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CompletedNCT01781013DCATUpdated Dec 5, 2014

Diabetes-Depression Care-management Adoption Trial

An interventional study of Technology-supported care in Depression and Diabetes Mellitus, sponsored by University of Southern California. Completed at 8 sites in United States. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2014-12-05.

Sponsored by University of Southern California · Not applicable, Interventional, and Health services research

Phase
Not applicable
Study type
Interventional
Enrollment
1,485
Allocation
Non-randomized
Ages
18 Years and older
Sex
All
01

Study summary

The specific aims of the proposed study are to:

  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.
Read the detailed description

In addition, the study will aim to answer the secondary research questions listed below:

  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?
02

Conditions studied

  • Depression
  • Diabetes Mellitus

Keywords

  • Depression screening
  • Depression monitoring
  • Chronic illness
  • Behavioral health
  • Care management
  • Automatic telephone assessment
  • Clinical decision support
  • Suicide alert
03

In context

Diabetes Mellitus

10,925 studies on the registry are indexed under Diabetes Mellitus; 1,319 are open to participants now.

This study's enrollment of 1,485 is above the median of 80 across 8,367 interventional studies indexed under Diabetes Mellitus.

Browse Diabetes Mellitus studies →

Lead sponsor

University of Southern California is the lead sponsor of 773 studies on the registry; 135 are open to participants now.

Of its 68 completed or terminated interventional studies of FDA-regulated products, 32 (47%) have results posted.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No

Inclusion criteria

  • age equal to or greater than 18 years
  • 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

Exclusion Criteria:

  • current suicidal ideation;
  • 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.

Provider and administrator inclusion criteria are: practicing or managing at one of the eight study sites; involved with diabetes or depression care

No specific exclusion criteria will be applied to providers and administrators.

05

Study design

Phase
Not applicable
Primary purpose
Health services research
Allocation
Non-randomized
Intervention model
Parallel assignment
Masking
None (open label)
Enrollment
1,485 participants (actual)

Study arms

  • Experimental
    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.

    Other: Technology-supported care

  • No intervention
    Supported-Care

    This arm consists of CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study.

  • No intervention
    Usual Care

    This arm consists of non-CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study.

Interventions

  • OtherTechnology-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)..

06

What researchers measure

Primary outcomes

  1. Change from baseline in depression outcome at 6-months

    Depression is measured using depression scales Patient Health Questionnaire (PHQ)-9. Major depression is classified as PHQ-9\>=10.

    Time frame: 6-months from enrollment

Secondary outcomes

  1. Change from baseline in diabetes self-care score in 6 months

    Diabetes self-care is measured using the Toolbert diabetes self-care scale.

    Time frame: 6 months from enrollment

Other outcomes

  1. Change from baseline in physical functional status in 6 months

    Physical functional status is measured using the physical component score of the SF-12 scale

    Time frame: 6 months from enrollment

  2. Change from baseline in mental functional status in 6 months

    Mental functional status is measured using the mental component score of the SF-12 scale

    Time frame: 6 months from enrollment

  3. Change from baseline in physical functional status in 12 months

    Physical functional status is measured using the physical component score of the SF-12 scale

    Time frame: 12 months from enrollment

  4. Change from baseline in mental functional status in 12 months

    Mental functional status is measured using the mental component score of the SF-12 scale

    Time frame: 12 months after enrollment

  5. Change from baseline of mental health-related functional impairment in 12 months

    Assessed using the Sheehan disability scale

    Time frame: 12 months from enrollment

  6. Change from baseline of mental health-related functional impairment in 6 months

    Assessed using the Sheehan disability scale

    Time frame: 6 months from enrollment

  7. Change from baseline in depression outcome in 12 months

    Depression is measured using depression scales Patient Health Questionnaire (PHQ)-9. Major depression is classified as PHQ-9\>=10.

    Time frame: 12 months from enrollment

  8. Change from baseline in diabetes self-care score in 12 months

    Diabetes self-care is measured using Toolbert diabetes self-care scale.

    Time frame: 12 months after enrollment

  9. Change from baseline of diabetes symptoms in 12 months

    Assessed using the Whitty-9 diabetes symptoms scale

    Time frame: 12 months from enrollment

  10. Change from baseline of diabetes symptoms in 6 months

    Assessed using the Whitty-9 diabetes symptoms scale

    Time frame: 6 months from enrollment

  11. Change from baseline in percentage of patients who receive HbA1C lab test in 12 months

    This is one of our diabetes care processes measure. We are going to analyze the percentage of patients who receive the requisite lab tests, including HbA1C, microalbumin, and lipid panel.

