trial that will test whether TSE in the pre-clinically disabled can reduce future incidence of outright disability. PUBLIC HEALTH RELEVANCE: The proposed project will develop a trajectory of research that will identify individuals on the version of physical disability and intervene with exercises specifically designed to improve everyday task performance. This research is highly relevant to the public health as it will build evidence for extending the capacity to live independently.

DESCRIPTION (provided by applicant): Americans spend over 90% of their activity related energy expenditure performing common daily activities. Health care professionals use normative data as a guide to prescribe appropriate activities for patients and clients. Additionally, scientists use this resource to plan physical activity or nutritional interventions ad apply these estimates to epidemiological research. However, while normative data have existed for over 20 years and are seemingly accurate in young adults, they can lead to misguided estimates of the metabolic cost of daily activities in older adults. This is not a trivial issue sice physical activity is one of the only known modalities to improve physical function in older adults and plays a critical role in regulating body weight. However, there is a serious lack of information pertaining to potential age-related differences in the metabolic cost of daily activitis. This leaves a major gap in knowledge for properly prescribing physical activity for a population that has elevated risk cardiopulmonary and orthopedic impairments. The primary goal of this project is to test the hypothesis that aging is associated with a difference in the metabolic cost f doing exercise and lifestyle activities. We will assess pulmonary gas exchange in 210 adults aged 20 to 80+ years with a portable indirect calorimeter worn while performing 38 daily activities. We will examine the metabolic equivalent (MET as a function of 3.5 milliliter min-1kg-1), metabolic economy (energy expended for a given work rate) and relative metabolic cost (as a function of resting and peak oxygen consumption) for each task as a function of age. Secondly, we will address how metabolic costs of daily activities are affected by having functional impairments by testing an additional 90 older adults (60+ years) with functional impairment. Thirdly, because scientists and public health officials alike rely on perception-based exertion to monitor intensity of physical activity, we will address the question- Is aging associated with inaccuracies for self-gauging perceived exertion? Addressing this question will gain insight into a better delivery system for recommending physical intensity to older adults. Lastly, the design and comprehensive metabolic measurements being proposed will provide an unprecedented opportunity to validate accelerometers for estimating the type and intensity of physical activity. Using new mathematical techniques that apply machine learning approaches (random forests, support vector and multiple kernel learning techniques), we will assess the potential to reduce the error in estimating the type and intensity of physical activity as compared to traditional methods. There are many end products of this research. First, the work will produce the largest dataset of metabolic cost for daily activities in 60+ years old. Second, an age-correction factor for metabolic costs will be created to apply to hundreds of tasks that fall into similar categories as those being evaluated. Finally, the work will refine the tools needed to feasibly assess physical activity in young and old adults. These accomplishments will directly impact the fields of epidemiology, geriatric medicine, rehabilitation, and nutritional sciences.

DESCRIPTION (provided by applicant): The prevalence of cardiovascular disease will rise as life expectancy of older Americans continues to increase, with persons aged >75 years representing the fastest growing segment of the US population. Poor physical fitness is a major contributor to poor cardiopulmonary function that is primarily caused by a sedentary lifestyle. Increasing physical activity remains a priority for improving cardiovascular health, especially in older adults, who are at the greatest risk of chronic health conditions. While it is generally recognized that physical activity benefits adults regardless of age, sex, race/ethnicity, or health status, the cardiopulmonary responsiveness varies greatly. Approximately 40% of people do not achieve a clinically meaningful benefit despite excellent adherence. On the other hand, 30% of people with poor adherence respond better than expected. Based on our previous observation that both common and rare nonsynonymous mitochondrial DNA (mtDNA) variants are associated with physical activity energy expenditure and cardiopulmonary outcomes, we postulated that these variants are likely to identify cardiopulmonary responsiveness to chronic physical activity. These data formed our central hypothesis that mtDNA sequence variation explains a portion of the heterogeneity in cardiopulmonary responsiveness to chronic physical activity. We have a unique opportunity to test our central hypothesis efficiently and cost-effectively by sequencing the entire 16.5kb of mtDNA in stored samples of participants in the Lifestyle Interventions and Independence for Elders Study (The LIFE study). The LIFE study is a definitive Phase 3 multicenter single-masked Randomized Controlled Trial that evaluates a physical activity program vs. a successful aging health education program. The average follow-up duration of the study is approximately 2.7 yrs, and the participants are 1,592 community-dwelling sedentary persons aged 70-89 yrs with stored genetic material. The completed LIFE Pilot study-a cohort of 396 participants randomized to the same interventions for 12 months-will be used to replicate significant associations. Our hypotheses address the effect of common and rare mtDNA variants on responsiveness to the following cardiopulmonary measures that are being collected as part of the trial: 1) walking speed, 2) blood pressure, and 3) pulmonary capacity. We will integrate clinical, behavioral, and genetic data in models to predict the heterogeneity in cardiopulmonary responsiveness to physical activity. By identifying genetic modifiers, this research will provide a starting point to build a personalized medicine framework to better improve cardiovascular health with physical activity. Identifying these genetic factors may also provide novel insights into the molecular pathways that regulate the cardiovascular adaptation to chronic physical activity. This approach could have a large impact in moving the field toward the NIH's goals of personalizing behavioral interventions for a rapidly aging America.

