<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/NCT03249896</url>
  </required_header>
  <id_info>
    <org_study_id>DSRB 2017/00088</org_study_id>
    <nct_id>NCT03249896</nct_id>
  </id_info>
  <brief_title>Web/Smartphone-based Lifestyle Coaching Program in Pregnant Women With Gestational Diabetes</brief_title>
  <acronym>SMART-GDM</acronym>
  <official_title>Effects of a Web/Smartphone-based Lifestyle Coaching Program on Gestational Weight Gain in Pregnant Women With Gestational Diabetes</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National University Hospital, Singapore</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Jana Care</agency>
      <agency_class>Industry</agency_class>
    </collaborator>
  </sponsors>
  <source>National University Hospital, Singapore</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>
      Gestational diabetes mellitus (GDM) affects one fifth of Singaporean pregnancies and can&#xD;
      result in short and long term complications for mother and child. Mobile applications are&#xD;
      effective in improving diabetes care and weight related behaviors through improved&#xD;
      self-management. A multidisciplinary healthcare team from National University Hospital,&#xD;
      Singapore has worked with Jana Care to develop the Habits-GDM smartphone app, a lifestyle&#xD;
      coaching program specific for gestational diabetes. It consists of interactive lessons to&#xD;
      provide patient education, diet, activity and weight tracking tools, messaging platform for&#xD;
      coaching and motivating patients towards healthy behavior beneficial for gestational&#xD;
      diabetes. It interfaces with the Aina device, a novel hardware sensor that plugs into any&#xD;
      smartphone and can be used for glucose monitoring. This study aims to test the effectiveness&#xD;
      of this app in preventing excessive weight gain in pregnancy among patients with gestational&#xD;
      diabetes.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Gestational diabetes mellitus (GDM) is defined as glucose intolerance of any degree with&#xD;
      onset or first recognition during pregnancy. In Singapore, 20-30% of pregnant women are&#xD;
      affected by GDM. If poorly controlled, GDM is associated with various maternal and perinatal&#xD;
      morbidities such as increased cesarean deliveries, preeclampsia, preterm labour, macrosomia,&#xD;
      neonatal hypoglycemia etc. It is well recognized now that GDM is also associated with&#xD;
      long-term metabolic complications in mothers and offspring. Women with a history of GDM have&#xD;
      increased risk of GDM in subsequent pregnancies, and at high risk of developing Type 2&#xD;
      diabetes after pregnancy. Infants born to mothers with GDM are also at increased risk of&#xD;
      developing obesity and diabetes in later life. An increasing number of studies, including&#xD;
      studies in Singapore, suggest that screening and management of GDM can be cost-effective,&#xD;
      although these results are highly dependent on intervention efficacy.&#xD;
&#xD;
      In Singapore, individuals with GDM are first advised to adopt diet modification, and if&#xD;
      glycemic control is not on target despite diet control, insulin therapy is the next line of&#xD;
      treatment. In all cases, patients need to perform self-monitoring of blood glucose (SMBG) to&#xD;
      guide treatment decisions. These strategies aim to reduce the risks maternal and perinatal&#xD;
      complications. In addition to that, preventing excessive gestational weight gain (GWG) is&#xD;
      another important goal in women with GDM. This is because excessive GWG is not only&#xD;
      associated with higher risks of delivering a large for gestational age infant, but is also&#xD;
      the strongest risk factor for postpartum weight retention, and an important predictor for&#xD;
      future development of Type 2 diabetes. Lifestyle intervention programs have been shown to be&#xD;
      effective in reducing GWG in pregnant women.&#xD;
&#xD;
      One thing in common among diet modification, SMBG and achieving optimum GWG is that they&#xD;
      involve self-management, and hence require a certain degree of self-efficacy in women with&#xD;
      GDM. To achieve this in the National University Hospital (NUH), patients are referred to a&#xD;
      gestational diabetes clinic for education. At this time, if the patient's plasma glucose at 0&#xD;
      minute and/or 120 minutes of a 75g oral glucose tolerance test (OGTT) is &lt;7.0 mmol/L and&#xD;
      &lt;11.1 mmol/L respectively, this is conducted in a group teaching session lasting 1 - 1.5&#xD;
      hours, with 4 - 6 patients per group, led by a diabetes nurse educator and a dietitian. If&#xD;
      the patient's plasma glucose at 0 minutes and/or 120 minutes of a 75g OGTT is ≥7.0 and ≥11.1&#xD;
      mmol/L respectively, this is conducted in an individual session lasting 1 hour with a&#xD;
      diabetes nurse educator and a dietitian. Patients are initiated on capillary glucose&#xD;
      monitoring, typically 7 times a day, 2-3 days in a week. Subsequently, their care is provided&#xD;
      by their obstetrician until such time as the obstetrician feels that insulin is required, in&#xD;
      which case they are often referred back to the endocrinology service for the initiation and&#xD;
      management of insulin therapy. Capillary glucose monitoring is carried out using a glucometer&#xD;
      that is purchased by the patient, and the patient duly records blood glucose on a paper&#xD;
      record which will be shown to her obstetrician at the clinic appointments every 2 - 4 weeks.&#xD;
      In addition, weight is generally monitored at the clinic visit. Advice on diet and lifestyle&#xD;
      modification is provided by the obstetrician based on the results of capillary glucose&#xD;
      monitoring at the clinic visit. In between appointments, there is limited interaction between&#xD;
      healthcare providers unless the patient identifies a problem and contacts the provider.&#xD;
&#xD;
      This arrangement has some limitations. Firstly, it has been demonstrated that spacing&#xD;
      learning activities over a period of time improves encoding and long term retention of&#xD;
