The Reach, Effectiveness, Adoption, Implementation, and Maintenance of Digital Mental Health Interventions for College Students: A Systematic Review.

PURPOSE OF REVIEW: We evaluated the impact of digital mental health interventions (DMHIs) for college students. We organized findings using the RE-AIM framework to include reach, effectiveness, adoption, implementation, and maintenance. RECENT FINDINGS: We conducted a systematic literature review of recent findings from 2019-2024. Our search identified 2,701 articles, of which 95 met inclusion criteria. In the reach domain, student samples were overwhelmingly female and White. In the effectiveness domain, over 80% of DMHIs were effective or partially effective at reducing their primary outcome. In the adoption domain, studies reported modest uptake for DMHIs. In the implementation and maintenance domains, studies reported high adherence rates to DMHI content. While recruitment methods were commonly reported, adaptations and costs of implementation and maintenance were rarely reported. DMHIs for college students are effective for many psychological outcomes. Future work should address diversifying samples and considering implementation in a variety of college settings.

Nurturing innovation, catalyzing change: Honoring the legacy of Dr. C. Barr Taylor in the eating disorders field.

This Virtual Issue of the International Journal of Eating Disorders honors the legacy of the late Dr. C. Barr Taylor in the eating disorders (EDs) field. For decades, Dr. Taylor led the way in not only conducting the research needed to achieve the ultimate goal of making affordable, accessible, and evidence-based care for EDs available to all, but also nurturing the next generation of scientific leaders and innovators. Articles included in this Virtual Issue are a selection of Dr. Taylor's published works in the Journal in the past decade, spanning original research, ideas worth researching, commentaries, and a systematic review. We hope this Virtual Issue will inspire the next generation of research in EDs, and equally, if not more importantly, the next generation of young investigators in the field. We urge the field to continue and build upon Dr. Taylor's vision-to increase access to targeted prevention and intervention for EDs in innovative and forward-thinking ways-while embracing his unique and powerful mentorship style to lift up early career investigators and create a community of leaders to address and solve our field's biggest challenges.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students.

BACKGROUND: A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated. METHODS: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD). RESULTS: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes. CONCLUSION: These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression. TRIAL REGISTRATION: Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Discrepancies in prevalence estimates of suicidal ideation and attempts in 18- to 22-year-old US college students: a comparison of three surveys.

PURPOSE: Suicidal thoughts and behaviors (STB) have been increasing among US college students. Accurate measurement of STB is key to understanding trends and guiding suicide prevention efforts. We aimed to compare the prevalence estimates of STB among college students from two campus-based surveys (the National College Health Assessment [NCHA] and the Healthy Minds Study [HMS]) and one general population study (the National Survey on Drug Use and Health [NSDUH]). METHODS: Estimates were generated from the three surveys for past year suicidal ideation (PYSI) and past year suicide attempts (PYSA) among 18- to 22-year-old full-time college students. Data were combined from each survey to develop bivariate and multivariate regression models for odds of PYSI and PYSA. RESULTS: Estimates for PYSI varied between the three surveys: 34.3% for NCHA, 15.0% for HMS, and 10.7% for NSDUH. Estimates for PYSA were 2.6% for NCHA, 1.6% for HMS, and 1.7% for NSDUH. After adjusting for demographic and educational characteristics, odds of PYSI remained significantly lower for HMS participants (aOR 0.31, 95% CI 0.29-0.33) and NSDUH participants (aOR 0.19, 95% CI 0.19-0.30) compared to NCHA participants. The odds of PYSA for HMS participants were lower than those for NCHA participants (aOR 0.63, 95% CI 0.54-0.73). CONCLUSION: Estimates of PYSI and PYSA vary between leading sources of data on college student mental health. The differences are likely related to question wording, survey implementation, as well as institutional and individual representation. Accounting for these differences when interpreting estimates of STB can help guide suicide prevention efforts.

College roommates have a modest but significant influence on each other's political ideology.