ABSTRACT Data Science and Applied Technology (DSAT) Core (RC4), a recent addition to the University of Florida (UF) Older Americans Independence Center (OAIC), provides an interactive data and technology ecosystem aimed at preserving mobility and preventing disability. Big data initiatives, applied technologies, and new methodological approaches for data science have grown rapidly in many various environments, and the world is moving toward a connected system of computing and sensing components. The broadly used term ?Internet of Things? refers to an environment in which detailed data are collected on health, activity, location, and other aspects of the participating entities. Flexible control of the different interconnected and frequently communicating components can provide a rich set of applications that can adapt dynamically to their environment. Additionally, mobile health (mHealth, smartphones and smartwatches) technologies are changing the landscape for how patients and research participants communicate about their health in real time. These possibilities have led the NIH to put forth large initiatives (Big Data to Knowledge (BD2K) and The Precision Medicine Initiative Cohort Program) for meeting this new demand for knowledge. DSAT investigators provide OAIC leadership to assure that researchers in Geriatrics in general, mobility and disability are prepared for the rapid advances in these expanding technologies. The RC4 provides many unique attributes, such as: developing software for interactive mobile technology (e.g., wearable sensors that are programmable in real time); validating new sensing technology; warehousing data; repurposing data; and applying machine learning techniques to domain problems. DSAT provides a central hub of expertise in computer science, biomedical engineering, biomedical informatics, data science, applied technology, epidemiology, and content expertise in the assessment of mobility to: ? Support OAIC cores, train Junior Scholars, and provide outreach to researchers and practitioners; ? Advance interactive monitoring for assessing mobility phenotypes; ? Warehouse and integrate multimodal data; ? Conduct machine-learning and pattern-discovery analyses; ? Harvest electronic health record (EHR) data to identify and recruit participants; ? Repurpose high-resolution biomedical data and physiological signals to derive mobility phenotypes; and ? Enhance externally supported projects. There is a growing demand for data science and applied technology for meeting the challenge of preserving mobility and preventing disability. The DSAT Core adds a highly innovative aspect to this challenge that will lead it into the future of connected systems of computing, sensing and biomedical informatics.

Project Description: Mobility impairments in older adults decrease quality of life and are associated with high societal and economic burden. NIH RFA-AG-18-019 solicits applications ??to investigate the central neural control of mobility in older adults?using innovative and cutting-edge methods.? Current approaches to study the neural control of walking are limited by either the inability to measure people during walking (functional magnetic resonance imaging, fMRI) or the inability to measure activity below the cortex (functional near- infrared spectroscopy, fNIRS). We assert that a full and accurate understanding of the neural control of walking in older adults requires real time measurement of active regions throughout the brain during actual walking. We will achieve this by using innovative mobile brain imaging with high-density electroencephalography (EEG). This approach relies upon innovative hardware and software to deliver three-dimensional localization of active cortical and subcortical brain regions with high spatial and temporal resolution during walking. The result is unprecedented insight into the neural control of walking. Here, our overarching objective is to determine the central neural control of mobility in older adults by collecting EEG during walking and correlating these findings with a comprehensive set of diverse mobility outcomes (clinic-based walking, complex walking and community mobility measures). Our first aim is to evaluate the extent to which brain activity during actual walking explains mobility decline. In both cross sectional and longitudinal designs, we will determine whether poorer walking performance and steeper trajectories of decline are associated with the Compensation Related Utilization of Neural Circuits Hypothesis (CRUNCH). CRUNCH is a well-supported model of brain activity patterns that are seen when older individuals perform tasks of increasing complexity. CRUNCH describes the over-recruitment of frontoparietal brain networks that older adults exhibit in comparison to young adults, even at low levels of task complexity. CRUNCH also describes the limited reserve resources available in the older brain. These factors cause older adults to quickly reach a ceiling in brain resources when performing tasks of increasing complexity. When the ceiling is reached, performance suffers. The RFA also calls for proposals to ?Operationalize and harmonize imaging protocols and techniques for quantifying dynamic gait and motor functions?. In accordance with this call, our second aim is to characterize and harmonize high-density EEG during walking with fNIRS (during actual and imaged walking) and fMRI (during imagined walking). This will allow us to identify the most robust CRUNCH-related hallmarks of brain activity across neuroimaging modalities, which will strengthen our conclusions and allow for widespread application of our findings. Our third aim is to study the mechanisms related to CRUNCH during walking. Thus, our project will address a majority of the objectives in NIH RFA-AG-18-019 and will identify the neural correlates of walking in older adults, leading to unprecedented insight into mobility declines and dysfunction.

