<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03249077</url>
  </required_header>
  <id_info>
    <org_study_id>1R01DK115237</org_study_id>
    <nct_id>NCT03249077</nct_id>
  </id_info>
  <brief_title>Evaluating the Implementation of the Diabetes Prevention Program in an Integrated Health System</brief_title>
  <official_title>Evaluating the Implementation of the Diabetes Prevention Program in an Integrated Health System</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Kaiser Permanente</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Kaiser Permanente</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      In preparation for the roll out of the Medicare Diabetes Prevention Program (DPP) in 2018,&#xD;
      Kaiser Permanente Northwest (KPNW), a large, integrated health care system, plans to pilot&#xD;
      the implementation of DPP starting in April 2017. Patients 19-75 years old at high risk for&#xD;
      diabetes will be offered DPP online or DPP in-person. A pragmatic, rigorous, quantitative and&#xD;
      qualitative evaluation will be conducted to compare patients enrolled in DPP (either online&#xD;
      or in-person) to those not enrolled to better inform future implementation efforts of DPP&#xD;
      within and outside of KPNW. This study is a natural experiment project.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The prevalence of prediabetes and obesity among U.S. adults age 40 and older is significant,&#xD;
      with over 30% having prediabetes and over 40% having obesity.[1,2] Prediabetes and obesity&#xD;
      increase the risk for diabetes, cardiovascular disease, and poor quality of life, and are&#xD;
      responsible for substantial healthcare costs.[3] In response to the multi-level burden of&#xD;
      prediabetes and obesity, there have been several efforts to prevent diabetes at the&#xD;
      population level and reduce healthcare costs,[4,5] including national implementation and&#xD;
      reimbursement of the successful Diabetes Prevention Program (DPP).&#xD;
&#xD;
      Beginning in April 2018, the Centers for Medicare &amp; Medicaid Services (CMS) made a landmark&#xD;
      decision to reimburse clinical and non-clinical settings for providing DPP to Medicare&#xD;
      beneficiaries (i.e., Medicare DPP); this coverage is currently for in-person DPP only and not&#xD;
      digital DPP.[6,7] CMS's decision to cover DPP among older adults with prediabetes further&#xD;
      catapulted efforts within healthcare organizations to address the increasing number of&#xD;
      individuals with diabetes receiving care in their facilities. However, few studies have&#xD;
      examined the sustainability of providing DPP based on maintenance of the effect (i.e.,&#xD;
      long-term change in weight and HbA1c), healthcare costs, participant experience, and&#xD;
      organizational support. In addition, attracting individuals to DPP and similar lifestyle&#xD;
      change interventions remains a significant challenge and identifying useful approaches is&#xD;
      important.[8-11] Lastly, whereas the effectiveness of in-person DPP is well-established,&#xD;
      prior studies evaluating the effect of digital DPP identified positive outcomes but had&#xD;
      significant methodological limitations, such as a single arm pre- / post-test design and&#xD;
      participant-reported outcomes.[12,13-19]&#xD;
&#xD;
      In 2017 Kaiser Permanente Northwest (KPNW), a large, integrated health system serving Oregon&#xD;
      and southwest Washington, began piloting both digital and in-person versions of DPP for its&#xD;
      adult health plan members with prediabetes and obesity. The purpose of this mixed-methods,&#xD;
      natural experiment is to evaluate this large health system initiative by assessing the&#xD;
      effects of both digital and in-person DPP on change in weight and HbA1c, health behaviors,&#xD;
