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Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary (2015)

Chapter: 2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement

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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
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2

New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement

The workshop featured presentations on new technologies and analytic methods that can aid effective dissemination and implementation of evidence-based preventive interventions as well as their quality improvement. Some of these innovations enable the best use of multiple evidence-based interventions (EBIs) and tailoring of such interventions so they better suit the needs of their consumers, while others empower communities and organizations to run their own studies aimed at assessing whether they are effectively implementing EBIs and ways to improve that implementation.

MANAGING AND ADAPTING PRACTICE

Managing and Adapting Practice (MAP) is a knowledge management system with a direct service model that provides a way to develop and tailor an intervention based on research evidence so that the intervention is best suited to the youth receiving it. This system, which was developed by Bruce Chorpita of the University of California, Los Angeles, and Eric Daleiden from PracticeWise LLC, involves a feedback and local evidence component that involves real-time monitoring of progress during implementation and adapting practices appropriately because, as Chorpita noted, “Without being dynamic, we are not always going to succeed.”

Chorpita and Daleiden developed MAP to coordinate and leverage both generalized knowledge, which stems from theory and randomized trials, and local knowledge, which is specific to the locale or even the individual for whom the intervention is implemented. Chorpita pointed out

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

that often interventions are well suited for some but not all of the target population, and many have features that are not essential. “It’s like if you go to buy a car and you only want one feature, but you have to get the other features you don’t want because they are all bundled together. That is a bit how it felt when we were shopping for evidence-based programs in Hawaii,” Chorpita said. He added that better guidance is needed for how to deliver the information necessary to make real-time dynamic decisions, such as what intervention to implement when the one already implemented does not appear to be working.

MAP is a way to build treatment in a rational manner, Chorpita said. It is based on analyses Chorpita and his colleagues did on more than 700 randomized trials of EBIs for youths. From these analyses, they developed an easy-to-use automated system for culling EBIs relevant to the characteristics of the child being treated and showing which EBIs work best together for that individual. The system can then issue a one-page summary of information relevant to the planning and adapting of care for that child. The individual practices are represented as two-page “practice guides” that summarize how to implement the relevant EBI procedures (such as teaching problem-solving skills or adapting negative thinking). “Process guides” are also available that outline how to piece the practices together when several are used as part of the treatment.

Chorpita’s MAP system also has a clinical dashboard that shows the progress being made with a treatment while simultaneously showing the practice history, so practice can be appropriately adapted. Progress is rated based on assessments using locally relevant measures on a schedule that is tailored either to fit the child’s presentation and/or the local system requirements.

A study that Daleiden, Chorpita, and colleagues performed in Hawaii found that use of MAP was linked to rates of improvement that were more than twice that achieved before the system was applied, with an effect size of 0.76 (Daleiden et al., 2006). The cost of MAP per client is about as much as an average EBI, but it serves multiple problems, Chorpita noted, so it can be applied with greater efficiency. A randomized trial of a modular treatment approach based on the MAP system but developed specifically for youth with anxiety, depression, or conduct problems found significantly greater rates of improvement compared with providing either a standard EBI or usual care (see Figure 2-1) (Weisz et al., 2012).

Providers also expressed greater satisfaction when they used the modular system. “This kind of modular way of repackaging what we have seems to work better. It is guided, informed, and adaptive, so it lets you be dynamic and responsive when looking at youth’s real-time outcome data,” Chorpita said.

Chorpita is continuing to improve EBI treatment architecture by finding

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

image

FIGURE 2-1 Rate of improvement on internal, external, and total symptoms, and
family-nominated top problems, comparing standard and modular evidence-based
treatments (EBTs) and usual care.

SOURCES: Chorpita, 2014; adapted from Weisz et al., 2012.

structured ways to respond to poor outcomes, poor engagement, change in treatment focus, comorbid interference, or other emergent problems. The need for such dynamic therapy was revealed by a study Chorpita conducted that found that 69 percent of cases had a client event, such as the death of a loved one or a school expulsion, that caused treatment to derail with no guidance on what should be done next (Chorpita et al., 2014). After such events, providers were able to return to the treatment program during the session only 20 percent of the time. Instead many tended to “go rogue,” as Chorpita put it, and respond not based on any specific EBI. In this study, they found that when a critical event is disclosed in session, only 33 percent of the time will a therapist use content from the protocol and attempt to relate it to the crisis. “We don’t think this is good enough. If something goes wrong in the middle of a treatment encounter, we have not given providers the support that says ‘Here’s what you do in that situation,’” Chorpita said. He is currently applying for funding to study how to bring dynamic design down to the specific encounter level.