      information. As such, the current method of providing all the education that a patient needs&#xD;
      in a single session is less likely to be optimal for retention of information. Secondly, the&#xD;
      collection of information (through capillary glucose monitoring) is often separated from any&#xD;
      feedback from health care providers by days or weeks. In general, the lifestyle activities&#xD;
      (whether diet or physical activities) which generate any abnormal blood glucose results occur&#xD;
      in close proximity to the glucose readings (often hours before rather than weeks). By the&#xD;
      time feedback is received, the patient often does not recall the events that generated the&#xD;
      abnormal readings. More importantly, it does not provide any meaningful feedback to the&#xD;
      patient that allows modification of the risk of an episode of hyperglycemia. In fact, this&#xD;
      generates a significant amount of distress for the patient. It has been shown that the&#xD;
      distress perceived in response to a stressor is much greater when the person experiencing the&#xD;
      stressor does not have a way to control the occurrence of the stressor. In this context, the&#xD;
      lack of timely feedback that is actionable related to blood glucose or weight, results in&#xD;
      significant distress on the part of patients and results in a failure to adhere to efforts to&#xD;
      monitor or control blood glucose. In a recent mixed-methods feasibility study to assess&#xD;
      acceptability of mobile-application based support tool for women with GDM in NUH, most&#xD;
      reported significant stress from the burden of management and desire for supporting tools&#xD;
      that would aid control of blood glucose as an adjunct to self-monitoring.&#xD;
&#xD;
      It is the investigators' hypothesis that by providing education that is spaced out over 1-2&#xD;
      weeks and providing feedback that is timely and actionable to the patient, it will improve&#xD;
      adherence to lifestyle modification, reduce patient distress and improve clinical outcomes&#xD;
      for women with GDM.&#xD;
&#xD;
      Singapore has among the highest smartphone use in the world, with a smartphone adoption rate&#xD;
      of 88%. Such widespread adoption of mobile phones and smartphone provides a promising&#xD;
      opportunity to improve diabetes care and self-management, and to intervene on weight-related&#xD;
      behaviors in new and exciting ways. There is emerging evidence that mobile technologies&#xD;
      improve outcomes in patients with diabetes in the short term.&#xD;
&#xD;
      For these reasons, the investigators have developed a mobile application to aid in the&#xD;
      management of GDM. This is carried out with a company called Jana Care. Jana Care has&#xD;
      developed the Habits Program (http://www.habitsprogram.com), a lifestyle coaching program&#xD;
      which is available on Apple App Store and Google Play, two of the most commonly used&#xD;
      smartphone app platforms. This was developed based on the Diabetes Prevention Program and&#xD;
      Look-AHEAD Trial. It targets behavioral change by providing a personalized diabetes&#xD;
      management program which consists of 12 interactive video lessons, diet, physical activity&#xD;
      and weight tracking tools, interactive messaging platform with the lifestyle coaches, and&#xD;
      daily short messaging tips. It interfaces with the Aina device, a novel hardware sensor that&#xD;
      plugs into any smartphone and can be used to measure blood glucose. The glucose readings&#xD;
      measured are automatically transferred to the Habits Program application. It is able to&#xD;
      generate weekly reports for patients to assess their progress.&#xD;
&#xD;
      In 2013, a pilot study was conducted by Madras Diabetes Research Foundation &amp; Dr. Mohan's&#xD;
      Diabetes Specialties Center, World Health Organization Collaborating Center for&#xD;
      Non-communicable Diseases, International Diabetes Federation Center for Education, Chennai,&#xD;
      in collaboration with Jana Care, to look at the effect of the Habits Program on changes in&#xD;
      weight, caloric intake and physical activity in 64 overweight adults at high-risk of&#xD;
      developing diabetes, over a period of 16 weeks. The participants achieved moderate weight&#xD;
      loss of 4.2%, up to a maximum of 11kg. Average daily calorie and fat intake decreased by 28%&#xD;
      and 44% respectively, while daily physical activity increased from 3438 steps (week 1) to&#xD;
      8459 steps (week 16) (p&lt;0.05). Statistics collected by Jana Care from commercial deployment&#xD;
      with approximately 13,000 individuals also showed self-reported weight loss of 4.3% and&#xD;
      physical activity improvement of 26% at 12 weeks. Another randomized-control trial to assess&#xD;
      the effectiveness of the program is currently underway in India, funded by the Department of&#xD;
      Biotechnology, Government of India, in collaboration with Madras Diabetes Research&#xD;
      Foundation, Chennai and All India Institute of Medical Sciences (AIIMS), Delhi.&#xD;
&#xD;
      The Habits Program, however, is not specifically designed for the management of GDM. Working&#xD;
      with the Department of Obstetrics and Gynecology we have developed a workflow to manage&#xD;
      patients with GDM by optimizing blood glucose control through lifestyle modification and&#xD;
      achieving optimal GWG. We have worked with Jana Care to develop 'Habits-GDM' - an app&#xD;
      modified from the Habits Program which is designed specifically to support this workflow,&#xD;
      which takes into account the nutritional requirements and limitations on exercise during&#xD;
      pregnancy, and the need to prevent excessive weight gain (as opposed to weight loss in the&#xD;
      original Habits Program). The program has been customized for use in Singapore including&#xD;
      translation (with modification of messaging) and a Singapore food database.&#xD;
&#xD;
      The investigators hypothesize that the use of a web/smartphone-based coaching program&#xD;
      specific for the management of GDM can improve clinical outcomes among women with GDM. The&#xD;
