The Impact of Sparse Datasets When Harmonizing Data from Studies with Different Measures of the Same Construct.

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.

Retrospective Psychometrics and Effect Heterogeneity in Integrated Data Analysis: Commentary on the Special Issue.

The current special issue of Prevention Science indicates that momentum in using individual participant data (IPD) and integrative data analysis (IDA) to combine and synthesize findings in prevention science has accelerated over the past decade. In this commentary, we focus on two general themes involving methods for harmonizing measures and findings of effect heterogeneity. We describe methods for harmonization as retrospective psychometrics, requiring that we attend to the assumptions necessary for accurate measurement, but adjust our methods given the constraints of working with existing datasets that often involve different measures in different studies. We point to novel approaches for increasing confidence that semantic matching and empirical modeling used in these studies will yield accurate and valid measurements that can be combined in IDA. We also review findings about effect heterogeneity, emphasizing the importance of using etiologic and action theories to identify and evaluate sources of such effects. We note that all of the papers in this issue deserve careful attention, as they illustrate how prevention scientists are approaching the complexities of IDA and exploring novel methods for overcoming its challenges.

Heterogeneity in the Effects of Interventions to Prevent Depression in Couples Facing Job Loss: Studying Baseline Target Moderation of Impact.

Couples' communication styles are associated with depression following job loss for both job seekers and their partners. The Couples Employment Program (CEP), an eight-session program for couples facing job loss, was developed to integrate job search strategies sessions from the JOBS program with couple communication sessions, targeting job search behavior, motivation, mastery, and couple communication. We hypothesized that CEP would have compensatory effects, such that those who began the program with lower job search behavior, lower motivation, less mastery, and more negative or less positive couple communication would make more gains on these targets, and this would mediate impact on reducing risk for depression. We conducted a randomized field trial of CEP with 1477 heterosexual couples facing recent unemployment. Baseline levels of job search behavior and motivation, but not mastery or depression, moderated the impact of intervention on job seeker depression slopes over 12 months; job seekers reporting less job search behavior and motivation at baseline benefited more. Male partners with higher baseline depression also benefited. Opposite to our hypothesis, baseline levels of couple's communication moderated the impact of intervention such that partners in couples with more negative and less positive communication showed iatrogenic effects. There was no evidence that baseline target levels moderated the impact of the intervention on any of these targets. We speculate that more intensive communication training may be necessary for positive impact.

Re-envisioning, Retooling, and Rebuilding Prevention Science Methods to Address Structural and Systemic Racism and Promote Health Equity.

The historic momentum from national conversations on the roots and current impacts of racism in the USA presents an incredible window of opportunity for prevention scientists to revisit how common theories, measurement tools, methodologies, and interventions can be radically re-envisioned, retooled, and rebuilt to dismantle racism and promote equitable health for minoritized communities. Recognizing this opportunity, the NIH-funded Prevention Science and Methodology Group (PSMG) launched a series of presentations focused on the role of Prevention Science to address racism and discrimination guided by a commitment to social justice and health equity. The current manuscript aims to advance the field of Prevention Science by summarizing key issues raised during the series' presentations and proposing concrete research priorities and steps that hold promise for promoting health equity by addressing systemic racism. Being anti-racist is an active practice for all of us, whether we identify as methodologists, interventionists, practitioners, funders, community members, or an intersection of these identities. We implore prevention scientists and methodologists to take on these conversations with us to promote science and practice that offers every life the right to live in a just and equitable world.

The impact of social rhythm and sleep disruptions on waist circumference after job loss: A prospective 18-month study.

OBJECTIVE: This study prospectively examined change in waist circumference (WC) as a function of daily social rhythms and sleep in the aftermath of involuntary job loss. It was hypothesized that disrupted social rhythms and fragmented/short sleep after job loss would independently predict gains in WC over 18 months and that resiliency to WC gain would be conferred by the converse. METHODS: Eligible participants (n = 191) completed six visits that included standardized measurements of WC. At the baseline visit, participants completed the social rhythm metric and daily sleep diary and wore an actigraph on their nondominant wrist each day for a period of 2 weeks. RESULTS: When controlling for obesity and other covariates, WC trajectories decreased for individuals with more consistent social rhythms, more activities in their sdiocial rhythms, and higher sleep quality after job loss. WC trajectories did not change for individuals with lower scores on these indicators. CONCLUSIONS: The frequency and consistency of social rhythms after job loss play a key role in WC loss. These findings support the implementation of social rhythm interventions after job loss, a potentially sensitive time for the establishment of new daily routines that have an impact on metabolic health.

