2009 — 2011 |
Gottfredson, Nisha Claire |
F31Activity Code Description: To provide predoctoral individuals with supervised research training in specified health and health-related areas leading toward the research degree (e.g., Ph.D.). |
Modeling Non-Random Missingness in Experience Sampling Research On Substance Use @ Univ of North Carolina Chapel Hill
DESCRIPTION (provided by applicant): For over half a century, psychologists have theorized a link between daily emotion (e.g., stress, negative affect) and substance use. More recently, risk and protective factors including coping strategies and cognitive expectancies have been identified as potential moderators to the emotion-substance use relationship. Recent advances in multilevel statistical modeling techniques and experience sampling methodology (e.g., diary studies) have resulted in a flurry of research applications designed to test a variety of emotion-substance use relations at the intra-individual level. This important development allows a more nuanced understanding of the etiology of substance use that was not possible with inter-personal studies. However, diary studies may be especially prone to nonignorably missing data (i.e., the most troubling kind of missing data) for a number of reasons. First, the sensitive, and sometimes criminal, nature of the measures makes disclosure somewhat risky. Second, ecological assessments of substance use rely on self reports from intoxicated or "high" individuals. Nonignorable missingness leads to biased inferences regarding the relationship between emotion, substance use, and moderators. Recently, researchers in the area of clinical trials have utilized latent class pattern mixture models (LCPMMs) to obtain unbiased parameter estimates even in the presence of nonignorably missing data. LCPMMs have worked in this context by accounting for conditional dependencies between dropout patterns and outcome trajectories with latent class variables. Within-class estimates are aggregated to obtain unbiased overall estimates. While promising, LCPMMs have not yet been applied to experience sampling datasets. The proposed project has three specific aims. The first is to conduct a thorough review of the characteristic types and patterns of missingness in experience sampling datasets which examine substance use. The second aim is an extension of the LCPMM framework to accommodate these types and patterns of missing data. The final aim is to more rigorously test the self medication hypothesis by reanalyzing two datasets that were previously analyzed under questionable assumptions about the missing data mechanisms. PUBLIC HEALTH RELEVANCE: The proposed project will make unique substantive and quantitative contributions. Substantively, this project will reliably measure the effects that day-to-day emotional fluctuations have on substance use behaviors and the role of potential risk and protective factors in this process. This knowledge will reveal new ways to effectively design and implement interventions to prevent substance abuse. The substantive analysis will provide a vehicle for demonstrating and disseminating quantitative advances.
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0.988 |
2014 — 2018 |
Gottfredson, Nisha Claire |
K01Activity Code Description: For support of a scientist, committed to research, in need of both advanced research training and additional experience. |
The Impact of Affect Regulatory Mechanisms and Binge Eating On Drug Recovery @ Univ of North Carolina Chapel Hill
DESCRIPTION (provided by applicant): Accumulating evidence increasingly supports a strong and multifaceted association between drug dependence and binge eating. Epidemiological investigations confirm high comorbidity between binge eating and substance use disorders; animal models reveal that addiction-like behaviors generalize across substances (including food); and neurobiological studies reveal common reward pathways for both food and illicit drugs. To better define the nature of this association, the current proposal examines daily behavioral associations between the most commonly abused drug among treatment populations, namely opiates, and binge eating behaviors. Specifically, I will investigate how abstinence from opiates among individuals recovering from addiction is associated with affect-driven binge eating, and how these dysregulated eating patterns are longitudinally associated with risk for opiate lapse and relapse. My research aims are to: 1) establish an ideal assessment system for quantifying daily caloric and nutritional intake and binge eating behavior in individuals recovering from opiate addiction; 2) test whether the relationship between negative affect and binge eating differs for individuals recovering from opiate addiction and demographically-matched controls; 3) identify time- specific (e.g., stress) and person-specific (e.g., personality) factors that exacerbate affect-driven binge eating as moderated by addiction status; and 4) test the effects of various coping strategies, and particularly the practice of affet-driven binge eating, on the longitudinal risk for relapse among those in recovery from opiate addiction. To accomplish these aims, I will study a minimum of N = 106 participants from two treatment facilities for drug addiction and N = 35 demographically comparable healthy controls from a community sample in the geographic vicinity of the treatment centers. Participants will respond to electronic signals three times daily over fourteen consecutive days by answering questions about drug use and cravings, negative affect, and eating behaviors occurring during the day. Individuals recovering from addiction will also be assessed every two weeks over 24 months to determine ongoing risk for relapse, substance use and other health outcomes. By integrating my proposed research plan with a plan to receive formal training and mentorship from faculty with expertise in affective models of drug use, the treatment of drug addiction, binge eating, and the neurobiology of addiction, the proposed Career Development Award complements my background in quantitative methodology by providing training I need to develop an independent and productive line of research dedicated to understanding the co-occurrence of addictive and binge eating behaviors, particularly in relationship to affect dysregulation. The research and training that I propose will lead to the submission of an R01 proposal that will provide a transition to an independent research career and serve to inform clinicians and clients about the ways in which binge eating influences recovery efforts for individuals in treatment for opiate use disorder.