      investigators propose to conduct a randomised clinical trial to study the efficacy of the&#xD;
      Habits-GDM program in clinical practice in Singapore. The primary outcome would be the&#xD;
      percentage of patients who have excessive gestational weight gain (EGWG) according to the&#xD;
      2009 US Institute of Medicine (IOM) guidelines.&#xD;
&#xD;
      GDM provides an ideal clinical scenario for the use of smartphone technologies to improve&#xD;
      self-efficacy and clinical outcomes. In Singapore, women of child bearing age well versed&#xD;
      with using smartphone apps, these individuals are generally highly motivated, engaged to&#xD;
      improve their own health and well-being of the fetus. In a preliminary studies, this seems to&#xD;
      be an acceptable and preferred mode of providing information and care. The most popular&#xD;
      option for obtaining support for management would be a web-based or smartphone app-based&#xD;
      resource, with 58.8% of the participants rated it as their most preferred choice. More&#xD;
      traditional source of information such as individual counselling, group teaching or printed&#xD;
      education materials were less preferred.&#xD;
&#xD;
      Furthermore, this phase of life and intervention period is short-lived and lasts a further&#xD;
      5-6 months from the time of diagnosis of GDM and therefore avoids the potential problem of&#xD;
      technology fatigue of a largely lifestyle modification intervention.&#xD;
&#xD;
      Despite pregnancy being a precious window of opportunity to bring about lasting beneficial&#xD;
      for patients and their offspring, and smartphone-based resources being the preferred source&#xD;
      of information by women with GDM, there are very few smartphone apps that are designed&#xD;
      specifically to support self-management of GDM. Moreover, there is a need for such apps to be&#xD;
      customized to the local context in terms of language, nutritional habits and food sources;&#xD;
      and integrated to a reliable degree with existing healthcare services, clinical workflow and&#xD;
      setup. To the investigators' knowledge, there are no such apps that fulfil these&#xD;
      characteristics and features that are currently available in Singapore and in this region,&#xD;
      and globally there are only two other similar trials among the GDM population which is being&#xD;
      carried out - one in Norway and another in Ireland.&#xD;
&#xD;
      This study will result in a unique clinical application for GDM that integrates lifestyle&#xD;
      coaching with glucose monitoring and can be used to treat GDM with minimal manpower&#xD;
      commitment. The improved clinical outcomes are not only relevant to the current pregnancy but&#xD;
      also have significant impact on the future metabolic health of both mothers and offspring&#xD;
      throughout later life.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Active, not recruiting</last_known_status>
  <start_date type="Actual">September 5, 2017</start_date>
  <completion_date type="Anticipated">May 2019</completion_date>
  <primary_completion_date type="Actual">April 26, 2019</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>
    <intervention_model_description>This is a prospective, randomised, controlled, parallel-group, single centre study. Study subjects will be randomised into either the intervention or the control arm in a 1:1 ratio. The randomisation will be stratified by ethnicity (Chinese or non-Chinese) and pre-pregnancy BMI (BMI of &lt;25kg/m2 or ≥25kg/m2), and will be performed using computer-generated randomisation.</intervention_model_description>
    <primary_purpose>Supportive Care</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Percentage of patients who have excessive gestational weight gain (EGWG)</measure>
    <time_frame>during the pregnancy until delivery</time_frame>
    <description>Percentage of patients who have EGWG is the proportion of subjects whose gestational weight gain (GWG) exceed the upper range of recommended weight gain for corresponding pre-pregnancy BMI (in this study, this is calculated using the first recorded weight and height in pregnancy at or before 12 weeks gestation) according to the 2009 IOM guidelines.&#xD;
GWG is calculated by subtracting the first recorded weight (in kilograms) in pregnancy at or before 12 weeks gestation from the most recent weight measurement taken in the hospital (either in the clinic or in the ward) prior to delivery.&#xD;
Pre-pregnancy BMI is calculated using the first recorded weight (in kilograms) and height (in meters) in pregnancy at or before 12 weeks gestation.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Absolute GWG stratified by whether or not the subject has EGWG for the gestational weeks at recruitment</measure>
    <time_frame>during the pregnancy until delivery</time_frame>
    <description>Absolute GWG stratified by whether or not the subject has exceeded the optimal GWG for the gestational weeks at recruitment (based on the 2009 IOM guidelines).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Absolute gestational weight gain</measure>
    <time_frame>during the pregnancy until delivery</time_frame>
    <description>Absolute gestational weight gain is calculated by subtracting the first recorded weight (in kilograms) in pregnancy at or before 12 weeks gestation from the most recent weight measurement taken in the hospital (either in the clinic or in the ward) prior to delivery.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Percentage of patients who have EGWG according to the 2009 US IOM guidelines stratified by whether or not the subject has EGWG for the gestational weeks at recruitment</measure>
    <time_frame>during the pregnancy until delivery</time_frame>
    <description>Percentage of patients who have EGWG according to the 2009 US IOM guidelines stratified by whether or not the subject has exceeded the optimal GWG for the gestational weeks at recruitment</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Adherence to SMBG</measure>
    <time_frame>From recruitment until delivery</time_frame>
    <description>Numbers of SMBG performed</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Average readings of self-monitored blood glucose</measure>