      and psychosocial factors. Also, sustainability based on cost-effectiveness and patient and&#xD;
      healthcare stakeholder perspectives will be examined.&#xD;
&#xD;
      KPNW patients eligible to participate in DPP (digital or in-person) will be identified and&#xD;
      recruited using the electronic health record (EHR). Both the digital and in-person&#xD;
      (group-based) DPP programs will be delivered over 12 months. Demographic and clinical data to&#xD;
      be included in analyses will also be extracted from the EHR. Behavioral and psychosocial&#xD;
      questionnaires will be administered to DPP enrollees and non-enrollees online using REDCap.&#xD;
      Semi-structured qualitative interviews will be conducted with a subset of DPP enrollees and&#xD;
      non-enrollees to understand reasons for enrollment and likes/dislikes about the program.&#xD;
      Healthcare system providers and stakeholders will also be interviewed to capture factors&#xD;
      related to sustainability of offering DPP within the health system.&#xD;
&#xD;
      For the primary analysis, investigators plan to model 12- and 24-month weight and HbA1c&#xD;
      trajectories using a linear mixed effects model using time since baseline as the time axis.&#xD;
      Because randomization is not feasible in this real-world implementation of DPP, propensity&#xD;
      score adjustment will be used to control for potential confounding. Furthermore,&#xD;
      investigators will conduct an economic evaluation over the 12-month follow-up period for both&#xD;
      the digital and in-person DPP cohorts as well as over the 24-month period for the digital DPP&#xD;
      cohort from the perspective of the health plan, following best practices,[20] and guided by&#xD;
      previous economic analyses of DPP interventions.[21-26]&#xD;
&#xD;
      The mixed-methods, natural experiment design investigators will use to evaluate KPNW's&#xD;
      implementation of digital and in-person DPP will build on existing evidence related to DPP&#xD;
      effectiveness across the two delivery modes on change in weight and HbA1c over time. In&#xD;
      addition, the cost-effectiveness analysis will determine the impact of digital and in-person&#xD;
      DPP on return on investment for healthcare systems and sustainability of the program.&#xD;
      Findings from our evaluation will therefore inform best practices for implementing and&#xD;
      sustaining DPP within large healthcare systems.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">July 1, 2017</start_date>
  <completion_date type="Actual">April 15, 2021</completion_date>
  <primary_completion_date type="Actual">April 15, 2021</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Weight</measure>
    <time_frame>Baseline through 12 months</time_frame>
    <description>Weight obtained from the electronic health record</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>HbA1c</measure>
    <time_frame>Baseline through 12 months</time_frame>
    <description>HbA1c obtained from the electronic health record</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Cost-effectiveness</measure>
    <time_frame>Baseline through 12 months; and Baseline through 24 months (for digital DPP comparison to usual care only).</time_frame>
    <description>Cost data will include: 1) medical care; and 2) the cost of intervention delivery, obtained from administrative and electronic health records.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Weight (24-month for digital DPP)</measure>
    <time_frame>Baseline through 24 months</time_frame>
    <description>Weight obtained from the electronic health record (for digital DPP comparison to usual care only)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>HbA1c (24-month for digital DPP)</measure>
    <time_frame>Basaeline through 24 months</time_frame>