Does college change students' political preferences? While existing research has documented associations between college education and political views, it remains unclear whether these associations reflect a causal relationship. We address this gap in previous research by analyzing a quasi-experiment in which university students are assigned to live together as roommates. While we find little evidence that college students as a whole become more liberal over time, we do find strong evidence of peer effects, in which students' political views become more in line with the views of their roommates over time. This effect is strongest for conservative students. These findings shed light on the role of higher education in an era of political polarization.

Mixed Signals in Child and Adolescent Mental Health and Well-Being Indicators in the United States: A Call for Improvements to Population Health Monitoring.

Policy Points Social indicators of young peoples' conditions and circumstances, such as high school graduation, food insecurity, and smoking, are improving even as subjective indicators of mental health and well-being have been worsening. This divergence suggests policies targeting the social indicators may not have improved overall mental health and well-being. There are several plausible reasons for this seeming contradiction. Available data suggest the culpability of one or several common exposures poorly captured by existing social indicators. Resolving this disconnect requires significant investments in population-level data systems to support a more holistic, child-centric, and up-to-date understanding of young people's lives.

Clinical correlation but no elevation of striatal dopamine synthesis capacity in two independent cohorts of medication-free individuals with schizophrenia.

Dysregulation of dopamine systems has been considered a foundational driver of pathophysiological processes in schizophrenia, an illness characterized by diverse domains of symptomatology. Prior work observing elevated presynaptic dopamine synthesis capacity in some patient groups has not always identified consistent symptom correlates, and studies of affected individuals in medication-free states have been challenging to obtain. Here we report on two separate cohorts of individuals with schizophrenia spectrum illness who underwent blinded medication withdrawal and medication-free neuroimaging with [18F]-FDOPA PET to assess striatal dopamine synthesis capacity. Consistently in both cohorts, we found no significant differences between patient and matched, healthy comparison groups; however, we did identify and replicate robust inverse relationships between negative symptom severity and tracer-specific uptake widely throughout the striatum: [18F]-FDOPA specific uptake was lower in patients with a greater preponderance of negative symptoms. Complementary voxel-wise and region of interest analyses, both with and without partial volume correction, yielded consistent results. These data suggest that for some individuals, striatal hyperdopaminergia may not be a defining or enduring feature of primary psychotic illness. However, clinical differences across individuals may be significantly linked to variability in striatal dopaminergic tone. These findings call for further experimentation aimed at parsing the heterogeneity of dopaminergic systems function in schizophrenia.

Preferred treatment focus among college students with eating disorders and comorbid mental health problems in a digital cognitive-behavioral guided self-help program.

OBJECTIVE: To examine the mental health problems that college students with eating disorders (EDs) and comorbid depression and/or anxiety disorders preferred to target first in a digital treatment program and explore correlates of preferred treatment focus. METHODS: Four hundred and eighty nine college student users of a digital cognitive-behavioral guided self-help program targeting common mental health problems (76.7% female, Mage = 20.4 ± 4.4, 64.8% White) screened positive for an ED and ≥one other clinical mental health problem (i.e., depression, generalized anxiety disorder, social phobia, and/or panic disorder). Students also reported on insomnia, post-traumatic stress, alcohol use, and suicide risk. Before treatment, they indicated the mental health problem that they preferred to target first in treatment. Preferred treatment focus was characterized by diagnostic profile (i.e., ED + Depression, ED + Anxiety, ED + Depression + Anxiety), symptom severity, and demographics. RESULTS: 58% of students with ED + Anxiety, 47% of those with ED + Depression, and 27% of those with ED + Depression + Anxiety chose to target EDs first. Across diagnostic profiles, those who chose to target EDs first had more severe ED symptoms than those who chose to target anxiety or depression (ps < .05). Among students with ED + Depression + Anxiety, those who chose to target EDs first had lower depression symptoms than those who chose to target depression, lower generalized anxiety than those who chose to target anxiety, and lower suicidality than those who chose to target anxiety or depression (ps < .01). CONCLUSIONS: Students with EDs and comorbid depression and/or anxiety disorders showed variable preferred treatment focus across diagnostic profiles. Research should explore specific symptom presentations associated with preferred treatment focus. PUBLIC SIGNIFICANCE: Findings indicate that a sizable percentage of college students with depression/anxiety who also have EDs prefer to target EDs first in treatment, highlighting the importance of increasing availability of ED interventions to college students. Students with EDs and comorbid depression and/or anxiety disorders showed variable preferred treatment focus across diagnostic profiles, and preference to target EDs was associated with greater ED psychopathology across diagnostic profiles.

