Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
1485 participants
INTERVENTIONAL
2010-06-30
2013-09-30
Brief Summary
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1. Develop the innovative depression care management technology, including the speech recognition technology for automated monitoring and patient prompts over time, automatic integration of the responses into the patient registry, and evidence-based decision-support algorithms for care actions;
2. Conduct the quasi-experiment in eight Los Angeles County Department of Health Services (LAC-DHS) clinics to test the interventions;
3. Use mixed-method evaluation to assess the extent of the implementation of the interventions, the acceptance to the providers and to the patients, and the impact on adoption of depression screening and treatment management over time, utilization, and cost of healthcare services, and patient health outcomes; and
4. Conduct a cost-effectiveness analysis of the three study arms. Successful completion of the study will demonstrate which Comparative Effectiveness Research (CER) adoption strategies are successful and why, their comparative cost-effectiveness, as well as which strategies are successful under which circumstances to inform system-wide implementation of same.
Hypotheses of the Proposed Study
The following are the main hypotheses of the study:
1. There will be statistically significant difference in the adoption of depression care screening and management over time among the three study groups.
1.1. The adoption rate will be Technology-supported care (TC) \> Supported Care (SC) \> Usual Care (UC).
2. There will be statistically significant difference in the depression symptom reduction, and better functional status, and quality of life among the three study groups.
2.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
3. There will be statistically significant difference in the diabetes care process and outcomes among the three study groups.
3.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
4. There will also be statistically significant differences in healthcare utilization among the three study groups, with least utilization in the TC group where the greatest level of technology is applied.
5. Of the three groups compared, the TC group will be the most cost-effective approach for accelerating adoption of the CER depression care results.
Detailed Description
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1. What is medical provider satisfaction with the technology used in the TC (Technology Care) group?
2. What is patient acceptance with the technology used in the TC group?
3. What factors are identified by medical providers and clinic administrators as related to satisfaction, barriers, and sustaining the intervention post-trial?
4. What are patients' reported satisfaction and facilitating factors and barriers to receipt and acceptance of depression care?
Conditions
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Keywords
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Study Design
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NON_RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
NONE
Study Groups
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Technology-supported care
This arm consists of Clinic Resource Management (CRM) clinics and serves as our intervention arm where the tested technology is implemented. Our overarching aim in these comparisons is to assess the potential effects of technology-facilitated depression symptom monitoring, relapse prevention, and medication adjustments and to examine depression care receipt and symptom improvement, patient/provider acceptance, and cost.
Technology-supported care
The depression care-management technology that will interact with patients is the Automated Speech Recognition (ASR) for remote monitoring data collection. The ASR will use automated telephone calls to reach out to patients to repeat depression screening using PHQ-9, triggered either by calendar date or upcoming appointments, and to remind patients of their appointments in pre-determined time. In addition, the ASR will apply a structured script to conduct automatic follow-up with patients regarding their depression treatment adherence and side effects in order to provide data to help primary medical providers promptly and optimally adapt treatment. The ASR script will also include structured relapse prevention prompts. For providers and administrators, the depression care-management technology aimed to improve their workflow regarding depression care is Enhanced Disease Registry (EDR)..
Supported-Care
This arm consists of CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study.
No interventions assigned to this group
Usual Care
This arm consists of non-CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study.
No interventions assigned to this group
Interventions
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Technology-supported care
The depression care-management technology that will interact with patients is the Automated Speech Recognition (ASR) for remote monitoring data collection. The ASR will use automated telephone calls to reach out to patients to repeat depression screening using PHQ-9, triggered either by calendar date or upcoming appointments, and to remind patients of their appointments in pre-determined time. In addition, the ASR will apply a structured script to conduct automatic follow-up with patients regarding their depression treatment adherence and side effects in order to provide data to help primary medical providers promptly and optimally adapt treatment. The ASR script will also include structured relapse prevention prompts. For providers and administrators, the depression care-management technology aimed to improve their workflow regarding depression care is Enhanced Disease Registry (EDR)..
Eligibility Criteria
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Inclusion Criteria
* receiving primary care at DHS safety net clinics
* having a current diagnosis of type 2 diabetes mellitus (non-gestational).
* have a working telephone or cellular phone.
Exclusion Criteria
* inability to speak either English or Spanish;
* a score of 2 or greater on the CAGE (4M) alcohol assessment;
* having schizophrenia, schizoaffective disorder, manic-depressive, or needing lithium;
* and cognitive impairment precluding ability to give informed consent or participating in the intervention, i.e., Short Portable Mental Status Questionnaire(SPMSQ) score of 6 or more errors.
