<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/NCT03800680</url>
  </required_header>
  <id_info>
    <org_study_id>HSRGDB16Dec003</org_study_id>
    <nct_id>NCT03800680</nct_id>
  </id_info>
  <brief_title>A Randomized Trial to Slow the Progression of Diabetes</brief_title>
  <acronym>TRIPOD</acronym>
  <official_title>A Randomized Trial to Slow the Progression of Diabetes (The TRIPOD Study)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Duke-NUS Graduate Medical School</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>National Medical Research Council (NMRC), Singapore</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>SingHealth Polyclinics</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Singapore General Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Changi General Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Duke-NUS Graduate Medical School</source>
  <oversight_info>
    <has_dmc>Yes</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>
      The research objective of this one-year study is to test whether an evidence-based, low-cost&#xD;
      mobile diabetes management package (DMP), with or without an incentive program grounded in&#xD;
      economic theory (M-POWER Rewards), can effectively and cost-effectively improve health&#xD;
      outcomes for adults with type 2 diabetes.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Technological and economic advancement have created a major challenge to public health&#xD;
      agencies in Singapore and other first world countries: the challenge being how to get&#xD;
      individuals to maintain a healthy lifestyle when that is no longer a requirement for economic&#xD;
      prosperity (and in fact may be a hindrance). What is required is a low cost strategy that&#xD;
      effectively manages risk factors for chronic disease without overwhelming the public&#xD;
      healthcare infrastructure. Although technology and economic advancement are clearly part of&#xD;
      the problem, they may also be part of the solution.&#xD;
&#xD;
      For patients with type 2 diabetes mellitus (T2DM), lifestyle modification can be highly&#xD;
      effective at stemming the progression of the disease. Effective interventions include modules&#xD;
      that teach appropriate strategies for weight management, increased physical activity, better&#xD;
      diet, routine glucose monitoring, and strict compliance to prescribed diabetes medications.&#xD;
      These components can now be effectively delivered electronically. A recent review and&#xD;
      meta-analysis of 13 smartphone applications for diabetes management found that these&#xD;
      applications offered modest benefits, with a mean difference in HbA1c of -0.40%.&#xD;
&#xD;
      Because of the high costs involved in treating people with chronic conditions, employers,&#xD;
      insurers, and governments all have a financial incentive to contain the chronic disease&#xD;
      epidemic. Therefore, each has shown a willingness to invest in some level of prevention and&#xD;
      treatment efforts. It is our contention that contingent rewards may be necessary to help&#xD;
      people overcome their preferences for current over future consumption, often called present&#xD;
      bias. Behavioral economists recommend that, to overcome present bias, rewards should be tied&#xD;
      to both short-term and long-term outcomes or behaviors, such as monitoring blood glucose and&#xD;
      taking medications as prescribed (short term) and pre-defined outcomes such as achieving a&#xD;
      target HbA1c level over a specified duration (long term). Based on a recent systematic review&#xD;
      of incentive studies conducted by members of our team, an optimal rewards strategy has the&#xD;
      potential to greatly increase the effectiveness of existing mobile diabetes applications. If&#xD;
      shown to be effective and cost effective, the investigators also believe payers will&#xD;
      subsidize such a strategy.&#xD;
&#xD;
      Specifically, the investigators propose to conduct a 52-week, three-arm randomized controlled&#xD;
      trial to evaluate whether an evidence-based, low-cost mobile diabetes management package&#xD;
      (DMP), with or without an incentive program grounded in economic theory, can effectively and&#xD;
      cost-effectively improve outcomes for adults with diabetes. The control arm (Arm 1) will&#xD;
      receive usual care, whereas participants in the intervention arms will receive the DMP alone&#xD;
      (Arm 2) or the DMP with the M-POWER Rewards incentive program (Arm 3) in addition to their&#xD;
      usual care. The investigators hypothesize that participants in Arms 2 and 3 will show&#xD;
      improved glycemic control, as measured by HbA1c levels, at the Month 12 primary endpoint&#xD;