What is wrong with the Mission Dependency Index for US federal infrastructure decisions?

The Mission Dependency Index (MDI) is a risk metric used by US military services and federal agencies for guiding operations, management, and funding decisions for facilities. Despite its broad adoption for guiding the expenditure of billions in federal funds, several studies on MDI suggest it may have flaws that limit its efficacy. We present a detailed technical analysis of MDI to show how its flaws impact infrastructure decisions. We present the MDI used by the US Navy and develop a critique of current methods. We identify six problems with MDI that stem from its interpretation, use, and mathematical formulation, and we provide examples demonstrating how these flaws can bias decisions. We provide recommendations to overcome flaws for infrastructure risk decision making but ultimately recommend the US government develop a new metric less susceptible to bias.

Dashboards as Mechanisms for Community Empowerment: Developing a Prototype for Child and Adolescent Well-being in California.

CONTEXT: In recent years, stakeholders in public health have emphasized measuring young peoples' well-being as a more holistic and upstream approach to understanding their health and development. However, summarizing the available indicators of well-being in ways that strengthen ongoing policy and community efforts remains a challenge. PROGRAM: Our objective was to develop a measurement framework of young peoples' well-being that would be engaging and actionable to a broad and diverse set of stakeholders in California. IMPLEMENTATION: We began with a scan of the relevant literature documenting previous efforts to measure young peoples' well-being, both within the United States and internationally. Subsequently, we individually interviewed a set of key informants and then convened a multidisciplinary panel of experts to solicit feedback on our approach. Throughout this iterative and collaborative process, we developed and refined a measurement framework based on the information provided across these various sources. EVALUATION: Findings suggest data dashboards are a promising approach for presenting a parsimonious yet holistic picture of young peoples' well-being. Dashboards can highlight well-being's multidimensionality by categorizing indicators over different domains. Our framework organizes indicators over 5 types: child-centric, subjective well-being, contextual determinants, developmental, and equity-focused. The design and flexibility of dashboards can also highlight important gaps in data collection that are of interest to end users such as indicators not yet collected among the broader population. Furthermore, dashboards can include interactive features, such as selecting key data elements, that can help communities articulate priority areas for policy action, thereby generating momentum and enthusiasm for future iterations and improvements. DISCUSSION: Data dashboards are well suited for engaging a variety of stakeholders on complex multidimensional concepts such as young peoples' well-being. However, to fulfill their promise, they should be codesigned and codeveloped through an iterative process with the stakeholders and community members they intend to serve.

Comparison of Transcranial Sonography and [18 F]-Fluorodopa PET Imaging in GBA1 Mutation Carriers.

BACKGROUND: Mutations in GBA1 are a common genetic risk factor for parkinsonism; however, penetrance is incomplete, and biomarkers of future progression to parkinsonism are needed. Both nigral sonography and striatal [18 F]-FDOPA PET assay dopamine system health, but their utility and coherence in this context are unclear. OBJECTIVE: The aim of this study is to evaluate the utility and coherence of these modalities in GBA1-associated parkinsonism. METHODS: A total of 34 patients with GBA1 mutations (7 with parkinsonism) underwent both transcranial studies for substantia nigra echogenicity and [18 F]-FDOPA PET to determine striatal tracer-specific uptake (Ki ). RESULTS: Larger nigral echogenic areas and reduced striatal Ki were exclusively observed in parkinsonian patients. Sonographic and PET measurements showed strong inverse correlations but only in individuals with clinical parkinsonism. CONCLUSIONS: Close correspondence between nigral echogenicity and striatal presynaptic dopamine synthesis capacity observed only in GBA1 carriers with parkinsonism provides validation that these two modalities may conjointly capture aspects of the biology underlying clinical parkinsonism but raises questions about their utility as predictive tools in at-risk subjects. © 2022 International Parkinson and Movement Disorder Society.

