An interventional study of Electronic Decision Support System (EDSS) and Assisted Telemedicine in Chronic Conditions, Multiple, Diseases of the Circulatory System and Diseases of the Respiratory System, sponsored by Centre for Chronic Disease Control, India. Active, not recruiting at 119 sites in 2 countries. Open to participants aged 40 Years to 100 Years. Per ClinicalTrials.gov, last updated 2026-11-10.
Sponsored by Centre for Chronic Disease Control, India · Not applicable, Interventional, and Health services research
Many people in India and Nepal live with two or more long-term health conditions at the same time for example, high blood pressure together with diabetes, and often alongside problems such as joint pain, breathing difficulties, or anxiety. Living with several conditions at once is known as having multiple long-term conditions, or MLTC. Care for these conditions is usually provided one disease at a time, through separate clinics and services. This can mean repeated visits, several different doctors, many medicines to manage, and advice that does not always fit together. For participants and their families, this makes care harder, more time-consuming, and more expensive. This study tests a more joined-up way of caring for people with MLTC and examines whether it works better than usual care.
Before designing the new approach, the investigators worked to understand the problem. The investigators examined existing health data, identified which conditions most commonly occur together, visited primary health centres to assess how ready they were to provide care and what were common conditions reported at these facilities and spoke with patients, families, and health care providers about the difficulties they face. The investigators then worked together with these groups to design the new model of care, so that it reflects real needs and fits local conditions.
The new approach or intervention has three main components. The first is an electronic decision support system a computer tool that helps doctors and nurses assess a participant's conditions together and choose evidence-based treatment, rather than managing each condition separately. The second is an assisted-telemedicine service, which lets participants connect with a specialist by video from their local health centre, so expert advice is available without travelling long distances. The third is a mobile phone application for participants that offers simple health information, medicine and appointment reminders, and support for day-to-day self-care, available in local languages and in audio and video form for those who find reading difficult. Supporting all of this are trained care coordinators, called Care Mitras, who guide participants through their care, help them use the tools, and encourage regular follow-up.
The study takes place in both rural and urban areas: Anakapalli District in Andhra Pradesh, Jodhpur in Rajasthan, and Sonipat in Haryana in India, and Lalitpur , Kathmandu , Bhaktapur , Kavre , Chitwan , Makwanpur , Nuwakot Districts, at Bagmati province in Nepal. Adults aged 40 years and over who have two or more long-term chronic conditions can take part. The study uses a cluster randomised controlled trial design. This means whole primary health centres not individual participants were assigned to one of two groups. Health centres in the first group provide the new model of care, while those in the second group continue with usual care. Comparing the two groups shows whether the new approach works better. In total, 120 health centres took part, and 3,600 participants were enrolled across them.
The main question the investigators want to answer is whether the new approach improves participants' quality of life (how they feel and function in daily life). The investigators will also examine other important outcomes. Information is collected through health check-ups and interviews with participants over the course of the study, along with records of how the tools are used in practice.
By bringing together digital tools, specialist access, and local support within primary care, the study aims to show whether this integrated, people-centred approach can improve life for people with MLTC and reduce the gaps in their care. If it works, the findings can help build practical and lasting ways to care for people with multiple conditions in India, Nepal, and other countries with similar health systems.
Study design and setting:
This study is a cluster randomised controlled trial evaluating an integrated, technology-enabled model of care for people living with multiple long-term conditions (MLTC) in primary health centres (PHCs) in India and Nepal. Whole PHCs, rather than individual participants, are the unit of randomisation: each participating PHC is allocated to either the intervention arm or a usual-care (control) arm, allowing comparison between the two models while limiting contamination.
The trial is being implemented across four sites in India and Nepal: Anakapalli (Andhra Pradesh), Jodhpur (Rajasthan) and Sonipat (Haryana) in India, and Lalitpur , Kathmandu , Bhaktapur , Kavre , Chitwan , Makwanpur , Nuwakot Districts, at Bagmati province in Nepal site covering both rural and urban contexts. Each site includes 30 clusters, participants in intervention clusters receive the programme over a 24-month intervention period, while control clusters continue with routine care. The sample-size calculation allowed for clustering at the facility level, assuming a two-sided significance level of 0.05, 90% power, a mean control-group quality-of-life score of 74.37 (standard deviation 15.1), a minimum detectable difference of 2.5 points, an intracluster correlation coefficient of 0.02, and 20% attrition. On this basis, the required sample is 120 clusters with 30 participants each, giving 3,600 participants in total.