Secondary Analysis to Inform the Development of Adaptive Preventive Interventions.

For the past 30 years, scholars across the fields of epidemiology, education, psychology, and numerous other fields have worked to develop interventions designed to reduce risk and enhance protection to prevent mental, emotional, and behavioral problems across the lifespan. This article presents a series of next steps that leverage this foundational science to inform the development of adaptive preventive interventions. Adaptive preventive interventions (APIs) tailor the intervention to fit the diverse, sometimes changing, needs of participants with the goal of better prevention outcomes for more individuals. Secondary analyses of data from preventive intervention trials to identify moderators, mediators, and antecedents of attrition and intervention failure can be useful for designing effective APIs. Moderators that identify intervention effect heterogeneity can be used within an API to tailor the intervention to meet the unique needs of important participant subgroups. Mediators and predictors of disengagement and attrition can be helpful tailoring variables in an API to trigger change to the intervention. Preventive intervention trials that incorporate frequent assessment of potential mediators, moderators, and antecedents of attrition during the intervention period are needed. Secondary analyses of data from preventive intervention trials provide an important foundation for next-generation APIs.

Unemployed Individuals Reporting Hindrance Work Stress at Previous Job Have Increased Likelihood of Insomnia Disorder.

BACKGROUND: Workplace stress and unemployment are each associated with disturbances in sleep. However, a substantial gap exists in what we know about the type of workplace stress preceding job loss and the lasting effect workplace stressors may have on long-term health outcomes. We hypothesized that a specific type of workplace stress, hindrance stress, would be a stronger predictor of current insomnia disorder, compared to challenge stress. METHOD: Cross-sectional data were analyzed from 191 recently unemployed individuals participating in the ongoing Assessing Daily Patterns through occupational Transitions (ADAPT) study. Participants were administered the Cavanaugh et al. (J Appl Psychol. 85(1):65, 2000) self-reported work stress scale regarding their previous job and the Duke Sleep Interview (DSI-SD), a semi-structured interview assessing ICSD-3 insomnia disorder (chronic and acute). RESULTS: Results from logistic regression analyses indicated that hindrance work stress was associated with an increased likelihood of current overall, chronic, and acute insomnia disorder, when controlling for challenge stress and significant demographic factors. Challenge stress was associated with an increased likelihood of chronic insomnia disorder when controlling for hindrance stress and covariates. The association between challenge stress and acute insomnia differed as a function of sex. CONCLUSION: Hindrance work stressors were associated with increased odds of current insomnia disorder, even after employment ended. Across each of the tested models, hindrance stress had stronger effects on insomnia than challenge stress. These findings support and extend both the challenge-hindrance framework of work-related stress and the 3 P model of insomnia.

Using baseline target moderation to guide decisions on adapting prevention programs.

Tom Dishion, a pioneer in prevention science, was one of the first to recognize the importance of adapting interventions to the needs of individual families. Building towards this goal, we suggest that prevention trials be used to assess baseline target moderated mediation (BTMM), where preventive intervention effects are mediated through change in specific targets, and the resulting effect varies across baseline levels of the target. Four forms of BTMM found in recent trials are discussed including compensatory, rich-get-richer, crossover, and differential iatrogenic effects. A strategy for evaluating meaningful preventive effects is presented based on preventive thresholds for diagnostic conditions, midpoint targets and proximal risk or protective mechanisms. Methods are described for using the results from BTMM analyses of these thresholds to estimate indices of intervention risk reduction or increase as they vary over baseline target levels, and potential cut points are presented for identifying subgroups that would benefit from program adaptation because of weak or potentially iatrogenic program effects. Simulated data are used to illustrate curves for the four forms of BTMM effects and how implications for adaptation change when untreated control group outcomes also vary over baseline target levels.