Suicidality and Alcohol Use as Predictors of Future Suicidal Behavior in College Students.

AIMS: There has been a marked increase in suicide fatalities among college-age students in recent years. Moreover, heavy alcohol use, a well-known risk factor for suicide, is present on most campuses. Yet, no prospective studies have examined alcohol use patterns among college students as predictors of suicidal behaviors. METHODS: Online of 40,335 students at four universities took place at the beginning of four academic years, 2015-2018. Of these, 2296 met criteria for an increased risk of suicidal behavior and completed 1- and/or 6-month follow-up evaluation(s). Baseline assessments included the Alcohol Use Disorders Identification Test to quantify alcohol consumption and resulting problems, and measures of depression, suicidal ideation and suicidal behavior. RESULTS: Suicide attempts during follow-up were reported by 35 (1.5%) of high-risk students. Regression analyses indicated that baseline severity of alcohol use consequences, but not amount of alcohol consumption, was associated with greater odds of a follow-up suicide attempt after controlling for baseline suicidal ideation, functional impairment and history of suicide attempts. CONCLUSIONS: Among college students at elevated risk for suicide, the severity of alcohol-related consequences was a significant predictor of future suicide attempts. Alcohol consumption was not a significant predictor, suggesting that the amount students drink is less of a concern for suicidal behavior than are the problems (e.g. failing to meet expectations, experiencing blackouts) associated with drinking.

Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies.

The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative predictor for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of a predictor across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.

Longitudinal Positron Emission Tomography of Dopamine Synthesis in Subjects with GBA1 Mutations.

Mutations in GBA1, the gene mutated in Gaucher disease, are a common genetic risk factor for Parkinson disease, although the penetrance is low. We performed [18 F]-fluorodopa positron emission tomography studies of 57 homozygous and heterozygous GBA1 mutation carriers (15 with parkinsonism) and 98 controls looking for early indications of dopamine loss using voxelwise analyses to identify group differences in striatal [18 F]-fluorodopa uptake (Ki ). Forty-eight subjects were followed longitudinally. Cross-sectional and longitudinal comparisons of Ki and Ki change found significant effects of Parkinson disease. However, at baseline and over time, striatal [18 F]-fluorodopa uptake in mutation carriers without parkinsonism did not significantly differ from controls. ANN NEUROL 2020;87:652-657.

Relationships between patterns of weight-related self-monitoring and eating disorder symptomology among undergraduate and graduate students.

OBJECTIVE: To characterize patterns of weight-related self-monitoring (WRSM) among US undergraduate and graduate students and examine associations between identified patterns of WRSM and eating disorder symptomology. METHOD: Undergraduate and graduate students from 12 US colleges and universities (N = 10,010) reported the frequency with which they use WRSM, including self-weighing and dietary self-monitoring. Eating disorder symptomology was assessed using the Eating Disorder Examination Questionnaire. Gender-specific patterns of WRSM were identified using latent class analysis, and logistic regressions were used to identify differences in the odds of eating disorder symptomology across patterns of WRSM. RESULTS: Among this sample, 32.7% weighed themselves regularly; 44.1% reported knowing the nutrition facts of the foods they ate; 33.6% reported knowing the caloric content of the foods they ate; and 12.8% counted the calories they ate. Among women, four patterns of WRSM were identified: "no WRSM," "all forms of WRSM," "knowing nutrition/calorie facts," and "self-weigh only." Compared with the "no WRSM" pattern, women in all other patterns experienced increased eating disorder symptomology. Among men, three patterns were identified: "no WRSM," "all forms of WRSM," and "knowing nutrition/calorie facts." Only men in the "all forms WRSM" pattern had increased eating disorder symptomatology compared with those in the "no WRSM" pattern. DISCUSSION: In a large sample of undergraduate and graduate students, engaging in any WRSM was associated with increased eating disorder symptomology among women, particularly for those who engaged in all forms. Among men, engaging in all forms of WRSM was the only pattern associated with higher eating disorder symptomology.