18 Years
ALL
No
Sponsors
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Department of Health and Human Services
FED
University of Southern California
OTHER
Responsible Party
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Shinyi Wu
Associate Professor
Principal Investigators
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Shinyi Wu, PhD
Role: PRINCIPAL_INVESTIGATOR
University of Southern California
Locations
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El Monte Comprehensive Health Center
El Monte, California, United States
High Desert Comprehensive Health Center
Lancaster, California, United States
Long Beach Comprehensive Health Center
Long Beach, California, United States
H. Claude Hudson Comprehensive Health Center
Los Angeles, California, United States
Roybal Comprehensive Health Center
Los Angeles, California, United States
Olive View-UCLA Medical Center Diabetes Clinic
Sylmar, California, United States
Mid-Valley Comprehensive Health Center
Van Nuys, California, United States
Harbor Comprehensive Health Center
Wilmington, California, United States
Countries
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References
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Wells KB, Stewart A, Hays RD, Burnam MA, Rogers W, Daniels M, Berry S, Greenfield S, Ware J. The functioning and well-being of depressed patients. Results from the Medical Outcomes Study. JAMA. 1989 Aug 18;262(7):914-9.
Katon WJ. The comorbidity of diabetes mellitus and depression. Am J Med. 2008 Nov;121(11 Suppl 2):S8-15. doi: 10.1016/j.amjmed.2008.09.008.
Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001 Jun;24(6):1069-78. doi: 10.2337/diacare.24.6.1069.
Golden SH, Lazo M, Carnethon M, Bertoni AG, Schreiner PJ, Diez Roux AV, Lee HB, Lyketsos C. Examining a bidirectional association between depressive symptoms and diabetes. JAMA. 2008 Jun 18;299(23):2751-9. doi: 10.1001/jama.299.23.2751.
Lin EH, Katon W, Von Korff M, Rutter C, Simon GE, Oliver M, Ciechanowski P, Ludman EJ, Bush T, Young B. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care. 2004 Sep;27(9):2154-60. doi: 10.2337/diacare.27.9.2154.
U.S. Preventive Services Task Force. Screening for depression in adults: U.S. preventive services task force recommendation statement. Ann Intern Med. 2009 Dec 1;151(11):784-92. doi: 10.7326/0003-4819-151-11-200912010-00006.
Anderson RJ, Gott BM, Sayuk GS, Freedland KE, Lustman PJ. Antidepressant pharmacotherapy in adults with type 2 diabetes: rates and predictors of initial response. Diabetes Care. 2010 Mar;33(3):485-9. doi: 10.2337/dc09-1466. Epub 2009 Dec 23.
Ell K, Xie B, Quon B, Quinn DI, Dwight-Johnson M, Lee PJ. Randomized controlled trial of collaborative care management of depression among low-income patients with cancer. J Clin Oncol. 2008 Sep 20;26(27):4488-96. doi: 10.1200/JCO.2008.16.6371.
Cabassa LJ, Hansen MC, Palinkas LA, Ell K. Azucar y nervios: explanatory models and treatment experiences of Hispanics with diabetes and depression. Soc Sci Med. 2008 Jun;66(12):2413-24. doi: 10.1016/j.socscimed.2008.01.054. Epub 2008 Mar 12.
Katon W, Robinson P, Von Korff M, Lin E, Bush T, Ludman E, Simon G, Walker E. A multifaceted intervention to improve treatment of depression in primary care. Arch Gen Psychiatry. 1996 Oct;53(10):924-32. doi: 10.1001/archpsyc.1996.01830100072009.
Jin H, Wu S. Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study. J Med Internet Res. 2020 Mar 26;22(3):e17282. doi: 10.2196/17282.
Hay JW, Lee PJ, Jin H, Guterman JJ, Gross-Schulman S, Ell K, Wu S. Cost-Effectiveness of a Technology-Facilitated Depression Care Management Adoption Model in Safety-Net Primary Care Patients with Type 2 Diabetes. Value Health. 2018 May;21(5):561-568. doi: 10.1016/j.jval.2017.11.005. Epub 2017 Dec 6.
Ramirez M, Wu S, Jin H, Ell K, Gross-Schulman S, Myerchin Sklaroff L, Guterman J. Automated Remote Monitoring of Depression: Acceptance Among Low-Income Patients in Diabetes Disease Management. JMIR Ment Health. 2016 Jan 25;3(1):e6. doi: 10.2196/mental.4823.
Ell K, Katon W, Lee PJ, Guterman J, Wu S. Demographic, clinical and psychosocial factors identify a high-risk group for depression screening among predominantly Hispanic patients with Type 2 diabetes in safety net care. Gen Hosp Psychiatry. 2015 Sep-Oct;37(5):414-9. doi: 10.1016/j.genhosppsych.2015.05.010. Epub 2015 May 29.
Wu S, Vidyanti I, Liu P, Hawkins C, Ramirez M, Guterman J, Gross-Schulman S, Sklaroff LM, Ell K. Patient-centered technological assessment and monitoring of depression for low-income patients. J Ambul Care Manage. 2014 Apr-Jun;37(2):138-47. doi: 10.1097/JAC.0000000000000027.
Wu S, Ell K, Gross-Schulman SG, Sklaroff LM, Katon WJ, Nezu AM, Lee PJ, Vidyanti I, Chou CP, Guterman JJ. Technology-facilitated depression care management among predominantly Latino diabetes patients within a public safety net care system: comparative effectiveness trial design. Contemp Clin Trials. 2014 Mar;37(2):342-54. doi: 10.1016/j.cct.2013.11.002. Epub 2013 Nov 8.
Other Identifiers
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RFA-AE-10-001
Identifier Type: -
Identifier Source: org_study_id