Chorpita summed up his presentation by saying, “We are never going to be able to predict all the things we need to do, so we need to think about exception management. It is not about more discovery, but rather taking what we already have in our catalog and reorganizing it so we can

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

be more efficient with it—extending, not replacing, what we have done so far.” Later during discussion, David Brent of the University of Pittsburgh concurred with Chorpita, noting that “When we were developing a treatment for suicidal kids that was very structured, the therapist would come back to supervision and say ‘our kids’ problems keep getting in the way of our ability to deliver treatment.’ That is a real problem.”

In response to a question from a participant on how relevant the MAP system is to children with disabilities, Chorpita noted that his analyses found that EBIs designed for different populations, such as children in foster care, those in the juvenile justice system, and those with developmental disabilities, tended to use some of the same basic elements. “There is a finite set of things we do that help children, whether they are delivered by a soccer coach, a schoolteacher, or a therapist at a clinic. What we are looking at now is how to put more interfaces around that knowledge base so people who encounter kids with any kind of problem are in a position to take informed action and have a therapeutic influence on the child’s life.” He added, “We need to be talking across literatures and organizations and across populations of kids. It is time to build those translations. We have discovered most of the answers, and we need to figure out how to put them in the hands of all these different people.”

Eric Bruns of the University of Washington School of Medicine concurred, noting in his presentation that he has been trying to infuse common elements and factors of evidence-based practice (EBP) into real-world systems in schools by using Chorpita’s MAP and wraparound coordinated care. He noted that care coordinators rated the usefulness of the Web-based MAP resources Chorpita has developed almost as highly as did therapists.

Bruns added that public systems, such as state departments of child welfare and juvenile justice, are amenable to applying behavioral health EBP if they focus on the outcomes important to those systems. He has been working with the Washington State Children’s Administration to implement for child welfare applications a suite of behavioral EBIs, including The Incredible Years®, which was originally designed for school systems, as well as more traditional child welfare prevention interventions such as SafeCare.

Recognizing the importance of providing infrastructure support for implementers of EBIs, including implementation strategies, Bruns and his colleagues developed a Web-based guidance tool for social workers to pick the right EBI based on the characteristics of the child they are working with, as well as a readiness assessment, so they can choose providers able to implement the EBI, and enhancements to the existing suite of EBIs, such as cross-EBI motivational enhancement training. The researchers also developed a standardized cross-intervention fidelity monitoring strategy that provides consistent information needed to manage comprehensive implementation of eight EBIs for a statewide child welfare system. This

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

BOX 2-1
Examples of Strategies and Products to Support a Multi-Evidence-Based Intervention Public System Improvement Rollout

  • A unified approach to evidence-based prevention (EBP) fidelity supports and monitoring
  • The Guidance Tool
    • Detailed set of EBP referral guidelines for use by California social workers
  • The Toolkit—Provider fidelity tracking database using consistent categories
    • Facilitates compliance and provision of technical assistance
  • Structured EBP readiness assessment
    • Used by Children’s Administration regional staff during contract negotiations
  • EBP Staff Selection Guide
    • Pretraining agreement signed by provider agency representative in advance of EBP training
  • Enhancements to existing suite of EBPs
    • For example, motivational enhancement training
  • Data analysis and use of information to inform programming
    • For example, differential rates of EBP use across regions

SOURCE: Bruns, 2014.

information included how adequate referrals were and provider compliance and competence (see Box 2-1).

STUDY DESIGNS

Naihua Duan, retired Professor of Biostatistics in Psychiatry from Columbia University, reported on new study designs that have emerged in recent years to help tailor EBIs more appropriately to the population in which they are implemented and go beyond the standard, two-armed randomized controlled trials, in which an intervention is tested compared to usual care without the intervention.

