Using Mobile Technology to Better Understand and Measure Self-Regulation

Applying Novel Technologies and Methods to Inform the Ontology of Self-Regulation - Aim 2 Non-lab Study: Using Mobile Technology to Better Understand and Measure Self-Regulation

National Institutes of Health (NIH), National Institute on Drug Abuse (NIDA), University of Memphis, Ohio State University

This study will evaluate the extent to which we can engage and manipulate putative targets within the self-regulation domain outside of laboratory settings in samples of smokers and overweight/obese individuals with binge eating disorder. Fifty smokers and 50 overweight/obese individuals with binge eating disorder will be recruited to participate in a non-lab experimental paradigm in which we will leverage our novel mobile behavioral assessment/intervention technology platform. We will measure and modulate engagement of potential self-regulation targets and collect data in real time and in real-world conditions. Mobile sensing will be added to up to 50 additional participants.

Health risk behavior, including poor diet, physical inactivity, tobacco and other substance use, causes as much as 40% of the illness, suffering, and early death related to chronic diseases. Non-adherence to medical regimens is an important exemplar of the challenges in changing health behavior and its associated impact on health outcomes. Although an array of interventions has been shown to be effective in promoting initiation and maintenance of health behavior change, the mechanisms by which they actually work are infrequently systematically examined. One promising domain of mechanisms to be examined across many populations and types of health behavior is of self-regulation. Self-regulation involves identifying one's goals, and maintaining goal-directed behavior. A large scientific literature has identified the role of self-regulation as a potential causal mechanism in promoting health behavior. Advances in digital technologies have created unprecedented opportunities to assess and modify self-regulation and health behavior. In this project, we plan to use a systematic, empirical process to integrate concepts across the divergent self-regulation literatures to identify putative mechanisms of behavior change to develop an overarching "ontology" of self-regulatory processes. This multi-year, multi-institution project aims to identify an array of putative psychological and behavioral targets within the self-regulation domain implicated in medical regimen adherence and health behavior. This is in service of developing an "ontology" of self- regulation that will provide structure and integrate concepts across diverse literatures. We aim to examine the relationship between various constructs within the self-regulation domain, the relationship among measures and constructs across multiple levels of analysis, and the extent to which these patterns transcend population and context. The project consists of four primary aims: Aim 1. Identify an array of putative targets within the self-regulation domain implicated in medical regimen adherence and health behavior across these 3 levels of analysis. We will build on Multiple PI Russ Poldrack's pioneering "Cognitive Atlas" ontology to integrate concepts across divergent literatures to develop an "ontology" of self-regulatory processes. Our expert team will catalog tasks in the self-regulation literature, implement tasks via online testing (Mechanical Turk) to rapidly obtain large datasets of self-regulatory function, assess the initial ontology via confirmatory factor analysis and structural equation modeling, and assess and revise the resulting ontology according to neural similarity patterns across tasks (to identify tasks for Aim 2). Aim 2. Evaluate the extent to which we can engage and manipulate putative targets within the self-regulation domain both within and outside of laboratory settings. Fifty smokers and 50 overweight/obese persons with binge eating disorder will participate in a lab study (led by Poldrack) to complete the tasks identified under Aim 1. We will experimentally modulate engagement of targets (e.g., stimulus set of highly palatable foods images or tobacco-related images as well as self-regulation interventions). A comparable sampling of 100 persons will participate in a non-lab study (led by Multiple PI Lisa Marsch) in which we will leverage our novel mobile-based behavioral assessment/intervention platform to modulate target engagement and collect data in real-world conditions. Aim 3. Identify or develop measures and methods to permit verification of target engagement within the self-regulation domain. Led by Co-I Dave MacKinnon, we will examine cross-assay validity and cross-context and cross-sample reliability of assays. We will employ discriminant and divergent validation methods and Bayesian modeling to refine an empirically-based ontology of self-regulatory targets (to be used in Aim 4). Aim 4. We will evaluate the degree to which engaging targets produces a desired change in medical regimen adherence (across 4 week interventions) and health behavior among smokers (n=100) and overweight/obese persons with binge eating disorder (n=100) (objectively measured smoking in the former sample and binge eating in the latter sample). We will employ our novel mobile behavioral assessment/intervention platform to engage targets in these samples, given that (1) it offers self-regulation assessment and behavior change tools via an integrated platform to a wide array of populations, and (2) content within the platform can be quickly modified as needed to better impact targets. The proposed project is designed to identify valid and replicable assays of mechanisms of self-regulation across populations to inform an ontology of self-regulation that can ultimately inform development of health behavior interventions of maximal efficacy and potency. This protocol details the Aim 2 non-lab study led by Multiple PI Marsch. This phase of the study takes what we learned about self-regulation in the first phase and tests it in two samples that are exemplary for "lapses" in self-regulation: individuals who smoke and overweight/obese individuals with binge eating disorder. We expect that many real-world conditions (e.g., temptation, negative affect) may decrease self-regulation, whereas training through the mobile intervention described below may increase self-regulation. The primary purpose of this study is to determine whether we can shift self-regulation for the ultimate goal (in Aim 4) of targeting self-regulation to impact health behaviors.

Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not "diagnosis-specific" but rather enables integrated care for any combination of individuals' goals, needs, and preferences.