    <description>HbA1c obtained from the electronic health record (for digital DPP comparison to usual care only)</description>
  </secondary_outcome>
  <other_outcome>
    <measure>Dietary Intake</measure>
    <time_frame>Baseline, 6 month follow-up, and 12 month follow-up</time_frame>
    <description>Starting the Conversation tool will be used to measure consumption of sugar sweetened beverages, fast food, fruits &amp; vegetables, and fat.</description>
  </other_outcome>
  <other_outcome>
    <measure>Depression</measure>
    <time_frame>Baseline, 6 month follow-up, and 12 month follow-up</time_frame>
    <description>PHQ-2 will be used to assess the frequency of depressed mood and anhedonia during the two weeks prior to the Baseline and 6 month follow-up assessment points.</description>
  </other_outcome>
  <other_outcome>
    <measure>Quality of Life</measure>
    <time_frame>Baseline, 6 month follow-up, and 12 month follow-up</time_frame>
    <description>SF36 Vitality subscale will be used to assess the presence of awareness and absence of fatigue.</description>
  </other_outcome>
  <other_outcome>
    <measure>Stress</measure>
    <time_frame>Baseline, 6 month follow-up, and 12 month follow-up</time_frame>
    <description>Perceived Stress Scale will be used to assess perceived global stress over the past month.</description>
  </other_outcome>
  <other_outcome>
    <measure>Social Support - Eating Habits</measure>
    <time_frame>Baseline, 6 month follow-up, and 12 month follow-up</time_frame>
    <description>Social Support and Eating Habits Survey will be used to assess perceived social support in this domain from family and friends.</description>
  </other_outcome>
  <other_outcome>
    <measure>Motivation for enrolling</measure>
    <time_frame>Baseline</time_frame>
    <description>Treatment Self-Regulation Questionnaire for Entering a Weight Loss Program will be used to measure motivation for enrolling in DPP.</description>
  </other_outcome>
  <other_outcome>
    <measure>PROMIS Global Health</measure>
    <time_frame>Baseline, 6 month follow-up, and 12 month follow-up</time_frame>
    <description>Measure of health-related quality of life</description>
  </other_outcome>
  <other_outcome>
    <measure>Social Support - Exercise Habits</measure>
    <time_frame>Baseline, 6 month follow-up, and 12 month follow-up</time_frame>
    <description>Social Support and Exercise Survey will be used to assess perceived social support in this domain from family and friends.</description>
  </other_outcome>
  <other_outcome>
    <measure>Motivation for continuing</measure>
    <time_frame>6 month follow-up</time_frame>
    <description>Treatment Self-Regulation Questionnaire for Continuing Program Participation will be used to measure motivation for continuing with DPP.</description>
  </other_outcome>
  <number_of_groups>3</number_of_groups>
  <enrollment type="Actual">8198</enrollment>
  <condition>Diabetes</condition>
  <arm_group>
    <arm_group_label>Digital DPP enrolled</arm_group_label>
    <description>The DPP online program is a CDC-certified translation of the DPP lifestyle intervention delivered in an online small group format of 10-15 participants.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>In-person DPP enrolled</arm_group_label>
    <description>In-person DPP participants will attend group sessions of ~20 participants in size at KPNW clinics. The group facilitator will use the CDC National DPP curriculum,</description>
  </arm_group>
  <arm_group>
    <arm_group_label>DPP not enrolled (usual care)</arm_group_label>
    <description>Access to usual care services without restrictions.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Digital DPP</intervention_name>
    <description>The online DPP program is 12 months in duration with 16 core sessions delivered over 16-26 weeks and 6 maintenance sessions delivered over 6 months.</description>
