ALACRITY has a Research Methods Core that provides quality control for the Center’s projects, serves as an incubator for innovative approaches to novel design and analytic methods that enhance the information yield of effectiveness data. It also uses big-data to aid the identification of populations in need of novel interventions, provides policy support, and integrates novel mobile technology approaches to community interventions. The Methods Core also evaluate the Center’s productivity and impact on the field and disseminates its methodological advances.
Our Center proposes a novel deployment focused model that both streamlines behavioral interventions for late- and mid-life mood disorders and improves their delivery in the community. In response to the Center’s and the field’s needs, the Research Methods Core (RMC) is developing novel methods outlined in three initiatives:
Initiative 1. Analytic methods to increase the efficiency and the information yield of T2 community-based effectiveness studies.
This initiative supports the Center’s program of behavioral interventions with multiple outcomes and distinct behavioral targets by developing analytic approaches:
1) To maximize information on intervention by jointly analyzing multiple correlated longitudinal outcomes reflecting meaningful dimensions of health and reducing sample size requirements;
2) To estimate the indirect effect of longitudinal, continuous behavioral mediators of outcomes thereby quantifying the intervention effect on outcome through the neurobiologically derived behavioral targets;
3) To increase the information yield of mobile health technology by using its multiple repeated assessments as a time-series and test differences between intervention groups.
Initiative 2. Approaches to integrating multiple big data sources to identify populations in need of novel interventions and deployment approaches and to policy support.
This initiative responds to the Center’s and the field’s need to identify subgroups with mood disorders underserved by the current health care system. Accordingly, it will develop: 1) Innovative approaches for integration of heterogeneously distributed electronic health record data and administrative insurance claims data from the New York City CDRN (http://www.nyccdrn.org/); 2) Methods for identifying individuals with multiple mental and physical conditions; and 3) Approaches to characterizing subgroups with poor outcomes. Combined with stakeholders’ input, this information can chart directions for future community interventions.
Initiative 3: Novel approaches to integrating mobile technology in community interventions taking into account the skill sets of patients and therapists, and the resources of community settings.
Mobile technology is embedded in the Center’s behavioral interventions to augment information available to community clinicians and to guide them in targeting their sessions. This initiative responds to the Center’s and the field’s need for mobile technology accessible to older and middle-aged mental health consumers and usable at busy community treatment settings.