      compared to participants in the Arm 1 control group. The investigators also hypothesize that&#xD;
      Arm 3 will have improved HbA1c levels compared to Arm 2 at Month 12. Similar hypotheses will&#xD;
      be tested for secondary outcomes measured at Month 6.&#xD;
&#xD;
      In addition, the investigators will quantify the incremental cost-effectiveness of DMP with&#xD;
      M-POWER Rewards and the net cost implications of both from a third party payer's perspective.&#xD;
      The investigators hypothesize that despite its higher implementation cost, effectiveness will&#xD;
      be greater and net cost will be lower for Arm 3 relative to the Arm 1 control group due to&#xD;
      the reduction in medical expenditures that result from improved glycaemic control.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">October 19, 2019</start_date>
  <completion_date type="Actual">June 11, 2023</completion_date>
  <primary_completion_date type="Actual">June 8, 2023</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Change in HbA1c levels at Month 12</measure>
    <time_frame>Baseline, Month 12</time_frame>
    <description>HbA1c or glycated hemoglobin is a measure of blood glucose levels. HbA1c blood tests will be conducted at baseline and Month 12 and the difference will be calculated.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Change in HbA1c levels at Month 6</measure>
    <time_frame>Baseline, Month 6</time_frame>
    <description>HbA1c or glycated hemoglobin is a measure of blood glucose levels. HbA1c blood tests will be conducted at baseline and Month 6 and the difference between the follow-up assessment (Months 6) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in weight at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Weight will be measured at baseline, Month 6, and Month 12. The difference in weight between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in blood pressure at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Blood pressure will be measured at baseline, Month 6, and Month 12. The difference in blood pressure between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Proportion of participants that had insulin treatment initiated by Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Percentage of participants that had insulin treatment initiated by their diabetes care physician by each follow-up assessment (Months 6 and 12).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported physical activity at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported physical activity data will be collected at baseline, Month 6, and Month 12 via the Global Physical Activity Questionnaire (GPAQ). The difference in self-reported physical activity between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported weight monitoring at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported weight monitoring data will be collected at baseline, Month 6, and Month 12. The difference in self-reported weight monitoring between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported blood glucose monitoring at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported blood glucose monitoring data will be collected at baseline, Month 6, and Month 12. The difference in self-reported blood glucose monitoring between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported medication adherence at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported medication adherence data will be collected at baseline, Month 6, and Month 12. The difference in self-reported medication adherence between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported diabetes self-management at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported diabetes self-management data will be collected at baseline, Month 6, and Month 12 via the Diabetes Self-Management Questionnaire (DSMQ). The difference in self-reported diabetes self-management between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported sleep quality at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported sleep quality data will be collected at baseline, Month 6, and Month 12 via the Pittsburgh Sleep Quality Index (PSQI). The difference in self-reported sleep quality between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported work productivity and daily activity impairment at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported work productivity and daily activity impairment data will be collected at baseline, Month 6, and Month 12 via a modified Work Productivity and Activity Impairment: Specific Health Problem instrument (WPAI:SHP). The difference in self-reported work productivity and daily activity impairment between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in self-reported health utility index at Months 6 and 12</measure>