    Time frame: 12 months from enrollment

  12. Change from baseline in percentage of patients who receive the lipid panel lab test in 12 months

    This is one of our diabetes care processes measure. We are going to analyze the percentage of patients who receive the requisite lab tests, including HbA1C, microalbumin, and lipid panel.

    Time frame: 12 months from enrollment

  13. Change from baseline in percentage of patients who receive microalbumin lab test in 12 months

    This is one of our diabetes care processes measure. We are going to analyze the percentage of patients who receive the requisite lab tests, including HbA1C, microalbumin, and lipid panel.

    Time frame: 12 months from enrollment

  14. Change from baseline in percentage of patients who receive HbA1C lab test in 6 months

    This is one of our diabetes care processes measure. We are going to analyze the percentage of patients who receive the requisite lab tests, including HbA1C, microalbumin, and lipid panel.

    Time frame: 6 months from enrollment

  15. Change from baseline in percentage of patients who receive the lipid panel lab test in 6 months

    This is one of our diabetes care processes measure. We are going to analyze the percentage of patients who receive the requisite lab tests, including HbA1C, microalbumin, and lipid panel

    Time frame: 6 months from enrollment

  16. Change from baseline in percentage of patients who receive microalbumin lab test in 6 months

    This is one of our diabetes care processes measure. We are going to analyze the percentage of patients who receive the requisite lab tests, including HbA1C, microalbumin, and lipid panel.

    Time frame: 6 months from enrollment

  17. Change from baseline in percentage of patients whose HbA1C is in control in 12 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose HbA1C is in control pre- and post-intervention. HbA1C is considered controlled if it is \<7%.

    Time frame: 12 months from enrollment

  18. Change from baseline in percentage of patients whose microalbumin is in control in 12 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose microalbumin is in control pre- and post-intervention. Microalbumin is considered controlled if it is \<30 microg/mg.

    Time frame: 12 months from enrollment

  19. Change from baseline in percentage of patients whose total cholesterol is in control in 12 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose total cholesterol is in control pre- and post-intervention. Total cholesterol is considered controlled if it is \<200mg/dL.

    Time frame: 12 months from enrollment

  20. Change from baseline in percentage of patients whose LDL cholesterol is in control in 12 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose LDL cholesterol is in control pre- and post-intervention. LDL cholesterol is considered controlled if it is \<100mg/dL.

    Time frame: 12 months from enrollment

  21. Change from baseline in percentage of patients whose HDL cholesterol is in control in 12 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose HDL cholesterol is in control pre- and post-intervention. HDL cholesterol is considered controlled if it is \<40mg/dL.

    Time frame: 12 months from enrollment

  22. Change from baseline in percentage of patients whose triglycerides is in control in 12 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose triglycerides is in control pre- and post-intervention. Triglycerides is considered controlled if it is \>200mg/dL.

    Time frame: 12 months from enrollment

  23. Change from baseline in percentage of patients whose HbA1C is in control in 6 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose HbA1C is in control pre- and post-intervention. HbA1C is considered controlled if it is \<7%.

    Time frame: 6 months from enrollment

  24. Change from baseline in percentage of patients whose microalbumin is in control in 6 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose microalbumin is in control pre- and post-intervention. Microalbumin is considered controlled if it is \<20mg/L.

    Time frame: 6 months from enrollment

  25. Change from baseline in percentage of patients whose total cholesterol is in control in 6 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose total cholesterol is in control pre- and post-intervention. Total cholesterol is considered controlled if it is \>240mg/dL

    Time frame: 6 months from enrollment

  26. Change from baseline in percentage of patients whose LDL cholesterol is in control in 6 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose LDL cholesterol is in control pre- and post-intervention. LDL cholesterol is considered controlled if it is \>160mg/dL.

    Time frame: 6 months from enrollment

  27. Change from baseline in percentage of patients whose HDL cholesterol is in control in 6 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose HDL cholesterol is in control pre- and post-intervention. HDL cholesterol is considered controlled if it is \>60mg/dL.

    Time frame: 6 months from enrollment

  28. Change from baseline in percentage of patients whose triglycerides is in control in 6 months

    This is part of our diabetes outcome measure. We would like to know the percentage of patients whose triglycerides is in control pre- and post-intervention. Triglycerides is considered controlled if it is \<150mg/dL

    Time frame: 6 months from enrollment

  29. Change from baseline to 12 months in number of outpatient visits during the past 6 months

    This is part of our utilization measure. We would like to know the number of outpatient visits during 6-months before baseline and between 6- and 12-months after enrollment.

    Time frame: 12 months from enrollment

  30. Change from baseline to 6 months in number of outpatient visits during the past 6 months

    This is part of our utilization measure. We would like to know the number of outpatient visits during 6-months before baseline and during the 6-months after enrollment.