Evaluating construct equivalence of youth depression measures across multiple measures and multiple studies.

Construct equivalence of measures across studies is necessary for synthesizing results when combining data in meta-analysis or integrative data analysis. We discuss several assumptions required for construct equivalence, and review methods using individual-level data and item response theory (IRT) analysis for detecting or adjusting for violations of these assumptions. We apply IRT to data from 7 measures of depressive symptoms for 4,283 youth from 16 randomized prevention trials. Findings indicate that these data violate assumptions of conditional independence. Bifactor IRT models find that depression measures contain substantial reporter variance, and indicate that a single common factor model would be substantially biased. Separate analyses of ratings by youth find stronger evidence for construct equivalence, but factor invariance across sex and age does not hold. We conclude that data synthesis studies employing measures of youth depression should analyze results separately by reporter, explore more complex approaches to integrate these different perspectives, and explore methods that adjust for sex and age differences in item functioning. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Preventive Effect Heterogeneity: Causal Inference in Personalized Prevention.

This paper employs a causal inference framework to explore two logically distinct forms of preventive effect heterogeneity relevant for studying variation in preventive effect as a basis for developing more personalized interventions. Following VanderWeele (2015), I begin with a discussion of causal interaction involving manipulable moderators that combine to yield more complex nonadditive effects. This is contrasted with effect heterogeneity, which involves variation in causal structure indexed by stable characteristics of populations or contexts. The paper then discusses one particularly promising approach, the baseline target moderated mediation (BTMM) design, which uses theoretically informed baseline target moderators to strengthen causal inference, suggesting methods for using BTMM designs to develop targeting strategies for personalized prevention. It presents examples of recent intervention trials that apply these different forms of moderation, and discusses causal inference and the problem of moderation confounding, reviewing methods for minimizing its impact, including recent advances in the use of propensity score matching.

Programs for Preventing Depression in Adolescence: Who Benefits and Who Does Not? An Introduction to the Supplemental Issue.

We introduce this supplemental issue of Prevention Science, which brings together a set of papers from leading investigators who have conducted trials testing whether intervention programs prevent adolescent depression. Using data from these trials, these papers explore a series of factors that might account for variation in intervention benefit, employing several novel methods for assessing effect heterogeneity. These studies follow two general paradigms: three papers report findings from single randomized preventive intervention trials, while the remaining papers develop and apply new methods for combining data from multiple studies to evaluate effect heterogeneity more broadly. Colleagues from NIMH and SAMHSA also provide commentaries on these studies. They conclude that synthesis of findings from multiple trials holds great promise for advancing the field, and progress will be accelerated if collaborative data sharing becomes the norm rather than the exception.

Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials.

Integrative Data Analysis (IDA) encompasses a collection of methods for data synthesis that pools participant-level data across multiple studies. Compared with single-study analyses, IDA provides larger sample sizes, better representation of participant characteristics, and often increased statistical power. Many of the methods currently available for IDA have focused on examining developmental changes using longitudinal observational studies employing different measures across time and study. However, IDA can also be useful in synthesizing across multiple randomized clinical trials to improve our understanding of the comprehensive effectiveness of interventions, as well as mediators and moderators of those effects. The pooling of data from randomized clinical trials presents a number of methodological challenges, and we discuss ways to examine potential threats to internal and external validity. Using as an illustration a synthesis of 19 randomized clinical trials on the prevention of adolescent depression, we articulate IDA methods that can be used to minimize threats to internal validity, including (1) heterogeneity in the outcome measures across trials, (2) heterogeneity in the follow-up assessments across trials, (3) heterogeneity in the sample characteristics across trials, (4) heterogeneity in the comparison conditions across trials, and (5) heterogeneity in the impact trajectories. We also demonstrate a technique for minimizing threats to external validity in synthesis analysis that may result from non-availability of some trial datasets. The proposed methods rely heavily on latent variable modeling extensions of the latent growth curve model, as well as missing data procedures. The goal is to provide strategies for researchers considering IDA.

Longitudinal assessment of daily activity patterns on weight change after involuntary job loss: the ADAPT study protocol.