Study phases and progress:
The study was developed in stages. First, a formative phase generated the evidence base, comprising a secondary analysis of MLTC prevalence and patterns by pooling 17 datasets and 2,016,846 participants (approximately two million) from India and Nepal, a case-mix assessment of the conditions presenting at primary care, in-depth interviews with patients, caregivers and health workers, and health facility assessments of readiness to deliver integrated care. The facility assessments covered 40 sub centres and 20 PHCs in India and 37 primary health facilities in Nepal, using standards adapted from the Indian Public Health Standards 2022 and Nepal's Minimum Service Standards 2020.
Second, the findings from the formative phase were brought together with stakeholders through a structured co-design process to develop the intervention. The intervention components were developed iteratively with stakeholders between December 2024 and early 2026. Fourteen co-design workshops were held in total: four at each of the three sites, together with two national-level workshops. The co-design process was guided by the ADAPT framework (Assessment, Decision, Adaptation, Production, and Testing/Implementation), which structured the progression from identifying care gaps to selecting, prioritising, prototyping and testing intervention features. Participants were organised into stakeholder groups to ensure representation: people with MLTC and their caregivers, community representatives; primary health care providers, technical experts and researchers; and policymakers and district or state officials. Patient and caregiver groups identified user preferences, including self-care support, and barriers such as fragmented care and out-of-pocket costs. Provider and expert groups made practical design recommendations, including integrating clinical guidelines into workflows, incorporating drug-interaction alerts, and defining standard teleconsultation formats with attention to language and trust. A joint session validated and prioritised features using the MoSCoW method; prioritised "must-have" features included an editable decision-support dashboard, simple application navigation in local languages, offline data entry, and a reliable telemedicine referral pathway. Feedback on the patient-facing application emphasised low-literacy formats (audio, video and SMS/IVR options) and event-triggered reminders. Emerging insights were documented and fed back through successive design cycles, ensuring that the EDSS algorithms, telemedicine workflows and mobile application reflected local context, language and health-system realities. The co-design phase also established stakeholder-engagement structures and preparatory materials such as training modules and user manuals.
Third stage, co-designed intervention was pilot tested in six PHCs (four in India and two in Nepal) with approximately 180 participants (about 30 per cluster). In pilot, investigators assessed feasibility, usability, acceptability, fidelity, data completeness and implementation processes. The findings were used to refine the workflows, training, study tools and technical specifications, without altering the core intervention components.
The study has now progressed to the main cluster randomised controlled trial. Participant screening, enrolment and baseline assessments commenced on 18 May 2026, and the intervention was initiated in the intervention arm on 13 August 2026.
Intervention components and digital architecture:
The intervention package has three components, delivered on a secure, cloud-enabled platform aligned with national digital health standards.
Electronic Decision Support System (EDSS): Built on the Centre for Chronic Disease Control's DigiSetu platform, the EDSS extends existing modules (hypertension, diabetes and cardiovascular disease) to cover conditions relevant to MLTC, including COPD, asthma, Knee osteoarthritis, backache, mental health conditions (anxiety and depression) and vision and hearing difficulties. Within a structured PHC workflow, nurses enter vital signs, history and laboratory results, and the system generates guideline-based management plans. Medical officers review these plans, override them where clinically indicated, and finalise care. The EDSS provides an at-a-glance dashboard of key diagnoses, pending follow-ups and alerts. It functions as an assistive tool, with clinicians retaining full override authority. Back-end audit trails will be maintained.
Assisted telemedicine: This component connects PHC staff and participants with remote specialists through a facility-based model that provides real-time specialist consultations within the PHC. When a participant needs specialist input or in absence/non availability of PHC medical officer, the trained PHC staff initiates a teleconsultation from the health centre, so the participant can receive expert advice locally without travelling long distances to higher-level facilities.
Patient-facing mobile application and care coordination through Care Mitras: The application supports self-management and is linked with the EDSS. This linkage makes the care plan and prescription generated during clinical visits available directly to the participant. The application displays the participant's current medications, care plan and scheduled follow-up dates, and aligns reminders with the instructions recorded in the EDSS. It also connects with the national ABHA Health ID to import health records securely and provides symptom tracking and a library of lifestyle and adherence content. Following co-design feedback, content is provided in low-literacy, audio-visual formats in local languages, with notifications triggered around clinic visits, medication changes and scheduled follow-ups rather than continuously. For participants without smartphones, SMS and interactive voice response reminders are used, with caregivers and frontline workers engaged to relay key messages. Care Mitras are trained, facility-based members of the study team who support participants throughout their care pathway. They help participants use the digital tools, encourage treatment adherence and attendance at follow-up visits, arrange telemedicine consultations at the PHC, and act as a trusted link between participants and the primary healthcare team.
