Depression and anxiety are among the leading causes of disability and mortality in the US. Although effective treatments exist, response to specific treatments varies widely among individuals. More accurate matching of individuals with treatments could significantly increase overall success rates. Matching or personalization could be accomplished either by pre-treatment prediction of response or by earlier detection of treatment failure (shorting the cycle time of trial-and-errbr). Recent evidence suggests that measures of neuropsychological performance (such as the NIMH RDoC constructs) or directly observed behaviors could predict or detect response to treatment more accurately than traditional symptom measures. Increasing use of mobile electronic devices will make it possible to widely and rapidly disseminate tools for these next-generation assessments. The purpose of this pilot project is to assess consumer engagement, predictive utility, and clinical applicability of mobile, IT-enabled assessment of cognitive, physical and social activity in patients seeking treatment for depression and anxiety.
The first aim will be to determine whether members of three health systems initiating treatment for depression and/or anxiety will complete cognitive assessment via internet and mobile devices, and whether members would be willing to collect and share physical and social activity data through mobile sensing devices or behavioral analytic apps installed on their smart phones.
The second aim will be to explore the utility of the data collected prior to and during treatment in either prediction of treatment outcomes or early detection of treatment failure.
The final aim will be to determine provider interest in and experience with accessing this information for treatment planning purposes.
Over the next decade, the classification and assessment of mental health conditions will be transformed by behavioral neuroscience. Next-generation assessments (including direct measures of behavior and neuropsychological performance) will supplant current symptom-based measures. This pilot study will prepare for that transformation by evaluating the feasibility, acceptability, and potential utility of nextgeneration assessments using mobile devides.
|Simon, Gregory E; Coleman, Karen J; Rossom, Rebecca C et al. (2016) Risk of suicide attempt and suicide death following completion of the Patient Health Questionnaire depression module in community practice. J Clin Psychiatry 77:221-7|
|Owen-Smith, Ashli; Stewart, Christine; Green, Carla et al. (2016) Adherence to common cardiovascular medications in patients with schizophrenia vs. patients without psychiatric illness. Gen Hosp Psychiatry 38:9-14|
|Scherrer, Jeffrey F; Salas, Joanne; Copeland, Laurel A et al. (2016) Prescription Opioid Duration, Dose, and Increased Risk of Depression in 3 Large Patient Populations. Ann Fam Med 14:54-62|
|Cummings, Janet R; Lynch, Frances L; Rust, Kristal C et al. (2016) Health Services Utilization Among Children With and Without Autism Spectrum Disorders. J Autism Dev Disord 46:910-20|
|Coleman, Karen J; Stewart, Christine; Waitzfelder, Beth E et al. (2016) Racial-Ethnic Differences in Psychiatric Diagnoses and Treatment Across 11 Health Care Systems in the Mental Health Research Network. Psychiatr Serv 67:749-57|
|Simon, Gregory E; Specht, Cynthia; Doederlein, Allen (2016) Coping With Suicidal Thoughts: A Survey of Personal Experience. Psychiatr Serv 67:1026-9|
|Rossom, Rebecca C; Simon, Gregory E; Beck, Arne et al. (2016) Facilitating Action for Suicide Prevention by Learning Health Care Systems. Psychiatr Serv 67:830-2|
|Hacker, Karen A; Penfold, Robert B; Arsenault, Lisa N et al. (2015) Effect of Pediatric Behavioral Health Screening and Colocated Services on Ambulatory and Inpatient Utilization. Psychiatr Serv 66:1141-8|
|Ahmedani, Brian K; Stewart, Christine; Simon, Gregory E et al. (2015) Racial/Ethnic differences in health care visits made before suicide attempt across the United States. Med Care 53:430-5|
|Coleman, Karen J; Lutsky, Marta A; Yau, Vincent et al. (2015) Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records. J Autism Dev Disord 45:1989-96|
Showing the most recent 10 out of 28 publications