BACKGROUND: The World Health Organization has identified obesity as one of the most visible and neglected public health problems worldwide. Meta-analytic studies suggest that insufficient sleep increases the risk of developing obesity and related serious medical conditions. Unfortunately, the nationwide average sleep duration has steadily declined over the last two decades with 25% of U.S. adults reporting insufficient sleep. Stress is also an important indirect factor in obesity, and chronic stress and laboratory-induced stress negatively impact sleep. Despite what we know from basic sciences about (a) stress and sleep and (b) sleep and obesity, we know very little about how these factors actually manifest in a natural environment. The Assessing Daily Activity Patterns Through Occupational Transitions (ADAPT) study tests whether sleep disruption plays a key role in the development of obesity for individuals exposed to involuntary job loss, a life event that is often stressful and disrupting to an individual's daily routine. METHODS: This is an 18-month closed, cohort research design examining social rhythms, sleep, dietary intake, energy expenditure, waist circumference, and weight gain over 18 months in individuals who have sustained involuntary job loss. Approximately 332 participants who lost their job within the last 3 months are recruited from flyers within the Arizona Department of Economic Security (AZDES) Unemployment Insurance Administration application packets and other related postings. Multivariate growth curve modeling will be used to investigate the temporal precedence of changes in social rhythms, sleep, and weight gain. DISCUSSION: It is hypothesized that: (1) unemployed individuals with less consistent social rhythms and worse sleep will have steeper weight gain trajectories over 18 months than unemployed individuals with stable social rhythms and better sleep; (2) disrupted sleep will mediate the relationship between social rhythm disruption and weight gain; and (3) reemployment will be associated with a reversal in the negative trajectories outlined above. Positive findings will provide support for the development of obesity prevention campaigns targeting sleep and social rhythms in an accessible subgroup of vulnerable individuals.

Stomaching rejection: Self-compassion and self-esteem moderate the impact of daily social rejection on restrictive eating behaviours among college women.

OBJECTIVE: The present study examined whether having high self-esteem or a self-compassionate perspective help mitigate the impact of daily social rejection on negative affect and restrictive eating behaviours. DESIGN: Following a baseline survey assessing self-esteem and self-compassion, 121 college women completed online daily diaries for one week. MAIN OUTCOME MEASURES: Negative affect and restrictive eating behaviours. RESULTS: On days when women reported more rejection, they also reported higher restrictive eating behaviours and greater negative affect. Effects were moderated by self-esteem and self-compassion, such that the lower participants were in self-esteem or self-compassion, the stronger the positive relation between rejection and negative affect and restrictive eating. However, only the common humanity/isolation dimension of self-compassion significantly moderated daily effects of rejection when controlling for self-esteem. Mediated moderation results reveal different mechanisms by which self-esteem and self-compassion buffer against rejections' effects on affect and restrictive eating. CONCLUSION: Self-compassion and self-esteem influence the complex impact that social rejection has on affect and restrictive eating. More than other dimensions of self-compassion or self-esteem, remembering one's common humanity can result in a healthier response to social rejection.

Combining Stress Exposure and Stress Generation: Does Neuroticism Alter the Dynamic Interplay of Stress, Depression, and Anxiety Following Job Loss?

OBJECTIVE: Emerging models of stress point to a dynamic formulation where stressors and internalizing symptoms reciprocally influence each other. This study tested whether this dynamic interplay is the result of a general internalizing process underlying both depression and anxiety, and whether it varies with neuroticism. METHOD: A total of 426 adults (51% female; 47% White, 42% African American) were assessed five times over 6 months following loss of employment, using repeated measurements of stressors, depression, anxiety, and neuroticism. RESULTS: Latent growth across 6 months and multilevel cross-lagged regressions across 6 weeks supported the hypothesis that stressors and internalizing symptoms have reciprocal effects after job loss. Findings for unique variation in depression paralleled those for general internalizing, whereas few findings emerged for general or social anxiety after controlling for internalizing. Neuroticism strengthened the association of change in stressors with change in symptoms across 6 months. Those with high neuroticism showed less reduction in internalizing following reemployment and were less likely to be reemployed when starting with higher internalizing. CONCLUSIONS: The moderated reciprocal effects model helps account for onset, maintenance, and resolution of symptoms following job loss. We speculate that these findings may be due in part to differential emotion regulation and reductions in motivation.