    <arm_group_label>Digital DPP enrolled</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>In-person DPP</intervention_name>
    <description>The in-person DPP program is 12 months and consists of weekly sessions for the first 6 months and monthly sessions for the remaining 6 months.</description>
    <arm_group_label>In-person DPP enrolled</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>DPP not enrolled</intervention_name>
    <description>Access to usual care services without restrictions.</description>
    <arm_group_label>DPP not enrolled (usual care)</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        KPNW adult members with prediabetes and obesity.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Age 19-75&#xD;
&#xD;
          2. BMI ≥ 30; and&#xD;
&#xD;
          3. HbA1c 5.7-6.4%.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        1) Diagnosis of diabetes prior to the study's recruitment efforts&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>19 Years</minimum_age>
    <maximum_age>75 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Stephanie L Fitzpatrick, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Kaiser Permanente</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Kaiser Permanente Center for Health Research</name>
      <address>
        <city>Portland</city>
        <state>Oregon</state>
        <zip>97227</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <link>
    <url>https://www.cdc.gov/diabetes/data/statistics/statistics-report.html</url>
    <description>Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. Centers for Disease Control and Prevention, U.S. Dept of Health and Human Services. Accessed 07/26/2019</description>
  </link>
  <link>
    <url>https://www.federalregister.gov/documents/2016/07/15/2016-16097/medicare-program-revisions-to-payment-policies-under-the-physician-fee-schedule-and-other-revisions</url>
    <description>Centers for Medicare &amp; Medicaid Services. Federal Register, Vol. 81, No. 136, July 15, 2016, Proposal Rules. Pages 46413-46418 Accessed 11/09/2016</description>
  </link>
  <link>
    <url>https://innovation.cms.gov/Files/reports/hcia-ymcadpp-evalrpt.pdf</url>
    <description>Hinnant L, Razi S, Lewis R, et al. Evaluation of the Health Care Innovation Awards: Community Resource Planning, Prevention, and Monitoring Annual Report 2015. Awardee-Level Findings: YMCA of the USA. Accessed 11/09/2016</description>
  </link>
  <reference>
    <citation>Gerstein HC, Santaguida P, Raina P, Morrison KM, Balion C, Hunt D, Yazdi H, Booker L. Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies. Diabetes Res Clin Pract. 2007 Dec;78(3):305-12. doi: 10.1016/j.diabres.2007.05.004. Epub 2007 Jun 29.</citation>
    <PMID>17601626</PMID>
  </reference>
  <reference>
    <citation>American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care. 2018 May;41(5):917-928. doi: 10.2337/dci18-0007. Epub 2018 Mar 22.</citation>
    <PMID>29567642</PMID>
  </reference>
  <reference>
    <citation>Ackermann RT, Kenrik Duru O, Albu JB, Schmittdiel JA, Soumerai SB, Wharam JF, Ali MK, Mangione CM, Gregg EW; NEXT-D Study Group. Evaluating diabetes health policies using natural experiments: the natural experiments for translation in diabetes study. Am J Prev Med. 2015 Jun;48(6):747-54. doi: 10.1016/j.amepre.2014.12.010.</citation>
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    <citation>Ali MK, Wharam F, Kenrik Duru O, Schmittdiel J, Ackermann RT, Albu J, Ross-Degnan D, Hunter CM, Mangione C, Gregg EW; NEXT-D Study Group. Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network. Curr Diab Rep. 2018 Nov 20;18(12):146. doi: 10.1007/s11892-018-1112-3.</citation>
    <PMID>30456479</PMID>
  </reference>
  <reference>