    <time_frame>From recruitment until delivery</time_frame>
    <description>Average readings of self-monitored blood glucose</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Proportion of glucose readings above glycemic targets</measure>
    <time_frame>From recruitment until delivery</time_frame>
    <description>Glycemic targets are &lt;/=5.5 mmol/L premeals, &lt;/= 6.6 mmol/L at 2 hours post meals</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Proportion of subjects who progress to needing metformin and/or insulin therapy</measure>
    <time_frame>From recruitment until delivery</time_frame>
    <description>Needing metformin and/or insulin therapy in addition to diet modification</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Mode of delivery</measure>
    <time_frame>At delivery</time_frame>
    <description>Vaginal delivery, assisted delivery, cesarean section</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Hypertensive disorders in pregnancy</measure>
    <time_frame>During the pregnancy until delivery</time_frame>
    <description>Pregnancy induced hypertension, preeclampsia, eclampsia</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Depression score</measure>
    <time_frame>Between 35-37 weeks of gestation</time_frame>
    <description>Edinburgh Postnatal Depression Scale</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Anxiety score</measure>
    <time_frame>Between 35-37 weeks of gestation</time_frame>
    <description>State-Trait Anxiety Inventory</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Premature delivery</measure>
    <time_frame>At delivery</time_frame>
    <description>Delivery before 37 weeks of gestation</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Apgar score</measure>
    <time_frame>At birth</time_frame>
    <description>Apgar score at 1 and 5 minutes after birth</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Birth weight</measure>
    <time_frame>At birth</time_frame>
    <description>Weight of the baby at birth</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Shoulder dystocia</measure>
    <time_frame>At birth</time_frame>
    <description>Shoulder dystocia at birth</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Birth trauma</measure>
    <time_frame>At birth</time_frame>
    <description>Birth trauma at birth</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Neonatal hypoglycemia</measure>
    <time_frame>First 24 hours from birth</time_frame>
    <description>Capillary blood glucose level of &lt;2.6mmol/L</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Respiratory distress needing intubation</measure>
    <time_frame>At birth</time_frame>
    <description>Respiratory distress needing intubation</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Neonatal intensive care unit admission</measure>
    <time_frame>First 24 hours from birth</time_frame>
    <description>Neonatal intensive care unit admission</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">340</enrollment>
  <condition>Gestational Diabetes</condition>
  <condition>Pregnancy Complications</condition>
  <condition>Weight Gain</condition>
  <arm_group>
    <arm_group_label>Intervention</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Patients in the intervention arm will receive standard medical care and in addition to that, be given the Aina or Aina Mini device for self-monitoring of blood glucose (SMBG), the Habits-GDM mobile app, and a weighing scale.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Patients in the control arm will receive standard medical care and only be given the Aina or Aina Mini device for SMBG.&#xD;
Standard medical care involves one session of face-to-face education by a diabetes nurse educator and a dietician. Patients are initiated on capillary glucose monitoring. Subsequently, standard clinical care is provided by their obstetrician. Participation in this study will not increase the frequency of clinic visits. The frequency of SMBG will be as clinically indicated and not increased as a result of participation in this study. Should the obstetrician feels that insulin is required, it will be initiated and if necessary the patient will be referred to the endocrinology service for management of insulin therapy. In some patients, the clinician may decide to prescribe metformin.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Habits-GDM mobile app</intervention_name>
    <description>The intervention is a self-administered mobile app designed for GDM. It targets behavioural change by providing personalised GDM management program which consists of three main elements: lessons, tracking and coaching/feedback. Lessons contains 12 interactive modules which provide patient education on GDM. Each lesson will take approximately 10-20 minutes. Information on SMBG (linked to the Aina or Aina Mini device), weight (linked to the Bluetooth weighing scale), physical activity (physical activity tracking function in the app), and food (equipped with common local food using the Singapore food database) can be tracked and displayed visually. An interactive messaging platform is used for coaching. Generic and customised automated messages are sent from a virtual lifestyle coach to encourage and motivate patients towards healthy behaviour beneficial for GDM.</description>
    <arm_group_label>Intervention</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Ability to provide informed consent.&#xD;
&#xD;
          2. Women aged 21 years and older.&#xD;
&#xD;
          3. Singleton pregnancy.&#xD;
&#xD;
          4. GDM diagnosed between 12 to 30 weeks of gestation, based on the 2013 World Health&#xD;
             Organization (WHO) criteria, i.e. either of the following: fasting plasma glucose ≥5.1&#xD;
             mmol/L, 60-minute plasma glucose ≥10.0 mmol/L, 120-minute plasma glucose ≥8.5 mmol/L,&#xD;
             during a 75g oral glucose tolerance test (OGTT).&#xD;
&#xD;
          5. Possesses a smartphone and ability to navigate a smartphone app.&#xD;
&#xD;
          6. Proficient in English language.&#xD;
&#xD;
          7. Plan to deliver the baby in National University Hospital.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Multiple pregnancy.&#xD;
&#xD;