    <time_frame>Baseline, Month 6, Month 12</time_frame>
    <description>Self-reported health utility index data will be collected at baseline, Month 6, and Month 12 via the EQ-5D-5L questionnaire. The difference in self-reported health utility index between each follow-up assessment (Months 6 and 12) and baseline will be calculated.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Incremental cost-effectiveness ratios based on HbA1c at Month 12</measure>
    <time_frame>Baseline, Month 12</time_frame>
    <description>Incremental cost-effectiveness ratios will be determined by calculating the incremental cost per unit reduction in HbA1c at Month 12 (primary endpoint) compared to baseline.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Incremental cost-effectiveness ratios based on QALY at Month 12</measure>
    <time_frame>Baseline, Month 12</time_frame>
    <description>Incremental cost-effectiveness ratios will be determined by calculating the incremental cost per quality adjusted life year (QALY) gained at Month 12 (primary endpoint) compared to baseline.</description>
  </secondary_outcome>
  <number_of_arms>3</number_of_arms>
  <enrollment type="Actual">269</enrollment>
  <condition>Diabetes Mellitus, Type 2</condition>
  <arm_group>
    <arm_group_label>Usual Care (Arm 1)</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Participants will receive usual care by their diabetes clinics.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>DMP (Arm 2)</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Participants will receive usual care by their diabetes clinics and the Diabetes Management Package (DMP).</description>
  </arm_group>
  <arm_group>
    <arm_group_label>DMP + M-POWER Rewards (Arm 3)</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Participants will receive usual care by their diabetes clinics, the Diabetes Management Package (DMP), and the financial incentive program, M-POWER Rewards.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Diabetes Management Package (DMP)</intervention_name>
    <description>M-POWER app: A one-stop portal to monitor diabetes self-management activities and progress. The app syncs and displays relevant data from study devices and apps.&#xD;
GlycoLeap: A 24-week, digitally-delivered education and behavior change program for T2DM patients.&#xD;
4 study devices with accompanying apps to aid in diabetes self-management: weighing scale, pedometer, glucometer, and pill tracker.&#xD;
Recommended activities:&#xD;
Complete all 24 GlycoLeap lessons and quizzes&#xD;
Weight Monitoring: Weigh at least once a week.&#xD;
Physical Activity: At least 150 minutes of moderate-to-vigorous activity per week, targeting at least 420 Fitbit active minutes per week.&#xD;
Blood Glucose Monitoring: At least three post-meal measurements within 4.0-10.0 mmol/L per week, with each reading taken on different days.&#xD;
Medication: Take medications as prescribed.</description>
    <arm_group_label>DMP (Arm 2)</arm_group_label>
    <arm_group_label>DMP + M-POWER Rewards (Arm 3)</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>M-POWER Rewards</intervention_name>
    <description>A financial incentive program were participants can earn up to 516 M-Points (1 M-Point is equivalent to S$1) over the one-year study period for performing specific activities according to recommendations that are meant to improve glycemic control and for achieving HbA1c and weight loss goals. M-Points can be reimbursed in the form of financial rebates for approved non-inpatient healthcare-related expenses incurred during the study period.</description>
    <arm_group_label>DMP + M-POWER Rewards (Arm 3)</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        All eligibility criteria will be self-declared.&#xD;
&#xD;
        Inclusion Criteria:&#xD;
&#xD;
          1. Diagnosed with T2DM with suboptimal diabetes control as defined by a HbA1c level of&#xD;
             between 7.5% and 11.0% (inclusive) at their most recent test taken within the past six&#xD;
             calendar months. This HbA1c inclusion criterion will be based on the patients'&#xD;
             self-reported HbA1c levels and test dates.&#xD;
&#xD;
          2. Not on insulin.&#xD;
&#xD;
          3. On at least one oral glucose-lowering drug.&#xD;
&#xD;
          4. Singapore Citizen or Permanent Resident with no plans to relocate during the study&#xD;
             period.&#xD;
&#xD;
          5. Able to read, write, and communicate in English.&#xD;
&#xD;
          6. Own a personal smartphone and be able to use it.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Pregnant or lactating.&#xD;
&#xD;
          2. Diagnosed with chronic kidney disease (stage 3B with eGFR &lt;45mL/min) or undergoing&#xD;
             dialysis for end-stage kidney failure.&#xD;
&#xD;