ABSTRACT Older Americans experience approximately 29 million falls and 13 million hospitalizations per year. These intervening health events (IHE - episodic falls, injuries, illnesses, and hospitalizations) are strong precipitants of disability in older adults. Because of their episodic nature, IHEs are extremely difficult to study. Continuous, long-term monitoring with remote capabilities using wearable technology is an ideal solution for capturing information surrounding an IHE and in particular, preceding it. This R21/R33 project aims to develop a sustainable research infrastructure built on the foundation of a smart watch application and server called ROAMM (Real-time Online Assessment and Mobility Monitor). It will offer long-term and continuous connectivity, bidirectional interactivity and remote programming. ROAMM will create a detailed narrative about mobility (activity patterns, walking speed, life space), patient reported outcomes/symptoms (pain, poor mood, fatigue, disability), cognition (working memory, processing speed, and executive functioning) and reports of health events (falls and hospitalizations). The infrastructure is composed of a diverse group of investigators with expertise in mobile technology/data science and applied/medical sciences who will serve in the following cores: Wearable Technology, Phenotyping, Clinical Outcomes, Data Science Management & Quality, and Recruitment, Retention & Compliance. In the R21 phase, we will create the ROAMM framework consisting of the watch application and accompanying server. We will also assess test-retest reliability, convergent validity and participant usability/acceptability. Each year, an Independent Advisory Panel and External Advisory Committee will evaluate milestone-driving activities and our Go/No-Go checkpoints for transitioning to the R33 phase. Work proposed in the R33 phase will showcase the ROAMM infrastructure by conducting a prospective, longitudinal study (range 1.25-2.5 yrs) in 200 community-dwelling persons aged 70+ yrs. This phase will test a field deployable version of ROAMM in real world settings to address the following hypotheses: 1) Pre-event patterns of low mobility, disability, fatigue, pain and depressive mood collected by ROAMM are independent predictors of incident IHE's; 2) IHE's will negatively impact the course of ROAMM measures; and 3) Additional value will be gained for explaining the change variability and recovery trajectories. An exploratory aim will evaluate safety while using ROAMM features and identify predictors of ROAMM adherence using both key-informant interviews and examine demographic and health histories to create boundaries for using ROAMM and other systems like it for long-term, continuous monitoring in research and practice. We will sustain ROAMM by targeting grant opportunities for the wearable technology surge for remote patient interaction, adopting licensing fees, and aligning our services with larger entities to become the go-to place for remote data capture. These activities will create a sustainable infrastructure to ensure research on older adults is keeping pace with the state-of-the-art ?smart and connected? health with wearable technology.

Preserved mobility is one of hallmarks of geriatric care, gerontology and geroscience. The loss of mobility with aging is progressive, caused by multiple factors and does not have a simple cure. Unfortunately, mobility loss continues to lack clinical attention, robust biomedical targets, objectively-measured surveillance systems, and effective treatments. As a result, mobility difficulties have remained persistently high and stagnant since it was systematically measured in the late 1980's. Currently, 30% of Americans aged 60-69, 40% of individuals aged 70-79, and 55% of individuals age 80 or older report difficulties with their mobility (e.g. walking and climbing stairs). To address this unmet need, we propose the Translational Research training on Aging and Mobility (TRAM) postdoctoral training program to train 4 post-doctoral fellows per year (2 in year one). The overall goal of the TRAM program is to develop outstanding independent investigators capable of sustaining productive multi-disciplinary and translational research careers addressing the multi-factorial causes and consequences of age-related changes in mobility and/or designing multi-modal interventions to prevent and rehabilitate mobility impairments in older adults. The goals are to: 1) Provide a 2-3 year integrated training program for PhD/MD fellows to create a career pathway for conducting mechanistic and clinically relevant translational research in mobility and aging; 2) Implement a cross-fertilized training program based on the Experiential Learning Theory; 3) To equip trainees with new research skills along with the knowledge and expertise to address impactful and unanswered questions regarding mobility and aging; 4) Closely monitor and track trainee-related experiences and outcomes for making continuous quality improvements; 5) Create a culture for professional excellence and development based on enhancing rigor, reproducibility and transparency in trainee-related research and; 6) To attract, recruit and enroll minorities, and those with disabilities and disadvantaged backgrounds. TRAM program faculty are collaborators on each other's projects, bring strong mentorship experience and successful commitment to research related to mobility and/or aging. Program faculty are grouped into either Aging or Mobility Research Clusters based on research focus and expertise. TRAM will use a mosaic mentoring approach that will employ dual primary mentors? one from ?aging? and another from ?mobility? expertise? a third mentor will serve as an advocate/sponsor. Mentees will also receive support from other archetypes like coaches, connectors and senior peer mentors. This unified mentoring team will guide trainees through an individual development plan, didactic coursework (e.g. mechanistic and clinical-based research on aging and/or mobility, ethics, responsible conduct of research), directed research training, and professional development activities (e.g. strategic planning, innovative leadership) that will be tailored according to the educational needs and research interests of the trainee. At completion, TRAM fellows will fulfill the scientific needs and grow the research workforce for meeting the growing population of mobility impaired older adults.