Human Subjects Protection and Technology in Prevention Science: Selected Opportunities and Challenges.

Internet-connected devices are changing the way people live, work, and relate to one another. For prevention scientists, technological advances create opportunities to promote the welfare of human subjects and society. The challenge is to obtain the benefits while minimizing risks. In this article, we use the guiding principles for ethical human subjects research and proposed changes to the Common Rule regulations, as a basis for discussing selected opportunities and challenges that new technologies present for prevention science. The benefits of conducting research with new populations, and at new levels of integration into participants' daily lives, are presented along with five challenges along with technological and other solutions to strengthen the protections that we provide: (1) achieving adequate informed consent with procedures that are acceptable to participants in a digital age; (2) balancing opportunities for rapid development and broad reach, with gaining adequate understanding of population needs; (3) integrating data collection and intervention into participants' lives while minimizing intrusiveness and fatigue; (4) setting appropriate expectations for responding to safety and suicide concerns; and (5) safeguarding newly available streams of sensitive data. Our goal is to promote collaboration between prevention scientists, institutional review boards, and community members to safely and ethically harness advancing technologies to strengthen impact of prevention science.

Standards of Evidence for Efficacy, Effectiveness, and Scale-up Research in Prevention Science: Next Generation.

A decade ago, the Society of Prevention Research (SPR) endorsed a set of standards for evidence related to research on prevention interventions. These standards (Flay et al., Prevention Science 6:151-175, 2005) were intended in part to increase consistency in reviews of prevention research that often generated disparate lists of effective interventions due to the application of different standards for what was considered to be necessary to demonstrate effectiveness. In 2013, SPR's Board of Directors decided that the field has progressed sufficiently to warrant a review and, if necessary, publication of "the next generation" of standards of evidence. The Board convened a committee to review and update the standards. This article reports on the results of this committee's deliberations, summarizing changes made to the earlier standards and explaining the rationale for each change. The SPR Board of Directors endorses "The Standards of Evidence for Efficacy, Effectiveness, and Scale-up Research in Prevention Science: Next Generation."

A longitudinal examination of re-employment quality on internalizing symptoms and job-search intentions.

Underemployed workers-those receiving less pay, working fewer hours, or using fewer skills than they would prefer-appear to experience negative mental health outcomes similar to the unemployed. Prior cross-sectional research provides mixed empirical evidence for this conclusion, however. The current study sought to clarify the impact of underemployment longitudinally, assessing mental health 5 times over 8 months after job loss. In addition to the commonly used indicators of underemployment, we designed a measure of cognitive complexity using the Occupational Information Network (O*NET), an extensive government database used to organize and categorize occupational information. Replicating past research, we found concurrent associations between all indexes of reemployment job quality and internalizing symptoms in the period immediately after reemployment. However, when controlling for quality of prior employment, all indicators except our measure for cognitive complexity became nonsignificant. As participants transitioned from unemployment to reemployment, only reductions in cognitive complexity were associated with sustained general internalizing symptoms. We also found that although changes in cognitive complexity had an immediate impact on the well-being of the recently reemployed, only the number of available weekly hours (full-time vs. part-time status) was relevant 6 to 12 weeks later. Our longitudinal model thus provides significant nuance to the current understanding of underemployment and mental health.

Translating Genetic Research into Preventive Intervention: The Baseline Target Moderated Mediator Design.

In this paper we present and discuss a novel research approach, the baseline target moderated mediation (BTMM) design, that holds substantial promise for advancing our understanding of how genetic research can inform prevention research. We first discuss how genetically informed research on developmental psychopathology can be used to identify potential intervention targets. We then describe the BTMM design, which employs moderated mediation within a longitudinal study to test whether baseline levels of intervention targets moderate the impact of the intervention on change in that target, and whether change in those targets mediates causal impact of preventive or treatment interventions on distal health outcomes. We next discuss how genetically informed BTMM designs can be applied to both microtrials and full-scale prevention trials. We use simulated data to illustrate a BTMM, and end with a discussion of some of the advantages and limitations of this approach.