    <citation>Chambers EC, Rehm CD, Correra J, Garcia LE, Marquez ME, Wylie-Rosett J, Parsons A. Factors in Placement and Enrollment of Primary Care Patients in YMCA's Diabetes Prevention Program, Bronx, New York, 2010-2015. Prev Chronic Dis. 2017 Mar 30;14:E28. doi: 10.5888/pcd14.160486.</citation>
    <PMID>28358669</PMID>
  </reference>
  <reference>
    <citation>Venkataramani M, Pollack CE, Yeh HC, Maruthur NM. Prevalence and Correlates of Diabetes Prevention Program Referral and Participation. Am J Prev Med. 2019 Mar;56(3):452-457. doi: 10.1016/j.amepre.2018.10.005. Epub 2019 Jan 17.</citation>
    <PMID>30661888</PMID>
  </reference>
  <reference>
    <citation>Ali MK, McKeever Bullard K, Imperatore G, Benoit SR, Rolka DB, Albright AL, Gregg EW. Reach and Use of Diabetes Prevention Services in the United States, 2016-2017. JAMA Netw Open. 2019 May 3;2(5):e193160. doi: 10.1001/jamanetworkopen.2019.3160.</citation>
    <PMID>31074808</PMID>
  </reference>
  <reference>
    <citation>Ackermann RT, O'Brien MJ. Evidence and Challenges for Translation and Population Impact of the Diabetes Prevention Program. Curr Diab Rep. 2020 Feb 20;20(3):9. doi: 10.1007/s11892-020-1293-4.</citation>
    <PMID>32080770</PMID>
  </reference>
  <reference>
    <citation>Moin T, Damschroder LJ, AuYoung M, Maciejewski ML, Havens K, Ertl K, Vasti E, Weinreb JE, Steinle NI, Billington CJ, Hughes M, Makki F, Youles B, Holleman RG, Kim HM, Kinsinger LS, Richardson CR. Results From a Trial of an Online Diabetes Prevention Program Intervention. Am J Prev Med. 2018 Nov;55(5):583-591. doi: 10.1016/j.amepre.2018.06.028. Epub 2018 Sep 24.</citation>
    <PMID>30262149</PMID>
  </reference>
  <reference>
    <citation>Sepah SC, Jiang L, Ellis RJ, McDermott K, Peters AL. Engagement and outcomes in a digital Diabetes Prevention Program: 3-year update. BMJ Open Diabetes Res Care. 2017 Sep 7;5(1):e000422. doi: 10.1136/bmjdrc-2017-000422. eCollection 2017.</citation>
    <PMID>28948027</PMID>
  </reference>
  <reference>
    <citation>Sepah SC, Jiang L, Peters AL. Long-term outcomes of a Web-based diabetes prevention program: 2-year results of a single-arm longitudinal study. J Med Internet Res. 2015 Apr 10;17(4):e92. doi: 10.2196/jmir.4052.</citation>
    <PMID>25863515</PMID>
  </reference>
  <reference>
    <citation>Kim SE, Castro Sweet CM, Cho E, Tsai J, Cousineau MR. Evaluation of a Digital Diabetes Prevention Program Adapted for Low-Income Patients, 2016-2018. Prev Chronic Dis. 2019 Nov 27;16:E155. doi: 10.5888/pcd16.190156.</citation>
    <PMID>31775010</PMID>
  </reference>
  <reference>
    <citation>Castro Sweet CM, Chiguluri V, Gumpina R, Abbott P, Madero EN, Payne M, Happe L, Matanich R, Renda A, Prewitt T. Outcomes of a Digital Health Program With Human Coaching for Diabetes Risk Reduction in a Medicare Population. J Aging Health. 2018 Jun;30(5):692-710. doi: 10.1177/0898264316688791. Epub 2017 Jan 24.</citation>
    <PMID>28553807</PMID>
  </reference>
  <reference>
    <citation>Chen F, Su W, Becker SH, Payne M, Castro Sweet CM, Peters AL, Dall TM. Clinical and Economic Impact of a Digital, Remotely-Delivered Intensive Behavioral Counseling Program on Medicare Beneficiaries at Risk for Diabetes and Cardiovascular Disease. PLoS One. 2016 Oct 5;11(10):e0163627. doi: 10.1371/journal.pone.0163627. eCollection 2016.</citation>
    <PMID>27706216</PMID>
  </reference>
  <reference>
    <citation>Lee PG, Damschroder LJ, Holleman R, Moin T, Richardson CR. Older Adults and Diabetes Prevention Programs in the Veterans Health Administration. Diabetes Care. 2018 Dec;41(12):2644-2647. doi: 10.2337/dc18-1141. Epub 2018 Oct 30.</citation>
    <PMID>30377187</PMID>
  </reference>
  <reference>
    <citation>Joiner KL, Nam S, Whittemore R. Lifestyle interventions based on the diabetes prevention program delivered via eHealth: A systematic review and meta-analysis. Prev Med. 2017 Jul;100:194-207. doi: 10.1016/j.ypmed.2017.04.033. Epub 2017 Apr 27.</citation>
    <PMID>28456513</PMID>
  </reference>