          2. Pre-existing diabetes (type 1 diabetes, type 2 diabetes, or other specific types of&#xD;
             diabetes) diagnosed prior to current pregnancy.&#xD;
&#xD;
          3. GDM diagnosed before 12 weeks of gestation.&#xD;
&#xD;
          4. No weight available in first trimester (at or before 12 weeks gestation) of the&#xD;
             pregnancy.&#xD;
&#xD;
          5. Need for insulin therapy from the start of diagnosis of GDM, as determined by the&#xD;
             primary clinician.&#xD;
&#xD;
          6. Heart failure.&#xD;
&#xD;
          7. Chronic kidney disease&#xD;
&#xD;
          8. Feeding and eating disorders.&#xD;
&#xD;
          9. History of bariatric surgery.&#xD;
&#xD;
         10. Long-term systemic corticosteroids use.&#xD;
&#xD;
         11. Impaired mobility.&#xD;
&#xD;
         12. Concomitant participation in another clinical study (i.e. Phase I-III clinical&#xD;
             studies) with investigational medicinal product(s).&#xD;
      </textblock>
    </criteria>
    <gender>Female</gender>
    <minimum_age>21 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>National University Hospital</name>
      <address>
        <city>Singapore</city>
        <zip>119228</zip>
        <country>Singapore</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Singapore</country>
  </location_countries>
  <link>
    <url>http://www.consumerbarometer.com/en/graph-builder/?question=M1&amp;filter=country:united_states,china,hong_kong_sar,korea,malaysia,singapore,australia</url>
    <description>Consumer Barometer with Google.</description>
  </link>
  <link>
    <url>http://www.who.int/iris/handle/10665/85975</url>
    <description>World Health Organization. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy.</description>
  </link>
  <reference>
    <citation>American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014 Jan;37 Suppl 1:S81-90. doi: 10.2337/dc14-S081. No abstract available.</citation>
    <PMID>24357215</PMID>
  </reference>
  <reference>
    <citation>Chong YS, Cai S, Lin H, Soh SE, Lee YS, Leow MK, Chan YH, Chen L, Holbrook JD, Tan KH, Rajadurai VS, Yeo GS, Kramer MS, Saw SM, Gluckman PD, Godfrey KM, Kwek K; GUSTO study group. Ethnic differences translate to inadequacy of high-risk screening for gestational diabetes mellitus in an Asian population: a cohort study. BMC Pregnancy Childbirth. 2014 Oct 2;14:345. doi: 10.1186/1471-2393-14-345.</citation>
    <PMID>25273851</PMID>
  </reference>
  <reference>
    <citation>Yew TW, Khoo CM, Thai AC, Kale AS, Yong EL, Tai ES. The Prevalence of Gestational Diabetes Mellitus Among Asian Females is Lower Using the New 2013 World Health Organization Diagnostic Criteria. Endocr Pract. 2014 Oct;20(10):1064-9. doi: 10.4158/EP14028.OR.</citation>
    <PMID>24936548</PMID>
  </reference>
  <reference>
    <citation>Bellamy L, Casas JP, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009 May 23;373(9677):1773-9. doi: 10.1016/S0140-6736(09)60731-5.</citation>
    <PMID>19465232</PMID>
  </reference>
  <reference>
    <citation>Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care. 2002 Oct;25(10):1862-8. doi: 10.2337/diacare.25.10.1862.</citation>
    <PMID>12351492</PMID>
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  <verification_date>November 2018</verification_date>
  <study_first_submitted>August 1, 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>May 27, 2019</last_update_submitted>
  <last_update_submitted_qc>May 27, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">May 29, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Gestational Diabetes</keyword>
  <keyword>RCT</keyword>
  <keyword>Lifestyle coaching program</keyword>
  <keyword>Smartphone app</keyword>
  <keyword>Gestational weight gain</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Diabetes, Gestational</mesh_term>
    <mesh_term>Pregnancy Complications</mesh_term>
    <mesh_term>Diabetes Mellitus</mesh_term>
    <mesh_term>Weight Gain</mesh_term>
    <mesh_term>Gestational Weight Gain</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- 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/NCT03249896
org study id: DSRB 2017/00088
nct id: NCT03249896
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Gestational diabetes mellitus (GDM) affects one fifth of Singaporean pregnancies and can
      result in short and long term complications for mother and child. Mobile applications are
      effective in improving diabetes care and weight related behaviors through improved
      self-management. A multidisciplinary healthcare team from National University Hospital,
      Singapore has worked with Jana Care to develop the Habits-GDM smartphone app, a lifestyle
      coaching program specific for gestational diabetes. It consists of interactive lessons to
      provide patient education, diet, activity and weight tracking tools, messaging platform for
      coaching and motivating patients towards healthy behavior beneficial for gestational
      diabetes. It interfaces with the Aina device, a novel hardware sensor that plugs into any
      smartphone and can be used for glucose monitoring. This study aims to test the effectiveness
      of this app in preventing excessive weight gain in pregnancy among patients with gestational
      diabetes.
Gestational diabetes mellitus (GDM) is defined as glucose intolerance of any degree with
      onset or first recognition during pregnancy. In Singapore, 20-30% of pregnant women are
      affected by GDM. If poorly controlled, GDM is associated with various maternal and perinatal
      morbidities such as increased cesarean deliveries, preeclampsia, preterm labour, macrosomia,
      neonatal hypoglycemia etc. It is well recognized now that GDM is also associated with
      long-term metabolic complications in mothers and offspring. Women with a history of GDM have
      increased risk of GDM in subsequent pregnancies, and at high risk of developing Type 2
      diabetes after pregnancy. Infants born to mothers with GDM are also at increased risk of
      developing obesity and diabetes in later life. An increasing number of studies, including
      studies in Singapore, suggest that screening and management of GDM can be cost-effective,
      although these results are highly dependent on intervention efficacy.