Neanderthal-derived genetic variation in living humans relates to schizophrenia diagnosis, to psychotic symptom severity, and to dopamine synthesis.

Schizophrenia has been hypothesized to be a human-specific condition, but experimental approaches to testing this idea have been limited. Because Neanderthals, our closest evolutionary relatives, interbred with modern humans prior to their disappearance from the fossil record, leaving a residual echo that survives in our DNA today, we leveraged new discoveries about ancient hominid DNA to explore this hypothesis in living people in three converging ways. First, in four independent case-control datasets totaling 9,362 individuals, individuals with schizophrenia had less Neanderthal-derived genetic variation than controls (p = .044). Second, in 49 unmedicated inpatients with schizophrenia, having more Neanderthal admixture predicted less severe positive symptoms (p = .046). Finally, using 18 F-fluorodopa PET scanning in 172 healthy individuals, having greater Neanderthal introgression was significantly associated with lower dopamine synthesis capacity in the striatum and pons (p's < 2 × 10-5 ), which is fundamentally important in the pathophysiology and treatment of psychosis. These results may help to elucidate the evolutionary history of a devastating neuropsychiatric disease by supporting the notion of schizophrenia as a human-specific condition. Additionally, the relationship between Neanderthal admixture and dopamine function suggests a potential mechanism whereby Neanderthal admixture may have affected our gene pool to alter schizophrenia risk and/or course.

Williams syndrome hemideletion and LIMK1 variation both affect dorsal stream functional connectivity.

Williams syndrome is a rare genetic disorder caused by hemizygous deletion of ∼1.6 Mb affecting 26 genes on chromosome 7 (7q11.23) and is clinically typified by two cognitive/behavioural hallmarks: marked visuospatial deficits relative to verbal and non-verbal reasoning abilities and hypersocial personality. Clear knowledge of the circumscribed set of genes that are affected in Williams syndrome, along with the well-characterized neurobehavioural phenotype, offers the potential to elucidate neurogenetic principles that may apply in genetically and clinically more complex settings. The intraparietal sulcus, in the dorsal visual processing stream, has been shown to be structurally and functionally altered in Williams syndrome, providing a target for investigating resting-state functional connectivity and effects of specific genes hemideleted in Williams syndrome. Here, we tested for effects of the LIMK1 gene, deleted in Williams syndrome and important for neuronal maturation and migration, on intraparietal sulcus functional connectivity. We first defined a target brain phenotype by comparing intraparietal sulcus resting functional connectivity in individuals with Williams syndrome, in whom LIMK1 is hemideleted, with typically developing children. Then in two separate cohorts from the general population, we asked whether intraparietal sulcus functional connectivity patterns similar to those found in Williams syndrome were associated with sequence variation of the LIMK1 gene. Four independent between-group comparisons of resting-state functional MRI data (total n = 510) were performed: (i) 20 children with Williams syndrome compared to 20 age- and sex-matched typically developing children; (ii) a discovery cohort of 99 healthy adults stratified by LIMK1 haplotype; (iii) a replication cohort of 32 healthy adults also stratified by LIMK1 haplotype; and (iv) 339 healthy adolescent children stratified by LIMK1 haplotype. For between-group analyses, differences in intraparietal sulcus resting-state functional connectivity were calculated comparing children with Williams syndrome to matched typically developing children and comparing LIMK1 haplotype groups in each of the three general population cohorts separately. Consistent with the visuospatial construction impairment and hypersocial personality that typify Williams syndrome, the Williams syndrome cohort exhibited opposite patterns of intraparietal sulcus functional connectivity with visual processing regions and social processing regions: decreased circuit function in the former and increased circuit function in the latter. All three general population groups also showed LIMK1 haplotype-related differences in intraparietal sulcus functional connectivity localized to the fusiform gyrus, a visual processing region also identified in the Williams syndrome-typically developing comparison. These results suggest a neurogenetic mechanism, in part involving LIMK1, that may bias neural circuit function in both the general population and individuals with Williams syndrome.