Factorial trials can test multiple components of an intervention, treatment, or prevention program, or an implementation strategy. Testing multiple components simultaneously enables understanding of how to optimize them and reveals which are core elements that must be part of the inter-

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

vention or implementation strategy versus elements that can be optional (Chakraborty et al., 2009; Collins et al., 2005, 2007, 2011), Duan said.

Conjoint analyses are factorial trials embedded in surveys of potential consumers for a hypothetical intervention or implementation strategy. For these analyses, researchers devise hypothetical variations of the intervention and ask consumers to rate or rank them. Their answers are used to determine the ultimate design of an intervention or implementation strategy (Green and Srinivasan, 1978; Lee et al., 2012).

Another type of design, sequential, multiple assignment, randomized trial (SMART), can be used to develop and evaluate adaptive treatment strategies (Chakraborty and Murphy, 2014; Collins et al., 2007; Murphy, 2003, 2005). With an adaptive treatment strategy, an individually customized sequence of treatments is delivered. When one treatment fails for a specific patient, the next treatment in sequence is delivered. SMART evaluates a variety of candidate adaptive treatment strategies to identify the optimal strategy for subsequent implementation.

David Mohr, Director of the Center for Behavioral Intervention Technologies at Northwestern University, added that SMART trials could also be useful in identifying which subpopulations need more intensive and thus more expensive interventions, enabling the bulk of the population to initially receive lower-cost care. “You can think of it not just from an outcomes perspective, but also in terms of cost-effectiveness,” Mohr said.

Duan also reported on mixed methods, which integrate qualitative and quantitative techniques. Mixed methods can be especially useful for studies with multiple objectives, such as studies on implementation, dissemination, and quality improvement, he said (Green et al., 2014; Palinkas et al., 2013). A typical study design would test a primary aim, but with implementation and dissemination there are multiple aims that are all important and should be considered simultaneously, Duan noted. These multiple aims can drive different study designs. Optimal design and purposeful sampling is a mixed methods study design aimed at achieving a compromise across different methods, according to Duan (Palinkas et al., 2013).

Later in the discussion, Duan emphasized the importance of providing technical assistance in methodology as part of the infrastructure development for prevention programs, including statistical methods and technology that can enable evaluations in local communities. “There needs to be some level of technology-based infrastructure development that will allow us to empower the local community to do the kind of evaluation using local data that might be useful to inform adaptation,” he said. Such infrastructure could also be useful at state and federal levels.

As Duan made clear, ownership of technology is increasingly being distributed across the population at large, enabling customization of objectives and procedures such that “it is possible to do things that might not

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

have been thinkable 10 or even 5 years ago.” He pointed out that schools and service agencies have the potential to use the hardware and software they already have for their own data entry, management, processing, and analysis, which could aid implementation and dissemination; however, technical assistance is needed in order for this potential to materialize. “This is a way to mitigate the methodology, in particular, statistics, as a barrier,” Duan said, as there often is a need for local investigations to address local issues. Larry Palinkas of the University of Southern California School of Social Work added that a critical part of community-based participatory research is the methodology that can enable research–practice partnerships to work at the locales where EBIs are implemented.

Local can mean at the school or agency level but also at the individual level. Duan and colleagues (Richard Kravitz, Chris Schmid, and Ida Sim) have been developing the technology and statistical infrastructure for the Personalized Research for Monitoring Pain Treatment (PREEMPT) study1 to facilitate the implementation of single-patient trials, which could be a useful implementation tool for individualized decision making for clinical treatments (Duan et al., 2013; Kravitz and Duan, 2014). He noted the same methodology used in PREEMPT could be used to conduct single-agency or single-school trials. In these trials, the individual patient, school, or agency tests different interventions in a systematic fashion such that there is balanced assignment of time intervals and repeated outcome assessments at least once per time period.