          3. Diagnosed with liver cirrhosis.&#xD;
&#xD;
          4. Diagnosed with cancer that required treatment in the past five years.&#xD;
&#xD;
          5. Diagnosed with heart attack (i.e., acute myocardial infarction) within the past one&#xD;
             year.&#xD;
&#xD;
          6. Diagnosed with heart failure (i.e., congestive heart failure)&#xD;
&#xD;
          7. Diagnosed with stroke or transient ischemic attacks.&#xD;
&#xD;
          8. Undergone whole blood or red blood cell transfusion within the past three months.&#xD;
&#xD;
          9. Diagnosed with severe anaemia (Haemoglobin &lt;10g/dL)&#xD;
&#xD;
         10. Diagnosed with sickle-cell disease&#xD;
&#xD;
         11. Diagnosed with Thalassemia major&#xD;
&#xD;
         12. Undergone bariatric surgery or extensive bowel resection.&#xD;
&#xD;
         13. Undergone lower limb amputation (including toe amputation).&#xD;
&#xD;
         14. Taking systemic corticosteroids (including Traditional Chinese or Malay medicine).&#xD;
&#xD;
         15. Currently on doctor's advice against engaging in moderate-to-vigorous physical&#xD;
             activity (i.e., brisk walking or more intense).&#xD;
&#xD;
         16. Currently have a condition(s) that restricts engaging in moderate-to-vigorous physical&#xD;
             activity (i.e., brisk walking or more intense).&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>21 Years</minimum_age>
    <maximum_age>70 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Eric A Finkelstein, PhD, MHA</last_name>
    <role>Principal Investigator</role>
    <affiliation>Duke-NUS Graduate Medical School</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Duke-NUS Medical School</name>
      <address>
        <city>Singapore</city>
        <zip>169857</zip>
        <country>Singapore</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Singapore</country>
  </location_countries>
  <verification_date>June 2023</verification_date>
  <study_first_submitted>January 9, 2019</study_first_submitted>
  <study_first_submitted_qc>January 9, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">January 11, 2019</study_first_posted>
  <last_update_submitted>June 21, 2023</last_update_submitted>
  <last_update_submitted_qc>June 21, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">June 22, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Duke-NUS Graduate Medical School</investigator_affiliation>
    <investigator_full_name>Eric A. Finkelstein</investigator_full_name>
    <investigator_title>Professor</investigator_title>
  </responsible_party>
  <keyword>Diabetes</keyword>
  <keyword>Smartphone application</keyword>
  <keyword>Behavior change</keyword>
  <keyword>Weight monitoring</keyword>
  <keyword>Physical activity</keyword>
  <keyword>Blood glucose monitoring</keyword>
  <keyword>Medication adherence</keyword>
  <keyword>Financial incentive</keyword>
  <keyword>Cost-effectiveness analysis</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Diabetes Mellitus</mesh_term>
    <mesh_term>Diabetes Mellitus, Type 2</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/NCT03800680
org study id: HSRGDB16Dec003
nct id: NCT03800680
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The research objective of this one-year study is to test whether an evidence-based, low-cost
      mobile diabetes management package (DMP), with or without an incentive program grounded in
      economic theory (M-POWER Rewards), can effectively and cost-effectively improve health
      outcomes for adults with type 2 diabetes.
Technological and economic advancement have created a major challenge to public health
      agencies in Singapore and other first world countries: the challenge being how to get
      individuals to maintain a healthy lifestyle when that is no longer a requirement for economic
      prosperity (and in fact may be a hindrance). What is required is a low cost strategy that
      effectively manages risk factors for chronic disease without overwhelming the public
      healthcare infrastructure. Although technology and economic advancement are clearly part of
      the problem, they may also be part of the solution.

      For patients with type 2 diabetes mellitus (T2DM), lifestyle modification can be highly
      effective at stemming the progression of the disease. Effective interventions include modules
      that teach appropriate strategies for weight management, increased physical activity, better
      diet, routine glucose monitoring, and strict compliance to prescribed diabetes medications.
      These components can now be effectively delivered electronically. A recent review and
      meta-analysis of 13 smartphone applications for diabetes management found that these
      applications offered modest benefits, with a mean difference in HbA1c of -0.40%.