Using Digital Technology to Reduce the Prevalence of Mental Health Disorders in Populations: Time for a New Approach.

Digital technology, which includes the collection, analysis, and use of data from a variety of digital devices, has the potential to reduce the prevalence of disorders and improve mental health in populations. Among the many advantages of digital technology is that it allows preventive and clinical interventions, both of which are needed to reduce the prevalence of mental health disorders, to be feasibly integrated into health care and community delivery systems and delivered at scale. However, the use of digital technology also presents several challenges, including how systems can manage and implement interventions in a rapidly changing digital environment and handle critical issues that affect population-wide outcomes, including reaching the targeted population, obtaining meaningful levels of uptake and use of interventions, and achieving significant outcomes. We describe a possible solution, which is to have an outcome optimization team that focuses on the dynamic use of data to adapt interventions for populations, while at the same time, addressing the complex relationships among reach, uptake, use, and outcome. We use the example of eating disorders in young people to illustrate how this solution could be implemented at scale. We also discuss system, practitioner-related, and other issues related to the adaptation of such an approach. Digital technology has great potential for facilitating the reduction of mental illness rates in populations. However, achieving this goal will require the implementation of new approaches. As a solution, we argue for the need to create outcome optimization teams, tasked with integrating data from various sources and using advanced data analytics and new designs to develop interventions/strategies to increase reach, uptake, use/engagement, and outcomes for both preventive and treatment interventions.

Change in Patient Outcomes After Augmenting a Low-level Implementation Strategy in Community Practices That Are Slow to Adopt a Collaborative Chronic Care Model: A Cluster Randomized Implementation Trial.

BACKGROUND: Implementation strategies are essential for promoting the uptake of evidence-based practices and for patients to receive optimal care. Yet strategies differ substantially in their intensity and feasibility. Lower-intensity strategies (eg, training and technical support) are commonly used but may be insufficient for all clinics. Limited research has examined the comparative effectiveness of augmentations to low-level implementation strategies for nonresponding clinics. OBJECTIVES: To compare 2 augmentation strategies for improving uptake of an evidence-based collaborative chronic care model (CCM) on 18-month outcomes for patients with depression at community-based clinics nonresponsive to lower-level implementation support. RESEARCH DESIGN: Providers initially received support using a low-level implementation strategy, Replicating Effective Programs (REP). After 6 months, nonresponsive clinics were randomized to add either external facilitation (REP+EF) or external and internal facilitation (REP+EF/IF). MEASURES: The primary outcome was patient 12-item short form survey (SF-12) mental health score at month 18. Secondary outcomes were patient health questionnaire (PHQ-9) depression score at month 18 and receipt of the CCM during months 6 through 18. RESULTS: Twenty-seven clinics were nonresponsive after 6 months of REP. Thirteen clinics (N=77 patients) were randomized to REP+EF and 14 (N=92) to REP+EF/IF. At 18 months, patients in the REP+EF/IF arm had worse SF-12 [diff, 8.38; 95% confidence interval (CI), 3.59-13.18] and PHQ-9 scores (diff, 1.82; 95% CI, -0.14 to 3.79), and lower odds of CCM receipt (odds ratio, 0.67; 95% CI, 0.30-1.49) than REP+EF patients. CONCLUSIONS: Patients at sites receiving the more intensive REP+EF/IF saw less improvement in mood symptoms at 18 months than those receiving REP+EF and were no more likely to receive the CCM. For community-based clinics, EF augmentation may be more feasible than EF/IF for implementing CCMs.

Rethinking Resilience Analytics.

The concept of "resilience analytics" has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data-driven analytic techniques, as well as the prevalence of high-profile natural and man-made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber-physical-social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.