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0.988 |
2021 |
Gottfredson, Nisha Claire |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Building a Reinforcement Learning Tool For Individually Tailoring Just-in-Time Adaptive Interventions: Extending the Reach of Mhealth Technology For Improved Weight Loss Outcomes @ Univ of North Carolina Chapel Hill
Project Summary Excess weight is associated with 13 types of cancer. These cancers disproportionately affect Black and Latinx individuals, as well as those with lower socioeconomic status, because overweight and obesity incidence is higher in these groups. Behavioral weight loss interventions are effective, but in-person interventions tend to have low reach. As mobile phone ownership is increasing in the United States, mHealth technology holds promise for reaching a larger population than in-person behavioral interventions. Furthermore, because they travel with individuals and can collect digital information in real-time, mHealth tools make it possible to intervene with individuals at the precise point when the interventions are needed with just-in-time adaptive interventions (JITAIs). As currently implemented, mHealth JITAIs are adaptive in the sense that interventionists can specify decision rules a priori that result in intervention messages that are triggered or tailored by certain events. These experimenter-specified decision rules are generally based upon results of prior studies, specifically micro-randomized trials that provide sequential tests of mHealth intervention messages in order to determine causal effects of messages conditional on user context. However, JITAIs that are developed in this manner cannot be truly individually tailored because the same decision rules are equally applied to everybody without regard to information about how individuals actually respond to intervention messages. Rapidly evolving machine learning methods, specifically reinforcement learning (RL), makes it possible to improve upon the current approach to JITAIs by learning each person's unique response patterns and integrating this information into subsequent, person-specific, adaptive decision rules. However, the few behavioral interventionists who have created mHealth JITAIs for weight loss using RL have noted high practical barriers to doing so because implementation of RL requires specialized expertise and can be labor intensive. The field needs a user-friendly tool to reduce these barriers in order for RL methodology to become widely adopted. Aim 1 is to develop Adapt, a tool that iteratively integrates real-time data, applies RL algorithms, and performs micro-randomized trials to optimize JITAI decision rules for weight loss. Adapt will pull in digital health data in real-time and conduct micro-randomized trials using behavioral patterns and outcomes to arrive at the most efficacious intervention message, delivered at the right time, for promoting weight loss in each participant. Aim 2 is to conduct a 12-week pilot feasibility study testing usability of Adapt in a weight loss intervention (NudgeRL). NudgeRL will build upon the team's existing JITAI, Nudge, which did not incorporate RL. The sample will consist of 20 adults with overweight or obesity, at least 50% of whom are Black or Latinx. Although Adapt will be developed to improve weight loss interventions, its widespread use will result in more efficient and efficacious JITAIs across a broad range of health outcomes, resulting in a lower burden of cancer and other disease due to a wide spectrum of improved health behaviors.
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0.988 |