  <reference>
    <citation>Sanders GD, Neumann PJ, Basu A, Brock DW, Feeny D, Krahn M, Kuntz KM, Meltzer DO, Owens DK, Prosser LA, Salomon JA, Sculpher MJ, Trikalinos TA, Russell LB, Siegel JE, Ganiats TG. Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA. 2016 Sep 13;316(10):1093-103. doi: 10.1001/jama.2016.12195. Erratum In: JAMA. 2016 Nov 8;316(18):1924.</citation>
    <PMID>27623463</PMID>
  </reference>
  <reference>
    <citation>Diabetes Prevention Program Research Group. Within-trial cost-effectiveness of lifestyle intervention or metformin for the primary prevention of type 2 diabetes. Diabetes Care. 2003 Sep;26(9):2518-23. doi: 10.2337/diacare.26.9.2518.</citation>
    <PMID>12941712</PMID>
  </reference>
  <reference>
    <citation>Diabetes Prevention Program Research Group. The 10-year cost-effectiveness of lifestyle intervention or metformin for diabetes prevention: an intent-to-treat analysis of the DPP/DPPOS. Diabetes Care. 2012 Apr;35(4):723-30. doi: 10.2337/dc11-1468. Erratum In: Diabetes Care. 2013 Dec;36(12):4173-5.</citation>
    <PMID>22442395</PMID>
  </reference>
  <reference>
    <citation>Herman WH. The cost-effectiveness of diabetes prevention: results from the Diabetes Prevention Program and the Diabetes Prevention Program Outcomes Study. Clin Diabetes Endocrinol. 2015 Sep 2;1:9. doi: 10.1186/s40842-015-0009-1. eCollection 2015.</citation>
    <PMID>28702228</PMID>
  </reference>
  <reference>
    <citation>Hoerger TJ, Hicks KA, Sorensen SW, Herman WH, Ratner RE, Ackermann RT, Zhang P, Engelgau MM. Cost-effectiveness of screening for pre-diabetes among overweight and obese U.S. adults. Diabetes Care. 2007 Nov;30(11):2874-9. doi: 10.2337/dc07-0885. Epub 2007 Aug 13.</citation>
    <PMID>17698614</PMID>
  </reference>
  <reference>
    <citation>Krukowski RA, Pope RA, Love S, Lensing S, Felix HC, Prewitt TE, West D. Examination of costs for a lay health educator-delivered translation of the Diabetes Prevention Program in senior centers. Prev Med. 2013 Oct;57(4):400-2. doi: 10.1016/j.ypmed.2013.06.027. Epub 2013 Jul 2.</citation>
    <PMID>23831492</PMID>
  </reference>
  <reference>
    <citation>Zhou X, Siegel KR, Ng BP, Jawanda S, Proia KK, Zhang X, Albright AL, Zhang P. Cost-effectiveness of Diabetes Prevention Interventions Targeting High-risk Individuals and Whole Populations: A Systematic Review. Diabetes Care. 2020 Jul;43(7):1593-1616. doi: 10.2337/dci20-0018.</citation>
    <PMID>33534726</PMID>
  </reference>
  <verification_date>June 2022</verification_date>
  <study_first_submitted>August 8, 2017</study_first_submitted>
  <study_first_submitted_qc>August 10, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 15, 2017</study_first_posted>
  <last_update_submitted>June 29, 2022</last_update_submitted>
  <last_update_submitted_qc>June 29, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">June 30, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Prevention</keyword>
  <keyword>Medicare</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Diabetes Mellitus</mesh_term>
  </condition_browse>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03249077
org study id: 1R01DK115237
nct id: NCT03249077
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
In preparation for the roll out of the Medicare Diabetes Prevention Program (DPP) in 2018,
      Kaiser Permanente Northwest (KPNW), a large, integrated health care system, plans to pilot
      the implementation of DPP starting in April 2017. Patients 19-75 years old at high risk for
      diabetes will be offered DPP online or DPP in-person. A pragmatic, rigorous, quantitative and
      qualitative evaluation will be conducted to compare patients enrolled in DPP (either online
      or in-person) to those not enrolled to better inform future implementation efforts of DPP
      within and outside of KPNW. This study is a natural experiment project.