Duan explained that essentially, the entity implementing the intervention acts as its own control group. For example, the outcome can be compared between the time periods during which a school implemented one EBI versus the other time periods during which the same school did not use the EBI or used a different EBI. “We often don’t know whether the new procedure is going to be much better than the existing procedure for the specific locale, and in these situations such a local investigation can be helpful,” Duan noted. Duan and colleagues are currently developing apps on Android devices for clinicians and patients to use to design single-patient trials and to implement the trials and collect the data in the PREEMPT study. Such apps could also be adapted to facilitate the infrastructure needs for prevention programs for youth, he said. “This methodology development hopefully will stimulate more local investigations that use empirical approaches, and it will empower local organizations to use their own data to address their own questions, which might be a way to help generate buy in,” Duan said. Hendricks Brown of Northwestern University agreed, noting, “Every community I have gone to claims they are unlike everybody

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1More information about the PREEMPT study can be found at http://www.ucdmc.ucdavis.edu/chpr/preempt (accessed October 24, 2014).

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

else,” so a program tested elsewhere will not necessarily work for them. But giving these communities the opportunity to conduct their own trials is likely to engage them more “as opposed to doing something deliberate to the community that they do not necessarily want,” Brown said.

Mohr added that adaptive or single-entity trials “give us the opportunity to examine outcomes in real time and in real use” while preserving randomization and other benefits of controlled trials that enable collection of nonbiased information. He envisions agencies curating and making these interventions available and then continuously monitoring the results of their implementations in real-world settings. “Like an open-panel horse race, when those applications demonstrate locally that they are not as effective as others, they can essentially be dropped from the system, leaving those remaining to prove whether they are worthy.” Interventions could be added over time into the system, whose continuous monitoring would be akin to postmarketing surveillance, which provides data while protecting the interest of consumers and payers, Mohr said.

BEHAVIORAL INTERVENTION TECHNOLOGY

Mohr gave a presentation on behavioral intervention technology (BIT), which involves using mobile phones, tablets, computers, and sensor data to promote behavior change in support of health, mental health, and wellness. He noted that BITs got off the ground about 10 to 15 years ago when Web-based interventions such as MoodGYM were first developed. This online, interactive program uses principles of cognitive behavioral therapy and relaxation and meditation techniques to prevent and help individuals cope with depression (MoodGYM, 2014). MoodGYM consists of several modules, including an interactive game, anxiety and depression assessments, a downloadable relaxation audio file, and a workbook and feedback assessment. There is no coaching component. A study of the benefits of a MoodGYM delivered as part of a high school curriculum (N = 157) found that adolescents who were randomized to use MoodGYM experienced a significantly faster rate of decline in depressive symptoms than students randomized to the usual curriculum. The effect size for MoodGYM was not significant immediately after the intervention but was moderate and significant 20 weeks after the intervention. However, there were no significant intervention effects on depression status, attributional style, depression literacy, and attitudes (O’Kearney et al., 2009). “We see this a lot—simply providing these Web-based interventions to people often does not work,” Mohr said.

Text messaging is another BIT used frequently in youth-targeted prevention programs, Mohr reported. Although generally well accepted, a systematic review did not find consistent improvement in complex health

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

behaviors, such as physical activity or smoking cessation, when text messaging prevention programs are applied, although they are useful for providing reminders for simple behaviors, such as taking a medication and going to appointments (Preston et al., 2011).

Smartphone applications have recently blossomed, with an estimated 40,000 health apps available in app stores, including more than 2,000 apps for specific health conditions, Mohr reported. “The good news is that most of them are free. The bad news is that most of them are of extremely poor quality, and there is no clear evidence base for them,” Mohr said. One study of an eatery app aimed at supporting diet changes in adults found that although there were nearly 200,000 downloads of the app, 86 percent of those downloads were never used and less than 3 percent of consumers used the application more than 10 times (Helander et al., 2014).

“My take-home point is that technology is great, but humans really are important,” Mohr stressed. He noted a study (Mohr et al., 2013) that found that coach-supported Web-based interventions had significantly more logins than stand-alone Web treatments (Cuijpers et al., 2009; Richards and Richardson, 2012). Such coach support can involve brief 10- to 15-minute phone calls or text messages and therefore does not require a lot of time on the part of the therapist, Mohr noted.

