Joaquin A. Anguera, Ph.D. - US grants

DESCRIPTION (provided by applicant): Over two million people in the US download health apps onto their smartphones and tablets, with the intent of improving their quality of life. Despit widespread use of these apps, there is relatively little information regarding app user access (do users download health apps and use them more than once), app user engagement (do users follow the app protocols) and app impact on mood, cognition and daily functioning. Our long-term goal is to conduct a future randomized controlled trial investigating access, engagement and impact of two types of mental health apps, apps based on evidence-based therapeutic principles (i.e.: Problem Solving Therapy) and apps based on cognitive neuroscience principles of depression (i.e.: a cognitive training game called Evolution) and compare both to an information only app. Our intent is to conduct this study entirely on mobile devices, in order to investigate access, engagement, and impact in an ecologically valid manner. The purpose of this pilot study is to test the feasibility of conducting our future randomized controlled trial comparing three mobile mental health apps for the management of depressed mood, improvement of cognitive control, and improvement in activities of daily living in people aged 18 and older. Recruitment, consent, randomization, app use and outcome assessment will be conducted entirely on mobile devices. We will recruit 150 people through four different recruitment avenues to determine which avenue results in the most representative sample of our target population (people 18 and older who have symptoms of depression that are interfering with their quality of life). We will also determine the number we need to recruit to have a final sample of 150 people willing to be randomized between the 3 apps and complete an 12-week study of app impact on mood, cognition and function. This pilot will provide information on the completeness of data from a study conducted in this manner, and uncover any other challenges we may face by using mobile devices for data collection, and if we will find differential drop out between app type (e.g.: will more people stop using of the information only app prematurely?). Although we will not have sufficient statistical power to answer questions about comparative effectiveness between the apps, we plan to explore relationships between sample demographics, app use, and improvement in cognitive control on improvements in mood and function.

Project: EVO (or ?EVO?) is a mobile 3D video game that has been shown to reduce older adults' susceptibility to interference by augmenting sustained attention and working memory abilities (e.g. cognitive control) through targeted adaptive algorithms. The combination of peer-reviewed validity, adaptivity, and fun video game mechanics elevates the EVO platform beyond other at-home training tools while reducing burden associated with tedious task replication. We propose to study EVO as a potential intervention for the treatment of depression, a disorder that worsens medical outcomes, promotes disability, increases expense, and complicates medical care by clouding the clinical picture and undermining treatment adherence. R61 Phase: We will first conduct a 2-year proof of concept study to determine if EVO can engage the cognitive control network (CCN) in 30 middle-aged and older adults with major depression. Primary aims for this phase of the proposed project are to determine if EVO will result in greater CCN engagement using three levels of analysis (circuitry, performance, self-report). At the circuitry level, we will measure CCN engagement by probing the system using task-based fMRI. We hypothesize that activation and functional connectivity (FC) of anterior aspects of the CCN will increase from baseline to 4-weeks after treatment initiation. Our decision to move to the next phase of the planned study is that 66% of our sample will show significant increases in CCN functions at the circuitry level of analysis (CCN activation and FC) and at either the performance level or self- report level of analysis. R33 Phase: Should our proof of concept phase pass the Go/No-go rule, we will then conduct a 3-year pilot study to compare EVO to an expectancy-matched control game in terms of CCN target engagement at the circuitry (task-based fMRI) and behavioral levels (task performance, self-report) in 60 middle-aged and older adults with major depression. In addition, we well determine if changes in target engagement are associated with changes in mood and mood-induced disability. The decision to move onto development of a proposal to study the clinical efficacy of EVO in a larger randomized clinical trial will be based on whether we find (1) that EVO out-performs our control condition in terms of the engagement of CCN at the circuitry and behavioral levels (2) significant associations between changes in engagement of the CCN and changes in mood and (3) that the study methods are feasible to complete (sampling rate, retention, intervention adherence, intervention acceptability and expectancy-match for our control condition).