The prevalence of prediabetes and obesity among U.S. adults age 40 and older is significant,
      with over 30% having prediabetes and over 40% having obesity.[1,2] Prediabetes and obesity
      increase the risk for diabetes, cardiovascular disease, and poor quality of life, and are
      responsible for substantial healthcare costs.[3] In response to the multi-level burden of
      prediabetes and obesity, there have been several efforts to prevent diabetes at the
      population level and reduce healthcare costs,[4,5] including national implementation and
      reimbursement of the successful Diabetes Prevention Program (DPP).

      Beginning in April 2018, the Centers for Medicare & Medicaid Services (CMS) made a landmark
      decision to reimburse clinical and non-clinical settings for providing DPP to Medicare
      beneficiaries (i.e., Medicare DPP); this coverage is currently for in-person DPP only and not
      digital DPP.[6,7] CMS's decision to cover DPP among older adults with prediabetes further
      catapulted efforts within healthcare organizations to address the increasing number of
      individuals with diabetes receiving care in their facilities. However, few studies have
      examined the sustainability of providing DPP based on maintenance of the effect (i.e.,
      long-term change in weight and HbA1c), healthcare costs, participant experience, and
      organizational support. In addition, attracting individuals to DPP and similar lifestyle
      change interventions remains a significant challenge and identifying useful approaches is
      important.[8-11] Lastly, whereas the effectiveness of in-person DPP is well-established,
      prior studies evaluating the effect of digital DPP identified positive outcomes but had
      significant methodological limitations, such as a single arm pre- / post-test design and
      participant-reported outcomes.[12,13-19]

      In 2017 Kaiser Permanente Northwest (KPNW), a large, integrated health system serving Oregon
      and southwest Washington, began piloting both digital and in-person versions of DPP for its
      adult health plan members with prediabetes and obesity. The purpose of this mixed-methods,
      natural experiment is to evaluate this large health system initiative by assessing the
      effects of both digital and in-person DPP on change in weight and HbA1c, health behaviors,
      and psychosocial factors. Also, sustainability based on cost-effectiveness and patient and
      healthcare stakeholder perspectives will be examined.

      KPNW patients eligible to participate in DPP (digital or in-person) will be identified and
      recruited using the electronic health record (EHR). Both the digital and in-person
      (group-based) DPP programs will be delivered over 12 months. Demographic and clinical data to
      be included in analyses will also be extracted from the EHR. Behavioral and psychosocial
      questionnaires will be administered to DPP enrollees and non-enrollees online using REDCap.
      Semi-structured qualitative interviews will be conducted with a subset of DPP enrollees and
      non-enrollees to understand reasons for enrollment and likes/dislikes about the program.
      Healthcare system providers and stakeholders will also be interviewed to capture factors
      related to sustainability of offering DPP within the health system.

      For the primary analysis, investigators plan to model 12- and 24-month weight and HbA1c
      trajectories using a linear mixed effects model using time since baseline as the time axis.
      Because randomization is not feasible in this real-world implementation of DPP, propensity
      score adjustment will be used to control for potential confounding. Furthermore,
      investigators will conduct an economic evaluation over the 12-month follow-up period for both
      the digital and in-person DPP cohorts as well as over the 24-month period for the digital DPP
      cohort from the perspective of the health plan, following best practices,[20] and guided by
      previous economic analyses of DPP interventions.[21-26]

      The mixed-methods, natural experiment design investigators will use to evaluate KPNW's
      implementation of digital and in-person DPP will build on existing evidence related to DPP
      effectiveness across the two delivery modes on change in weight and HbA1c over time. In
      addition, the cost-effectiveness analysis will determine the impact of digital and in-person
      DPP on return on investment for healthcare systems and sustainability of the program.