Cluster randomised controlled trial The trial involves participant screening, enrolment and baseline assessment, followed by cluster randomisation into intervention and control arms, intervention implementation, and subsequent midline and endline assessments.
Trained health workers screen attendees visiting PHCs using a standardised eligibility tool to determine whether they meet the study inclusion criteria, including age (40 years and above) and the presence of at least two of the listed long-term conditions. Eligible individuals receive study information and provide written informed consent. After consent, a baseline assessment visit will be scheduled to collects socio-demographic, medical-history and behavioural information, together with objective clinical measurements, using standardised, interviewer-administered instruments. Fasting venous blood samples are collected under standard operating procedures for glycaemic, lipid, renal and liver markers, with comparable protocols across sites. Participants receive a printed results report within approximately two to three days, explained by a trained study team member; those with abnormal findings are counselled and referred to the nearest appropriate public facility, and critical values are communicated promptly with referral support. After the 24-month intervention period, end-line assessments replicate baseline procedures, using the same instruments and protocols, to enable evaluation of change over time. All outcomes are assessed at baseline and repeated at midline and endline, enabling comparison of change between the intervention and control arms.
The primary outcome is the change in health-related quality of life, measured using the EuroQol five-dimension visual analogue scale (EQ-5D VAS). The EQ-5D captures five dimensions of daily life: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. It also includes a visual analogue scale on which participants rate their overall health from 0 to 100. The change in the mean EQ-5D VAS score between the intervention and control arms is the principal measure of effectiveness.
The secondary outcomes includes clinical and cardio renal metabolic, behavioural, psychosocial and functional, and health-system and economic domains, measured with standardised instruments and laboratory methods applied consistently across sites.
Clinical and cardio renal metabolic outcomes: Blood pressure control is assessed using validated digital blood-pressure monitors. Glycaemic control is assessed using fasting blood glucose and glycated haemoglobin (HbA1c), with HbA1c analysed from EDTA samples using high-performance liquid chromatography certified to the National Glycohemoglobin Standardization Program (NGSP) and Glucose Oxidase-Peroxidase method, for fasting glucose. The lipid profile comprises total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides; these are measured using standardised enzymatic assays. Renal function is assessed using serum creatinine measured by methods traceable to isotope-dilution mass spectrometry, and liver function is assessed using standard biochemical assays. Body mass index is calculated from measured height and weight. A composite cardiovascular disease risk score is derived using an established algorithm incorporating age, blood pressure, antihypertensive treatment status, fasting glucose, lipid profile and tobacco use.
Behavioural outcomes: Tobacco use, alcohol consumption, diet and physical activity are assessed using instruments from the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) study.
Psychosocial, mental-health and functional outcomes: Psychosocial, mental-health and functional outcomes: Health-related quality of life across physical and mental domains is also measured using the twelve-item Short Form Health Survey (SF-12), complementing the EQ-5D. Depressive symptoms are measured using the Patient Health Questionnaire (PHQ-9), and anxiety is measured using the Generalised Anxiety Disorder scale (GAD-7). Disability and functioning are assessed using the twelve-item World Health Organization Disability Assessment Schedule (WHODAS 2.0). Frailty is assessed using the Fried Frailty Phenotype scale. Self-efficacy for managing chronic disease is assessed using the six-item Self-Efficacy for Managing Chronic Disease scale (SEMCD-6).
Health-system and economic outcomes: Healthcare utilisation, treatment burden and economic burden are measured using structured instruments adapted from previously validated tools. These measurements will inform a cost-effectiveness analysis of the intervention.
The cluster randomised controlled trial (cRCT) commenced on 18 May 2026 with participant screening, enrolment and baseline assessments. Following completion of baseline assessments, clusters were randomised to the intervention and control groups. The intervention was initiated in the intervention group on 13 August 2026. Midline assessments are planned for August 2027, followed by end-line assessments to evaluate implementation progress, measure health outcomes and assess intervention effectiveness.
Quality assurance and Process evaluation:
Quality is monitored in real time throughout implementation. At each site, a designated study coordinator checks enrolment logs and EDSS entries each week to confirm completeness, while monthly central monitoring audits the data by cross-checking records between REDCap and the EDSS to identify missing or inconsistent entries. A process evaluation runs alongside the trial to understand how the intervention is delivered in practice, rather than only whether it is effective. It assesses the reach, dose, fidelity and acceptability of the intervention. This includes examining how consistently the EDSS, telemedicine and patient application are used and how closely delivery follows the intended workflows. The evaluation draws on system-usage logs, fidelity checklists, supervision records and qualitative interviews with participants and providers. These findings will help interpret the trial results, identify the barriers to and enablers of implementation, and inform future scale-up.