      Because of the high costs involved in treating people with chronic conditions, employers,
      insurers, and governments all have a financial incentive to contain the chronic disease
      epidemic. Therefore, each has shown a willingness to invest in some level of prevention and
      treatment efforts. It is our contention that contingent rewards may be necessary to help
      people overcome their preferences for current over future consumption, often called present
      bias. Behavioral economists recommend that, to overcome present bias, rewards should be tied
      to both short-term and long-term outcomes or behaviors, such as monitoring blood glucose and
      taking medications as prescribed (short term) and pre-defined outcomes such as achieving a
      target HbA1c level over a specified duration (long term). Based on a recent systematic review
      of incentive studies conducted by members of our team, an optimal rewards strategy has the
      potential to greatly increase the effectiveness of existing mobile diabetes applications. If
      shown to be effective and cost effective, the investigators also believe payers will
      subsidize such a strategy.

      Specifically, the investigators propose to conduct a 52-week, three-arm randomized controlled
      trial to evaluate whether an evidence-based, low-cost mobile diabetes management package
      (DMP), with or without an incentive program grounded in economic theory, can effectively and
      cost-effectively improve outcomes for adults with diabetes. The control arm (Arm 1) will
      receive usual care, whereas participants in the intervention arms will receive the DMP alone
      (Arm 2) or the DMP with the M-POWER Rewards incentive program (Arm 3) in addition to their
      usual care. The investigators hypothesize that participants in Arms 2 and 3 will show
      improved glycemic control, as measured by HbA1c levels, at the Month 12 primary endpoint
      compared to participants in the Arm 1 control group. The investigators also hypothesize that
      Arm 3 will have improved HbA1c levels compared to Arm 2 at Month 12. Similar hypotheses will
      be tested for secondary outcomes measured at Month 6.

      In addition, the investigators will quantify the incremental cost-effectiveness of DMP with
      M-POWER Rewards and the net cost implications of both from a third party payer's perspective.
      The investigators hypothesize that despite its higher implementation cost, effectiveness will
      be greater and net cost will be lower for Arm 3 relative to the Arm 1 control group due to
      the reduction in medical expenditures that result from improved glycaemic control.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Health Services Research
masking: None (Open Label)
measure: Change in HbA1c levels at Month 12
time frame: Baseline, Month 12
description: HbA1c or glycated hemoglobin is a measure of blood glucose levels. HbA1c blood tests will be conducted at baseline and Month 12 and the difference will be calculated.
measure: Change in HbA1c levels at Month 6
time frame: Baseline, Month 6
description: HbA1c or glycated hemoglobin is a measure of blood glucose levels. HbA1c blood tests will be conducted at baseline and Month 6 and the difference between the follow-up assessment (Months 6) and baseline will be calculated.
measure: Change in weight at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Weight will be measured at baseline, Month 6, and Month 12. The difference in weight between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in blood pressure at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Blood pressure will be measured at baseline, Month 6, and Month 12. The difference in blood pressure between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Proportion of participants that had insulin treatment initiated by Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Percentage of participants that had insulin treatment initiated by their diabetes care physician by each follow-up assessment (Months 6 and 12).