      In Singapore, individuals with GDM are first advised to adopt diet modification, and if
      glycemic control is not on target despite diet control, insulin therapy is the next line of
      treatment. In all cases, patients need to perform self-monitoring of blood glucose (SMBG) to
      guide treatment decisions. These strategies aim to reduce the risks maternal and perinatal
      complications. In addition to that, preventing excessive gestational weight gain (GWG) is
      another important goal in women with GDM. This is because excessive GWG is not only
      associated with higher risks of delivering a large for gestational age infant, but is also
      the strongest risk factor for postpartum weight retention, and an important predictor for
      future development of Type 2 diabetes. Lifestyle intervention programs have been shown to be
      effective in reducing GWG in pregnant women.

      One thing in common among diet modification, SMBG and achieving optimum GWG is that they
      involve self-management, and hence require a certain degree of self-efficacy in women with
      GDM. To achieve this in the National University Hospital (NUH), patients are referred to a
      gestational diabetes clinic for education. At this time, if the patient's plasma glucose at 0
      minute and/or 120 minutes of a 75g oral glucose tolerance test (OGTT) is <7.0 mmol/L and
      <11.1 mmol/L respectively, this is conducted in a group teaching session lasting 1 - 1.5
      hours, with 4 - 6 patients per group, led by a diabetes nurse educator and a dietitian. If
      the patient's plasma glucose at 0 minutes and/or 120 minutes of a 75g OGTT is ≥7.0 and ≥11.1
      mmol/L respectively, this is conducted in an individual session lasting 1 hour with a
      diabetes nurse educator and a dietitian. Patients are initiated on capillary glucose
      monitoring, typically 7 times a day, 2-3 days in a week. Subsequently, their care is provided
      by their obstetrician until such time as the obstetrician feels that insulin is required, in
      which case they are often referred back to the endocrinology service for the initiation and
      management of insulin therapy. Capillary glucose monitoring is carried out using a glucometer
      that is purchased by the patient, and the patient duly records blood glucose on a paper
      record which will be shown to her obstetrician at the clinic appointments every 2 - 4 weeks.
      In addition, weight is generally monitored at the clinic visit. Advice on diet and lifestyle
      modification is provided by the obstetrician based on the results of capillary glucose
      monitoring at the clinic visit. In between appointments, there is limited interaction between
      healthcare providers unless the patient identifies a problem and contacts the provider.

      This arrangement has some limitations. Firstly, it has been demonstrated that spacing
      learning activities over a period of time improves encoding and long term retention of
      information. As such, the current method of providing all the education that a patient needs
      in a single session is less likely to be optimal for retention of information. Secondly, the
      collection of information (through capillary glucose monitoring) is often separated from any
      feedback from health care providers by days or weeks. In general, the lifestyle activities
      (whether diet or physical activities) which generate any abnormal blood glucose results occur
      in close proximity to the glucose readings (often hours before rather than weeks). By the
      time feedback is received, the patient often does not recall the events that generated the
      abnormal readings. More importantly, it does not provide any meaningful feedback to the
      patient that allows modification of the risk of an episode of hyperglycemia. In fact, this
      generates a significant amount of distress for the patient. It has been shown that the
      distress perceived in response to a stressor is much greater when the person experiencing the
      stressor does not have a way to control the occurrence of the stressor. In this context, the
      lack of timely feedback that is actionable related to blood glucose or weight, results in
      significant distress on the part of patients and results in a failure to adhere to efforts to
      monitor or control blood glucose. In a recent mixed-methods feasibility study to assess
      acceptability of mobile-application based support tool for women with GDM in NUH, most
      reported significant stress from the burden of management and desire for supporting tools
      that would aid control of blood glucose as an adjunct to self-monitoring.

      It is the investigators' hypothesis that by providing education that is spaced out over 1-2
      weeks and providing feedback that is timely and actionable to the patient, it will improve
      adherence to lifestyle modification, reduce patient distress and improve clinical outcomes
      for women with GDM.

      Singapore has among the highest smartphone use in the world, with a smartphone adoption rate
      of 88%. Such widespread adoption of mobile phones and smartphone provides a promising
      opportunity to improve diabetes care and self-management, and to intervene on weight-related
      behaviors in new and exciting ways. There is emerging evidence that mobile technologies
      improve outcomes in patients with diabetes in the short term.

      For these reasons, the investigators have developed a mobile application to aid in the
      management of GDM. This is carried out with a company called Jana Care. Jana Care has
      developed the Habits Program (http://www.habitsprogram.com), a lifestyle coaching program
      which is available on Apple App Store and Google Play, two of the most commonly used
      smartphone app platforms. This was developed based on the Diabetes Prevention Program and
      Look-AHEAD Trial. It targets behavioral change by providing a personalized diabetes
      management program which consists of 12 interactive video lessons, diet, physical activity
      and weight tracking tools, interactive messaging platform with the lifestyle coaches, and
      daily short messaging tips. It interfaces with the Aina device, a novel hardware sensor that
      plugs into any smartphone and can be used to measure blood glucose. The glucose readings
      measured are automatically transferred to the Habits Program application. It is able to
      generate weekly reports for patients to assess their progress.