ABSTRACT The goal of this project is to conduct a pilot study to test the utility of a Natural Language Processing (NLP) clinical messaging tool to improve the reach and the quality (fidelity and competency) of coaches providing behavioral activation (BA) strategies through a mobile mental health app called Personalized Real-time Intervention for Motivational Enhancement (PRIME). Despite decades of research and development for the treatment of mood disorders, depression has risen from the 5th leading cause of disability to the leading cause in the US. Critical barriers for progress in the field are 1) Poor access to high quality care for consumers; 2) Limited mental health workforce; and 3) Few providers are trained in the delivery of evidence-based treatments. In response to this public health problem, our team is proposing to use a mobile platform to improve access to an evidence based treatment; increase the reach of mental health coaches while supporting the delivery of higher quality of care. We will improve the reach of clinicians by using a NLP-powered messaging tool, which will ensure fidelity to the BA model and clinicians will have access to real-time clinical information about their patients to guide more targeted treatment (competence). By improving the efficiency and competence of mental health coaches in the context of mobile mental health services, we expect patients to make greater gains in depression, mood-related disability, and in achieving personal goals. In order to evaluate the feasibility, tolerability, and overall impact of the enhancements to PRIME, we will spend Year 1 engaging stakeholders in a human centered iterative design process and spend Years 2-3 conducting a pilot randomized controlled trial in which we will randomize 120 individuals with depression to either receive 8- weeks of 1) PRIME 1.0 (current version) OR 2) PRIME 2.0 (version with the NLP-powered clinician dashboard). Using BRIGHTEN, our mobile clinical trial platform, we will recruit participants, consent, screen, evaluate and deliver the intervention entirely remotely. The pilot data from this study will be used to prepare for a R01 study to definitively determine whether PRIME may be used to efficiently and effectively improve outcomes for a significantly larger number of individuals with depression.

Project Summary Deficits in cognitive control, those abilities that allows one to engage in complex, goal-directed behavior, factor prominently in the functional declines experienced by older adults (OAs). Given that the older human brain still has the capacity to adapt, there is a critical need put into practice evidence-based approaches to help keep these individuals cognitive healthy, productive, and independent. Achieving this goal requires the development of targeted, accessible interventions to slow or reverse declines in these cognitive control processes. However, while telemedicine and internet-based approaches have been shown to be as effective as in-person treatment, there is virtually no known information about the optimal protocols for implementing self-administered mobile cognitive assessments or interventions in community settings like a senior center, or the feasibility of even attempting such efforts. The goal of this R21 is to test the feasibility of a targeted digital health remediation program for the older adults in senior living communities to enhance cognitive control abilities based upon an initial characterization of these abilities. To test this approach, we will use both custom assessments and interventions that are designed to be 100% self-administered via mobile devices. We will collect data from 120 OAs from the Brookline Senior Living communities, the largest owner and operator of senior living communities across the United States (1000+ communities, 100,000+ residents) where the average age of residents are 85 years old. Participating individuals will complete cognitive assessments and be randomly assigned to one of three training groups: directed training (DT), non-directed training (NDT), or an expectancy-matched placebo control group (PC). The total training experience will encompass 6 weeks of training (3 days/week), with each training session lasting ~30 minutes. All groups will complete baseline and immediate follow-up assessments of cognitive and functional outcomes. Evidence of feasibility here using these unique methodological approaches would provide empirical evidence supporting the basis for a larger-scale implementation of such digital health technologies into senior community settings.

Effective measurement of cognitive abilities is fundamental to effective diagnostics, risk assessment and evaluation of interventions targeted towards older adults (OA) and in particular those with Alzheimer's Disease and related dementias (ADRD). With the ubiquitous availability of smartphone/tablet technology in modern society a proliferation of mobile cognitive assessments from companies, healthcare providers, and researchers are being developed. However, a difficulty in evaluating such interventions, and in particular making comparison between them is the lack of standardization/interoperability of assessment tools. This is especially the case for early stage/mechanistic studies where it is common for investigators to each use their own labs' toolset to evaluate intervention outcomes. Here we address particular needs in the field of cognitive training, as well as for other longitudinal assessments focused on OA, where the limited standardization and accessibility of cognitive outcome measures makes it difficult to evaluate effectiveness of interventions. This R21/R33 infrastructure proposal seeks to develop shared tools to facilitate effective translation and sharing of cognitive assessment and training procedures. We accomplish this by leveraging technologies, existing assessment batteries, and know- how from 3 groups that have each independently developed robust systems for cognitive assessment and training that can run on mobile devices (UCR Brain Game Center, UCSF Neuroscape Center, and UCI Working Memory and Plasticity Lab). We target development of systems that allow for interoperability of assessments, enrollment/participant tracking systems, data visualization, and participant compliance systems. In the R21 phase, we aim to develop such systems and demonstrate that they can be effectively shared across labs, and in the R33 phase, these systems will be both tested for robustness in large scale-research projects that will now be able to share outcome measures, and for developing personalized, precision training approaches for participants based upon these assessments. Further, these systems will be documented and will be shared with other scientists groups to reduce the barrier of entry for other groups. The long-term impact of this work will be an infrastructure that will support better comparison across studies of cognitive training, as well as other interventions that are increasingly being used to ameliorate cognitive declines in older adults such as those related to ADRD. The key value of this system compared to others is that it will simultaneously support the flexibility required for basic research, by facilitating groups to continue to use their own lab's software systems, while at the same time providing them with a powerful infrastructure for sharing that allows them to incorporate assessments, server infrastructure and compliance tools into their own studies. This will facilitate comparisons across studies using common outcome measures as well as the ability to use the same assessments in numerous other domains including risk-assessment and longitudinal testing in older adults at risk for ADRD.