Mohr explored the scientific literature to develop a model for what type of coaching or support is the most effective in improving adherence to Web-based interactions. He developed a coaching model aimed at improving adherence, called supportive accountability (Mohr et al., 2011). The basic principle of supportive accountability is that users are more likely to adhere to a behavioral intervention technology if they have clear use goals and they know they will be communicating with a coach about whether or not they met those use goals. The value of this coaching is increased if users have a good therapeutic bond with the coach and view the coach as benevolent (having their best interest at heart) and competent. Motivation of participants also influences adherence and is variable. Users who are more intrinsically motivated likely need less coaching, while those who are externally motivated may require more. A third influence on adherence is communication bandwidth. Some forms of communication are wider, meaning they enable more types of nonverbal information to be conveyed, Mohr pointed out. When there is in-person communication, there are visual and voice cues in addition to the information conveyed, whereas communication via messaging lacks these cues. Surprisingly, researchers found it is the latter, leaner form of communication that can provide stronger relationships, especially initially, because people tend to make positive inferences in the absence of information. However, if there is a breach in the relationship, richer communication channels are required to repair the relational difficulties.

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

Using this information, Mohr developed a model that he tested and found worked well (Mohr et al., 2011, 2013). He then applied the supportive accountability model by developing computer interventions embedded in automated peer networks that display features for support and accountability, such as interventions that display information about when people were last logged in and what their activity was (Duffecy et al., 2013). When users have not logged in frequently enough, they receive emails stating that somebody in their group is missing them and they should come back. “We are providing people with what they need to hold each other accountable,” Mohr said. There also is a comments feature, which creates community and also displays accountability.

When Mohr tested one of his BIT interventions, which aims at preventing depression in youth, he found that over a 10-week period, users had a mean of 24 logins, which is a very high login rate for a Web-based intervention, according to Mohr. In response to usability testing, Mohr is rebuilding his intervention so it can be used on mobile devices. “Not surprisingly, kids want this on a mobile device, and they want text messages not emails,” Mohr said (see Figure 2-2). Mohr also modified his Web-based system so it can be used in a mobile app to prevent depression in cancer

image

FIGURE 2-2 Peer networked Web-based intervention for depression.
SOURCE: Mohr, 2014.

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

survivors. Initial tests of this app found that it increased improvement in mood and reduced users’ degree of depression more so than peer support (Duffecy et al., 2013).

Given the results he has had so far, Mohr stressed devising an appropriate system in which a BIT is used that provides the proper support for the technology. “I don’t think we can just put an app on the app store and expect that kids will download and use it effectively. We need to think more deeply about how these are going to be embedded in systems,” Mohr said.

Mohr also talked about the need to make technologies more user-friendly and easier to use, which includes creating intelligent systems that can use sensor data from mobile phones to understand the context in which users are. “We want to fit our interventions into the fabric of people’s lives. People take their cell phones with them, and a lot of data can be collected from cell phones,” he said. Those data include location, motion, social activity (such as texts, phone calls, and emails), use of apps and browsers on the phone, and other data such as what the weather currently is like in the environs of the user. A lot of Mohr’s work now is focused on using sensors and cell phones to detect whether users are at a place of entertainment, work, or a friend’s home; what they are doing; whether they are alone; their sleep patterns; their emotional state; and whether they are keeping their appointments. Mohr is using this information to develop behavioral interventions that positively reinforce desired behavior and offer suggestions for improving other behavior as well as provide information for clinicians.

But Mohr noted that it takes a long time to develop and test BITs—a time frame not well suited for digital devices, which tend to evolve swiftly. Thus, applications that use digital technologies may have relatively short life spans. He called for having rapid evaluation models that fit into the time line of technological development. He suggested not testing more apps for validation, as one would a pharmaceutical, but instead evaluating principles for how apps should be designed and implemented, thereby producing knowledge that can be more broadly applied. He also suggested eliminating the idea that applications should be “locked down” during evaluation, because the natural state of apps is that they are continually evolving. He also noted that knowledge about programming and developing apps is not transmitted across investigators. “Everybody is developing the same thing, nobody gets it right the first time, but we are not sharing our lessons,” Mohr emphasized. He described the Purple Development Environment as a model in which components such as logging tools, content delivery, visualizations, notification tools, and sensor data collection are developed in a modular and extensible manner, allowing them to be repurposed and refined across applications (Schueller et al., 2014).