    Time frame: 6 months from enrollment

  31. Change from baseline to 12 months in percentage of patients who were hospitalized during the past 6 months

    This is part of our utilization measure. We would like to know the percentage of hospitalized patients during 6-months before baseline and between 6- and 12-months after enrollment.

    Time frame: 12 months from enrollment

  32. Change from baseline to 6 months in percentage of hospitalized patients during the past 6 months

    This is part of our utilization measure. We would like to know the percentage of hospitalized patients during 6-months before baseline and during the 6-months after enrollment.

    Time frame: 6 months from enrollment

  33. Change from baseline to 12 months in percentage of patients with ER visits during the past 6 months

    This is part of our utilization measure. We would like to know the percentage of patients with ER visits during 6-months before baseline and between 6- and 12-months after enrollment.

    Time frame: 12 months from enrollment

  34. Change from baseline to 6 months in percentage of patients with ER visits during the past 6 months

    This is part of our utilization measure. We would like to know the percentage of patients with ER visits during 6-months before baseline and during the 6-months after enrollment.

    Time frame: 6 months from enrollment

  35. Difference between cost of care management in the intervention group and the control groups over a 12-month period per patient

    Cost of care management includes automated phone calls, provider time, costs associated with reviewing tasks and follow-ups.

    Time frame: 12 months

  36. Change from baseline to 12 months in percentage of patients satisfied with care received for diabetes

    Measured by the percentage of patients who answered "satisfied" or "very satisfied" to the question "How satisfied / dissatisfied are you with the overall health care available to you for your diabetes?" (with a 5-point Likert scale response option)

    Time frame: 12 months from enrollment

  37. Change from baseline to 6 months in percentage of patients satisfied with care received for diabetes

    Measured by the percentage of patients who answered "satisfied" or "very satisfied" to the question "How satisfied / dissatisfied are you with the overall health care available to you for your diabetes?" (with a 5-point Likert scale response option)

    Time frame: 6 months from enrollment

  38. Change from baseline to 12 months in percentage of patients satisfied with care received for depression

    Measured by the percentage of patients who answered "satisfied" or "very satisfied" to the question "How satisfied / dissatisfied are you with the clinical help received with your emotional problem?" (with a 5-point Likert scale response option)

    Time frame: 12 months from enrollment

  39. Change from baseline to 6 months in percentage of patients satisfied with care received for depression

    Measured by the percentage of patients who answered "satisfied" or "very satisfied" to the question "How satisfied / dissatisfied are you with the clinical help received with your emotional problem?" (with a 5-point Likert scale response option)

    Time frame: 6 months from enrollment

07

Study locations

8 sites
  • El Monte Comprehensive Health Center
    El Monte, California 91731, United States
  • High Desert Comprehensive Health Center
    Lancaster, California 93536, United States
  • Long Beach Comprehensive Health Center
    Long Beach, California 90813, United States
  • H. Claude Hudson Comprehensive Health Center
    Los Angeles, California 90007, United States
  • Roybal Comprehensive Health Center
    Los Angeles, California 90022, United States
  • Olive View-UCLA Medical Center Diabetes Clinic
    Sylmar, California 91342, United States
  • Mid-Valley Comprehensive Health Center
    Van Nuys, California 91405, United States
  • Harbor Comprehensive Health Center
    Wilmington, California 90744, United States
08

References and documents

Publications

  • 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. PubMed 2754791 ↗
  • 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. PubMed 18954592 ↗
  • 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. PubMed 11375373 ↗
  • 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. PubMed 18560002 ↗
  • 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. PubMed 15333477 ↗
  • 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. PubMed 19949144 ↗
  • 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. PubMed 20032276 ↗
  • 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. PubMed 18802161 ↗
  • 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. PubMed 18339466 ↗
  • 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. PubMed 8857869 ↗
  • 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. PubMed 32213473 ↗
  • 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. PubMed 29753353 ↗
  • 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. PubMed 26810139 ↗
  • 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. PubMed 26059979 ↗
  • 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. PubMed 24525531 ↗
  • 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. PubMed 24215775 ↗
09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Dec 5, 2014, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT01781013
Lead sponsor
University of Southern California
Collaborators
Department of Health and Human Services
Responsible party
Shinyi Wu (Associate Professor, University of Southern California) — Principal investigator
First posted
Jan 31, 2013
Start date
Jun 2010
Primary completion
Sep 2013
Completion
Sep 2013
Last update
Dec 5, 2014

Study contacts

Shinyi Wu, PhD
principal investigator · University of Southern California

Oversight

Data monitoring committee
Yes
View the source record on ClinicalTrials.gov ↗

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