      Findings from our evaluation will therefore inform best practices for implementing and
      sustaining DPP within large healthcare systems.
observational model: Cohort
time perspective: Prospective
measure: Weight
time frame: Baseline through 12 months
description: Weight obtained from the electronic health record
measure: HbA1c
time frame: Baseline through 12 months
description: HbA1c obtained from the electronic health record
measure: Cost-effectiveness
time frame: Baseline through 12 months; and Baseline through 24 months (for digital DPP comparison to usual care only).
description: Cost data will include: 1) medical care; and 2) the cost of intervention delivery, obtained from administrative and electronic health records.
measure: Weight (24-month for digital DPP)
time frame: Baseline through 24 months
description: Weight obtained from the electronic health record (for digital DPP comparison to usual care only)
measure: HbA1c (24-month for digital DPP)
time frame: Basaeline through 24 months
description: HbA1c obtained from the electronic health record (for digital DPP comparison to usual care only)
measure: Dietary Intake
time frame: Baseline, 6 month follow-up, and 12 month follow-up
description: Starting the Conversation tool will be used to measure consumption of sugar sweetened beverages, fast food, fruits & vegetables, and fat.
measure: Depression
time frame: Baseline, 6 month follow-up, and 12 month follow-up
description: PHQ-2 will be used to assess the frequency of depressed mood and anhedonia during the two weeks prior to the Baseline and 6 month follow-up assessment points.
measure: Quality of Life
time frame: Baseline, 6 month follow-up, and 12 month follow-up
description: SF36 Vitality subscale will be used to assess the presence of awareness and absence of fatigue.
measure: Stress
time frame: Baseline, 6 month follow-up, and 12 month follow-up
description: Perceived Stress Scale will be used to assess perceived global stress over the past month.
measure: Social Support - Eating Habits
time frame: Baseline, 6 month follow-up, and 12 month follow-up
description: Social Support and Eating Habits Survey will be used to assess perceived social support in this domain from family and friends.
measure: Motivation for enrolling
time frame: Baseline
description: Treatment Self-Regulation Questionnaire for Entering a Weight Loss Program will be used to measure motivation for enrolling in DPP.
measure: PROMIS Global Health
time frame: Baseline, 6 month follow-up, and 12 month follow-up
description: Measure of health-related quality of life
measure: Social Support - Exercise Habits
time frame: Baseline, 6 month follow-up, and 12 month follow-up
description: Social Support and Exercise Survey will be used to assess perceived social support in this domain from family and friends.
measure: Motivation for continuing
time frame: 6 month follow-up
description: Treatment Self-Regulation Questionnaire for Continuing Program Participation will be used to measure motivation for continuing with DPP.
arm group label: Digital DPP enrolled
description: The DPP online program is a CDC-certified translation of the DPP lifestyle intervention delivered in an online small group format of 10-15 participants.
arm group label: In-person DPP enrolled
description: In-person DPP participants will attend group sessions of ~20 participants in size at KPNW clinics. The group facilitator will use the CDC National DPP curriculum,
arm group label: DPP not enrolled (usual care)
description: Access to usual care services without restrictions.
intervention type: Behavioral
intervention name: Digital DPP
description: The online DPP program is 12 months in duration with 16 core sessions delivered over 16-26 weeks and 6 maintenance sessions delivered over 6 months.
arm group label: Digital DPP enrolled
intervention type: Behavioral
intervention name: In-person DPP
description: The in-person DPP program is 12 months and consists of weekly sessions for the first 6 months and monthly sessions for the remaining 6 months.
arm group label: In-person DPP enrolled
intervention type: Other
intervention name: DPP not enrolled
description: Access to usual care services without restrictions.
arm group label: DPP not enrolled (usual care)
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 19 Years
maximum age: 75 Years
healthy volunteers: No
last name: Stephanie L Fitzpatrick, PhD
role: Principal Investigator
affiliation: Kaiser Permanente
facility: 
country: United States
url: https://www.cdc.gov/diabetes/data/statistics/statistics-report.html
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responsible party type: Sponsor
mesh term: Diabetes Mellitus