      In 2013, a pilot study was conducted by Madras Diabetes Research Foundation & Dr. Mohan's
      Diabetes Specialties Center, World Health Organization Collaborating Center for
      Non-communicable Diseases, International Diabetes Federation Center for Education, Chennai,
      in collaboration with Jana Care, to look at the effect of the Habits Program on changes in
      weight, caloric intake and physical activity in 64 overweight adults at high-risk of
      developing diabetes, over a period of 16 weeks. The participants achieved moderate weight
      loss of 4.2%, up to a maximum of 11kg. Average daily calorie and fat intake decreased by 28%
      and 44% respectively, while daily physical activity increased from 3438 steps (week 1) to
      8459 steps (week 16) (p<0.05). Statistics collected by Jana Care from commercial deployment
      with approximately 13,000 individuals also showed self-reported weight loss of 4.3% and
      physical activity improvement of 26% at 12 weeks. Another randomized-control trial to assess
      the effectiveness of the program is currently underway in India, funded by the Department of
      Biotechnology, Government of India, in collaboration with Madras Diabetes Research
      Foundation, Chennai and All India Institute of Medical Sciences (AIIMS), Delhi.

      The Habits Program, however, is not specifically designed for the management of GDM. Working
      with the Department of Obstetrics and Gynecology we have developed a workflow to manage
      patients with GDM by optimizing blood glucose control through lifestyle modification and
      achieving optimal GWG. We have worked with Jana Care to develop 'Habits-GDM' - an app
      modified from the Habits Program which is designed specifically to support this workflow,
      which takes into account the nutritional requirements and limitations on exercise during
      pregnancy, and the need to prevent excessive weight gain (as opposed to weight loss in the
      original Habits Program). The program has been customized for use in Singapore including
      translation (with modification of messaging) and a Singapore food database.

      The investigators hypothesize that the use of a web/smartphone-based coaching program
      specific for the management of GDM can improve clinical outcomes among women with GDM. The
      investigators propose to conduct a randomised clinical trial to study the efficacy of the
      Habits-GDM program in clinical practice in Singapore. The primary outcome would be the
      percentage of patients who have excessive gestational weight gain (EGWG) according to the
      2009 US Institute of Medicine (IOM) guidelines.

      GDM provides an ideal clinical scenario for the use of smartphone technologies to improve
      self-efficacy and clinical outcomes. In Singapore, women of child bearing age well versed
      with using smartphone apps, these individuals are generally highly motivated, engaged to
      improve their own health and well-being of the fetus. In a preliminary studies, this seems to
      be an acceptable and preferred mode of providing information and care. The most popular
      option for obtaining support for management would be a web-based or smartphone app-based
      resource, with 58.8% of the participants rated it as their most preferred choice. More
      traditional source of information such as individual counselling, group teaching or printed
      education materials were less preferred.

      Furthermore, this phase of life and intervention period is short-lived and lasts a further
      5-6 months from the time of diagnosis of GDM and therefore avoids the potential problem of
      technology fatigue of a largely lifestyle modification intervention.

      Despite pregnancy being a precious window of opportunity to bring about lasting beneficial
      for patients and their offspring, and smartphone-based resources being the preferred source
      of information by women with GDM, there are very few smartphone apps that are designed
      specifically to support self-management of GDM. Moreover, there is a need for such apps to be
      customized to the local context in terms of language, nutritional habits and food sources;
      and integrated to a reliable degree with existing healthcare services, clinical workflow and
      setup. To the investigators' knowledge, there are no such apps that fulfil these
      characteristics and features that are currently available in Singapore and in this region,
      and globally there are only two other similar trials among the GDM population which is being
      carried out - one in Norway and another in Ireland.

      This study will result in a unique clinical application for GDM that integrates lifestyle
      coaching with glucose monitoring and can be used to treat GDM with minimal manpower
      commitment. The improved clinical outcomes are not only relevant to the current pregnancy but
      also have significant impact on the future metabolic health of both mothers and offspring
      throughout later life.
allocation: Randomized
intervention model: Parallel Assignment
intervention model description: This is a prospective, randomised, controlled, parallel-group, single centre study. Study subjects will be randomised into either the intervention or the control arm in a 1:1 ratio. The randomisation will be stratified by ethnicity (Chinese or non-Chinese) and pre-pregnancy BMI (BMI of <25kg/m2 or ≥25kg/m2), and will be performed using computer-generated randomisation.
primary purpose: Supportive Care
masking: None (Open Label)
measure: Percentage of patients who have excessive gestational weight gain (EGWG)
time frame: during the pregnancy until delivery
description: Percentage of patients who have EGWG is the proportion of subjects whose gestational weight gain (GWG) exceed the upper range of recommended weight gain for corresponding pre-pregnancy BMI (in this study, this is calculated using the first recorded weight and height in pregnancy at or before 12 weeks gestation) according to the 2009 IOM guidelines.
GWG is calculated by subtracting the first recorded weight (in kilograms) in pregnancy at or before 12 weeks gestation from the most recent weight measurement taken in the hospital (either in the clinic or in the ward) prior to delivery.