measure: Change in self-reported physical activity at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported physical activity data will be collected at baseline, Month 6, and Month 12 via the Global Physical Activity Questionnaire (GPAQ). The difference in self-reported physical activity between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in self-reported weight monitoring at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported weight monitoring data will be collected at baseline, Month 6, and Month 12. The difference in self-reported weight monitoring between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in self-reported blood glucose monitoring at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported blood glucose monitoring data will be collected at baseline, Month 6, and Month 12. The difference in self-reported blood glucose monitoring between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in self-reported medication adherence at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported medication adherence data will be collected at baseline, Month 6, and Month 12. The difference in self-reported medication adherence between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in self-reported diabetes self-management at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported diabetes self-management data will be collected at baseline, Month 6, and Month 12 via the Diabetes Self-Management Questionnaire (DSMQ). The difference in self-reported diabetes self-management between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in self-reported sleep quality at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported sleep quality data will be collected at baseline, Month 6, and Month 12 via the Pittsburgh Sleep Quality Index (PSQI). The difference in self-reported sleep quality between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in self-reported work productivity and daily activity impairment at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported work productivity and daily activity impairment data will be collected at baseline, Month 6, and Month 12 via a modified Work Productivity and Activity Impairment: Specific Health Problem instrument (WPAI:SHP). The difference in self-reported work productivity and daily activity impairment between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Change in self-reported health utility index at Months 6 and 12
time frame: Baseline, Month 6, Month 12
description: Self-reported health utility index data will be collected at baseline, Month 6, and Month 12 via the EQ-5D-5L questionnaire. The difference in self-reported health utility index between each follow-up assessment (Months 6 and 12) and baseline will be calculated.
measure: Incremental cost-effectiveness ratios based on HbA1c at Month 12
time frame: Baseline, Month 12
description: Incremental cost-effectiveness ratios will be determined by calculating the incremental cost per unit reduction in HbA1c at Month 12 (primary endpoint) compared to baseline.
measure: Incremental cost-effectiveness ratios based on QALY at Month 12
time frame: Baseline, Month 12
description: Incremental cost-effectiveness ratios will be determined by calculating the incremental cost per quality adjusted life year (QALY) gained at Month 12 (primary endpoint) compared to baseline.
arm group label: Usual Care (Arm 1)
arm group type: No Intervention
description: Participants will receive usual care by their diabetes clinics.
arm group label: DMP (Arm 2)
arm group type: Experimental
description: Participants will receive usual care by their diabetes clinics and the Diabetes Management Package (DMP).
arm group label: DMP + M-POWER Rewards (Arm 3)
arm group type: Experimental
description: Participants will receive usual care by their diabetes clinics, the Diabetes Management Package (DMP), and the financial incentive program, M-POWER Rewards.
intervention type: Behavioral
intervention name: Diabetes Management Package (DMP)
description: M-POWER app: A one-stop portal to monitor diabetes self-management activities and progress. The app syncs and displays relevant data from study devices and apps.
GlycoLeap: A 24-week, digitally-delivered education and behavior change program for T2DM patients.
4 study devices with accompanying apps to aid in diabetes self-management: weighing scale, pedometer, glucometer, and pill tracker.
Recommended activities:
Complete all 24 GlycoLeap lessons and quizzes
Weight Monitoring: Weigh at least once a week.
Physical Activity: At least 150 minutes of moderate-to-vigorous activity per week, targeting at least 420 Fitbit active minutes per week.
Blood Glucose Monitoring: At least three post-meal measurements within 4.0-10.0 mmol/L per week, with each reading taken on different days.
Medication: Take medications as prescribed.
arm group label: DMP (Arm 2)
arm group label: DMP + M-POWER Rewards (Arm 3)
intervention type: Behavioral
intervention name: M-POWER Rewards
description: A financial incentive program were participants can earn up to 516 M-Points (1 M-Point is equivalent to S$1) over the one-year study period for performing specific activities according to recommendations that are meant to improve glycemic control and for achieving HbA1c and weight loss goals. M-Points can be reimbursed in the form of financial rebates for approved non-inpatient healthcare-related expenses incurred during the study period.
arm group label: DMP + M-POWER Rewards (Arm 3)
criteria: 
gender: All
minimum age: 21 Years
maximum age: 70 Years
healthy volunteers: No
last name: Eric A Finkelstein, PhD, MHA
role: Principal Investigator
affiliation: Duke-NUS Graduate Medical School
facility: 
country: Singapore
responsible party type: Principal Investigator
investigator affiliation: Duke-NUS Graduate Medical School
investigator full name: Eric A. Finkelstein
investigator title: Professor
mesh term: Diabetes Mellitus
mesh term: Diabetes Mellitus, Type 2