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

REFERENCES

Bruns, E. J. 2014. Big thinking from small science: Promoting coordinated action to build knowledge-informed systems for youth and families. Presented at IOM and NRC Workshop on Harvesting the Scientific Investment in Prevention Science to Promote Children’s Cognitive, Affective, and Behavioral Health, Washington, DC.

Chakraborty, B., and S. A. Murphy. 2014. Dynamic treatment regimes. Annual Review of Statistics and Its Applications 1:447-464.

Chakraborty, B., L. M. Collins, V. J. Strecher, and S. A. Murphy. 2009. Developing multicomponent interventions using fractional factorial designs. Statistics in Medicine 28(21): 2687-2708.

Chorpita, B. F. 2014. Putting more evidence in evidence-based practice: Designing informed and efficient children’s mental health systems. Presented at IOM and NRC Workshop on Harvesting the Scientific Investment in Prevention Science to Promote Children’s Cognitive, Affective, and Behavioral Health, Washington, DC.

Chorpita, B. F., P. Korathu-Larson, L. Knowles, and K. Guan. 2014. Emergent life events and their impact on service delivery: Should we expect the unexpected? Professional Psychology: Research and Practice 45(5):387-393.

Collins, L. M., S. A. Murphy, V. N. Nair, and V. J. Strecher. 2005. A strategy for optimizing and evaluating behavioral interventions. Annals of Behavioral Medicine 30(1):65-73.

Collins, L. M., S. A. Murphy, and V. Strecher. 2007. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine 32(5 Suppl):S112-S118.

Collins, L. M., T. B. Baker, R. J. Mermelstein, M. E. Piper, D. E. Jorenby, S. S. Smith, B. A. Christiansen, T. R. Schlam, J. W. Cook, and M. C. Fiore. 2011. The multiphase optimization strategy for engineering effective tobacco use interventions. Annals of Behavioral Medicine 41(2):208-226.

Cuijpers, P., I. M. Marks, A. van Straten, K. Cavanagh, L. Gega, and G. Andersson. 2009. Computer-aided psychotherapy for anxiety disorders: A meta-analytic review. Cognitive Behavioral Therapy 38(2):66-82.

Daleiden, E. L., B. F. Chorpita, C. M. Donkervoet, A. A. Arensdorf, and M. Brogan. 2006. Getting better at getting them better: Health outcomes and evidence-based practice within a system of care. Journal of the American Academy of Child and Adolescent Psychiatry 45:749-756.

Duan, N., R. L. Kravitz, C. H. Schmid. 2013. Single-patient (N-of-1) trials: A pragmatic clinical decision methodology for patient-centered comparative effectiveness research. Journal of Clinical Epidemiology 66(8 Suppl):S21-S28.

Duffecy, J., S. Sanford, L. Wagner, M. Begale, E. Nawacki, and D. C. Mohr. 2013. Project Onward: An innovative e-health intervention for cancer survivors. Psychooncology 22(4):947-951.

Green, C. A., N. Duan, R. D. Gibbons, K. E. Hoagwood, L. A. Palinkas, and J. P. Wisdom. 2014 [Epub ahead of print]. Approaches to mixed methods dissemination and implementation research: Methods, strengths, caveats, and opportunities. Administration and Policy in Mental Health and Mental Health Services Research. http://link.springer.com/article/10.1007%2Fs10488-014-0552-6 (accessed November 11, 2014).

Green, P. E., and V. Srinivasan. 1978. Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research 5(2):103-123.

Helander, E., K. Kaipainen, I. Korhonen, and B. Wansink. 2014. Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: Retrospective cohort study. Journal of Medical Internet Research 16(4):e109.

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×

Kravitz, R. L., and N. Duan (Eds.). 2014. Design and implementation of N-of-1 trials: A user’s guide. AHRQ Publication No. 13(14)-EHC122-EF. Rockville, MD: Agency for Healthcare Research and Quality. http://www.effectivehealthcare.ahrq.gov/N-1-Trials. cfm (accessed September 25, 2014).

Lee, S. J., P. A. Newman, W. S. Comulada, W. E. Cunningham, and N. Duan. 2012. Use of conjoint analysis to assess HIV vaccine acceptability: Feasibility of an innovation in the assessment of consumer health-care preferences. International Journal of STD and AIDS 23(4):235-241.