Pre-pregnancy BMI is calculated using the first recorded weight (in kilograms) and height (in meters) in pregnancy at or before 12 weeks gestation.
measure: Absolute GWG stratified by whether or not the subject has EGWG for the gestational weeks at recruitment
time frame: during the pregnancy until delivery
description: Absolute GWG stratified by whether or not the subject has exceeded the optimal GWG for the gestational weeks at recruitment (based on the 2009 IOM guidelines).
measure: Absolute gestational weight gain
time frame: during the pregnancy until delivery
description: Absolute gestational weight gain is calculated by subtracting the first recorded weight (in kilograms) in pregnancy at or before 12 weeks gestation from the most recent weight measurement taken in the hospital (either in the clinic or in the ward) prior to delivery.
measure: Percentage of patients who have EGWG according to the 2009 US IOM guidelines stratified by whether or not the subject has EGWG for the gestational weeks at recruitment
time frame: during the pregnancy until delivery
description: Percentage of patients who have EGWG according to the 2009 US IOM guidelines stratified by whether or not the subject has exceeded the optimal GWG for the gestational weeks at recruitment
measure: Adherence to SMBG
time frame: From recruitment until delivery
description: Numbers of SMBG performed
measure: Average readings of self-monitored blood glucose
time frame: From recruitment until delivery
description: Average readings of self-monitored blood glucose
measure: Proportion of glucose readings above glycemic targets
time frame: From recruitment until delivery
description: Glycemic targets are </=5.5 mmol/L premeals, </= 6.6 mmol/L at 2 hours post meals
measure: Proportion of subjects who progress to needing metformin and/or insulin therapy
time frame: From recruitment until delivery
description: Needing metformin and/or insulin therapy in addition to diet modification
measure: Mode of delivery
time frame: At delivery
description: Vaginal delivery, assisted delivery, cesarean section
measure: Hypertensive disorders in pregnancy
time frame: During the pregnancy until delivery
description: Pregnancy induced hypertension, preeclampsia, eclampsia
measure: Depression score
time frame: Between 35-37 weeks of gestation
description: Edinburgh Postnatal Depression Scale
measure: Anxiety score
time frame: Between 35-37 weeks of gestation
description: State-Trait Anxiety Inventory
measure: Premature delivery
time frame: At delivery
description: Delivery before 37 weeks of gestation
measure: Apgar score
time frame: At birth
description: Apgar score at 1 and 5 minutes after birth
measure: Birth weight
time frame: At birth
description: Weight of the baby at birth
measure: Shoulder dystocia
time frame: At birth
description: Shoulder dystocia at birth
measure: Birth trauma
time frame: At birth
description: Birth trauma at birth
measure: Neonatal hypoglycemia
time frame: First 24 hours from birth
description: Capillary blood glucose level of <2.6mmol/L
measure: Respiratory distress needing intubation
time frame: At birth
description: Respiratory distress needing intubation
measure: Neonatal intensive care unit admission
time frame: First 24 hours from birth
description: Neonatal intensive care unit admission
arm group label: Intervention
arm group type: Experimental
description: Patients in the intervention arm will receive standard medical care and in addition to that, be given the Aina or Aina Mini device for self-monitoring of blood glucose (SMBG), the Habits-GDM mobile app, and a weighing scale.
arm group label: Control
arm group type: No Intervention
description: Patients in the control arm will receive standard medical care and only be given the Aina or Aina Mini device for SMBG.
Standard medical care involves one session of face-to-face education by a diabetes nurse educator and a dietician. Patients are initiated on capillary glucose monitoring. Subsequently, standard clinical care is provided by their obstetrician. Participation in this study will not increase the frequency of clinic visits. The frequency of SMBG will be as clinically indicated and not increased as a result of participation in this study. Should the obstetrician feels that insulin is required, it will be initiated and if necessary the patient will be referred to the endocrinology service for management of insulin therapy. In some patients, the clinician may decide to prescribe metformin.
intervention type: Behavioral
intervention name: Habits-GDM mobile app
description: The intervention is a self-administered mobile app designed for GDM. It targets behavioural change by providing personalised GDM management program which consists of three main elements: lessons, tracking and coaching/feedback. Lessons contains 12 interactive modules which provide patient education on GDM. Each lesson will take approximately 10-20 minutes. Information on SMBG (linked to the Aina or Aina Mini device), weight (linked to the Bluetooth weighing scale), physical activity (physical activity tracking function in the app), and food (equipped with common local food using the Singapore food database) can be tracked and displayed visually. An interactive messaging platform is used for coaching. Generic and customised automated messages are sent from a virtual lifestyle coach to encourage and motivate patients towards healthy behaviour beneficial for GDM.
arm group label: Intervention
criteria: 
gender: Female
minimum age: 21 Years
maximum age: N/A
healthy volunteers: No
facility: 
country: Singapore
url: http://www.consumerbarometer.com/en/graph-builder/?question=M1&filter=country:united_states,china,hong_kong_sar,korea,malaysia,singapore,australia
description: Consumer Barometer with Google.
url: http://www.who.int/iris/handle/10665/85975
description: World Health Organization. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy.
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PMID: 26625980
responsible party type: Sponsor
mesh term: Diabetes, Gestational
mesh term: Pregnancy Complications
mesh term: Diabetes Mellitus
mesh term: Weight Gain
mesh term: Gestational Weight Gain
sharing ipd: No