Self-reported momentary self-regulation assessed by the momentary self-regulation questionnaire four times daily (morning, early afternoon, late afternoon/evening, and night) over a 14-day period. Each item is scored 1 (not at all) to 5 (extremely). The scale is comprised of four subscales: momentary perseverance, momentary sensation seeking, momentary self-judgment, and momentary mindfulness. Each subscale score is calculated by averaging the responses from three of the scale items. Scores on each subscale range from 1 to 5, with higher subscale scores indicating greater momentary reporting of that facet of self-regulation (perseverance, sensation seeking, self-judgment, or mindfulness).

[Binge eating sample only] Self-reported binge eating episodes assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 14-day period. A binge eating episode is defined as self-reported overeating and loss of control. Overeating is assessed by the question "Since the last prompt, when you ate most recently, did you overeat?" and is scored as 0 (no) or 1 (yes). Loss of control is assessed by the question "When you ate most recently, did you lose control over your eating?" and is scored as 1 (not at all) to 5 (totally), where a 4 or 5 is considered loss of control.

[Smoking sample only] Self-reported smoking assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 14-day period. A smoking episode is defined as self-reported smoking of more than zero cigarettes and is assessed by the question "Since the last prompt, how many cigarettes have you smoked?" Participants are asked to input a number into a number field.

Inclusion criteria: Age 21-50 years Understand English sufficiently to provide informed consent Use a smartphone (participants without mobile sensing); proficient with using smartphone and comfort wearing devices (participants with mobile sensing) Additional inclusion criteria for binge eating sample: 27 ≤ BMI ≤ 45 kg/m2 Have binge eating disorder according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) criteria Non-smoking (defined as no cigarettes in past 12 months-this includes former and never smokers) Additional inclusion criteria for smoking sample: Smoke 5 or more tobacco cigarettes/day for past year 17 ≤ BMI < 27 kg/m2 Exclusion criteria: Any current substance use disorder o Will not exclude based on use of substances Currently pregnant or plans to become pregnant in next 3 months Lifetime history of major psychotic disorders (including schizophrenia and bipolar disorder) Current use of any medication for psychiatric reasons (including stimulants and mood stabilizers) Current use of prescription pain medications (e.g., Vicodin, oxycodone) Current use of any medication for smoking Exceptions: short-acting nicotine replacement therapy (e.g., gum, lozenge, nasal spray, inhaler) Will screen out for Wellbutrin or varenicline Current use of any medication for weight loss Have undergone weight-loss surgery (e.g., gastric bypass, lap band) Current nighttime shift work or obstructive sleep apnea Note: We will not exclude based on e-cigarette use. Additional exclusion criteria for binge eating sample: Compensatory behavior (e.g., purging, excessive exercise, fasting) [already excluded as part of the DSM-5 binge eating disorder criteria] Lost weight in recent past (>10 pounds in past 6 months) Currently in a weight-loss program (e.g., Weight Watchers, Jenny Craig) [will not exclude on online/mobile app weight-loss programs] Currently on a special diet for a serious health condition Additional exclusion criteria for smoking sample: Binge eating behavior according to Questionnaire on Eating and Weight Patterns-5 (QEWP-5) ("yes" to Qs 8 and 9 and for Q10, at least one episode per week for three months). - QEWP-5 #8: During the past three months, did you ever eat in a short period of time (for example, a two-hour period) what most people would think was an unusually large amount of food? [yes or no] - QEWP-5 #9: During the times when you ate an unusually large amount of food, did you ever feel you could not stop eating or control what or how much you were eating? [yes or no] - QEWP-5 #10: During the past three months, how often, on average, did you have episodes like this? That is, eating large amounts of food plus the feeling that your eating was out of control? (There may have been some weeks when this did not happen. Just average those in.) [less than one episode per week, five response options for 1 or more episodes per week]

After completion of the study, a de-identified dataset (i.e., stripped of all codes or other information that could be linked back to an individual participant) will be generated and made available to the research community as a whole. Before 3/22/2018: Informed consent procedures will ensure that participants are aware and opt into the free sharing of this open data set. Participants may consent to participate in the study without consenting to be included in the open data set, in which case their data will be excluded from the open data set. From 3/22/2018 forward: Informed consent procedures will ensure that participants are aware that consenting to participate in the study means consenting to inclusion in this open data set.

Eisenberg IW, Bissett PG, Canning JR, Dallery J, Enkavi AZ, Whitfield-Gabrieli S, Gonzalez O, Green AI, Greene MA, Kiernan M, Kim SJ, Li J, Lowe MR, Mazza GL, Metcalf SA, Onken L, Parikh SS, Peters E, Prochaska JJ, Scherer EA, Stoeckel LE, Valente MJ, Wu J, Xie H, MacKinnon DP, Marsch LA, Poldrack RA. Applying novel technologies and methods to inform the ontology of self-regulation. Behav Res Ther. 2018 Feb;101:46-57. doi: 10.1016/j.brat.2017.09.014. Epub 2017 Oct 5.

Scherer EA, Metcalf SA, Whicker CL, Bartels SM, Grabinski M, Kim SJ, Sweeney MA, Lemley SM, Lavoie H, Xie H, Bissett PG, Dallery J, Kiernan M, Lowe MR, Onken L, Prochaska JJ, Stoeckel LE, Poldrack RA, MacKinnon DP, Marsch LA. Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder. Front Digit Health. 2022 Mar 18;4:798895. doi: 10.3389/fdgth.2022.798895. eCollection 2022.