Mohr, D. C. 2014. Behavioral intervention technologies for depression in youth. Presented at IOM and NRC Workshop on Harvesting the Scientific Investment in Prevention Science to Promote Children’s Cognitive, Affective, and Behavioral Health, Washington, DC.

Mohr, D. C., P. Cuijpers, and K. Lehman. 2011. Supportive accountability: A model for providing human support to enhance adherence to eHealth interventions. Journal of Medical Internet Research 13(1):e30.

Mohr, D. C., J. Duffecy, J. Ho, M. Kwasny, X. Cai, M. N. Burns, and M. Begale. 2013. A randomized controlled trial evaluating a manualized TeleCoaching protocol for improving adherence to a Web-based intervention for the treatment of depression. PLoS ONE 8(8):e70086.

MoodGYM. 2014. The MoodGYM training program. https://moodgym.anu.edu.au/welcome (accessed September 8, 2014).

Murphy, S. 2003. Optimal dynamic treatment regimes (with discussion). Journal of the Royal Statistical Society, Series B 65(2):331-366.

Murphy, S. A. 2005. An experimental design for the development of adaptive treatment strategies. Statistics in Medicine 24(10):1455-1481.

O’Kearney, R., K. Kang, H. Christensen, and K. Griffiths. 2009. A controlled trial of a school-based Internet program for reducing depressive symptoms in adolescent girls. Depression and Anxiety 26(1):65-72.

Palinkas, L. A., S. M. Horwitz, C. A. Green, J. P. Wisdom, N. Duan, and K. Hoagwood. 2013 [Epub ahead of print]. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research. http://link.springer.com/article/10.1007%2Fs10488-013-0528-y (accessed November 11, 2014).

Preston, K. E., T. A. Walhart, and A. L. O’Sullivan. 2011. Prompting healthy behavior via text messaging in adolescents and young adults. American Journal of Lifestyle Medicine 5(3):247-252.

Richards, D., and T. Richardson. 2012. Computer-based psychological treatments for depression: A systematic review and meta-analysis. Clinical Psychology Review 32(4):329-342.

Schueller, S. M., M. Begale, F. J. Penedo, and D. C. Mohr. 2014. Purple: A modular system for developing and deploying behavioral intervention technologies. Journal of Medical Internet Research 16(7):e181.

Weisz, J. R., B. F. Chorpita, L. A. Palinkas, S. K. Schoenwald, J. Miranda, S. K. Bearman, E. L. Daleiden, A. M. Ugueto, A. Ho, J. Martin, J. Gray, A. Alleyne, D. A. Langer, M. A. Southam-Gerow, R. D. Gibbons, and the Research Network on Youth Mental Health. 2012. Testing standard and modular designs for psychotherapy treating depression, anxiety, and conduct problems in youth: A randomized effectiveness trial. Archives of General Psychiatry 69(3):274-282.

Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Suggested Citation:"2 New Analytic Methods and Technologies for Dissemination, Implementation, and Quality Improvement." Institute of Medicine and National Research Council. 2015. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18964.
×
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Over the past few decades there have been major successes in creating evidence-based interventions to improve the cognitive, affective, and behavioral health of children. Many of these interventions have been put into practice at the local, state, or national level. To reap what has been learned from such implementation, and to explore how new legislation and policies as well as advances in technology and analytical methods can help drive future implementation, the Institute of Medicine-National Research Council Forum on Promoting Children's Cognitive, Affective, and Behavioral Health held the workshop "Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health" in Washington, DC, on June 16 and 17, 2014.

The workshop featured panel discussions of system-level levers and blockages to the broad implementation of interventions with fidelity, focusing on policy, finance, and method science; the role of scientific norms, implementation strategies, and practices in care quality and outcomes at the national, state, and local levels; and new methodological directions. The workshop also featured keynote presentations on the role of economics and policy in scaling interventions for children's behavioral health, and making better use of evidence to design informed and more efficient children's mental health systems. Harvesting the Scientific Investment in Prevention Science to Promote Children's Cognitive, Affective, and Behavioral Health summarizes the presentations and discussion of the workshop.

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