Data Management:
Study data are managed on a common data platform that links the three digital components through a single unique participant identifier. Baseline and survey data are entered into REDCap, clinical visit and prescription data are captured in the EDSS on the DigiSetu platform, and patient-application usage data are recorded automatically; linking these through the shared identifier enables longitudinal follow-up, avoids duplicate entry, and supports unified monitoring and analysis. All data are held on secure, access-controlled systems with audit trails, and recruitment and retention are monitored monthly.
89 studies on the registry are indexed under Multiple Chronic Conditions; 16 are open to participants now.
This study's enrollment of 3,600 is above the median of 128 across 71 interventional studies indexed under Multiple Chronic Conditions.
Browse Multiple Chronic Conditions studies →Centre for Chronic Disease Control, India is the lead sponsor of 9 studies on the registry; 2 are open to participants now.
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The criteria were revised on the registry on Nov 9, 2026. See what changed
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Diagnosed with two or more of the following chronic conditions:
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Chronic obstructive pulmonary disease: long-term lung disease that makes it hard to breathe out, including emphysema and chronic bronchitis.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
The study doctor’s estimate of how long someone is likely to live. Criteria use it so that people can stay in the study long enough for it to answer its question.
COPD
Chronic obstructive pulmonary disease: long-term lung disease that makes it hard to breathe out, including emphysema and chronic bronchitis.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Life expectancy
The study doctor’s estimate of how long someone is likely to live. Criteria use it so that people can stay in the study long enough for it to answer its question.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Care in intervention PHCs is supported by a co-designed, integrated package: an electronic decision support system (EDSS) embedded in consultations, assisted telemedicine for specialist input, a patient-facing mobile application for self-management, and Care Mitras who support participants throughout their care. Medical officers and nurses use the EDSS to generate structured, guideline-based treatment plans for the patient's multiple conditions, modifiable by clinical judgement. Telemedicine is initiated from the PHC when specialist input is needed, avoiding travel. The application, linked to the EDSS, provides medication and appointment reminders and health education in local languages. Care Mitras help participants use the tools, encourage adherence and follow-up, and link them to the primary healthcare team.
Other: Electronic Decision Support System (EDSS) · Other: Assisted Telemedicine · Other: Patient-facing mobile application and care coordination by Care Mitras
The arm includes patients with multiple long-term conditions (MLTCs) who receive routine standard care at Primary Health Centres (PHCs) as per existing public health system practices, without any additional intervention components.
Evidence-based algorithms were developed for the priority conditions, including hypertension, diabetes, mental health conditions, respiratory diseases, backache, vision and hearing problems. National and low- and middle-income country (LMIC) guidelines were reviewed to create flowcharts covering the full care pathway from screening and investigations to diagnosis, treatment, referral and follow-up. After several rounds of expert review, the finalised flowcharts were translated into structured datasets and workflow variables that form the basis of the EDSS, which guides health workers step by step to deliver consistent, guideline-based care for people with multiple conditions.
Participants access specialist teleconsultations with support from PHC staff through a facility-based model. When specialist input is needed or where no medical officer is available or posted at the PHC, the nurse or medical officer initiates a video teleconsultation from the health centre, connecting the participant with a remote specialist, so expert advice is obtained locally without travelling to higher-level facilities. Structured clinical data and investigations are recorded beforehand and reviewed by the specialist with the EDSS summary, and recommendations are returned to the PHC workflow to support continuity of care.
Linked with the EDSS, the app gives participants their care plan, medications and follow-up dates, with aligned reminders, lifestyle content in low-literacy, local-language formats, with SMS and voice reminders where needed. Care Mitras help participants use the tools, encourage adherence and follow-up, and link them to the healthcare team.
Health-Related Quality of Life (EQ-5D VAS)
Mean change in health-related quality of life measured using the EuroQol Five-Dimension Visual Analogue Scale (EQ-5D VAS). Scale Range: 0 to 100, Interpretation: Higher scores indicate better health status. Unit of Measure: Score on a 0-100 scale
Time frame: Health-related quality outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Systolic Blood Pressure (SBP)
Unit: mmHg Description: Mean change in systolic blood pressure measured using validated digital monitors
Time frame: Systolic Blood Pressure outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Diastolic Blood Pressure (DBP)
Mean change in diastolic blood pressure measured using validated digital monitors
Time frame: Change in the diastolic blood pressure from baseline to 24-month endline.
Glycated Haemoglobin (HbA1c)
Mean change in the glycaemic control assessed using fasting blood glucose and glycated haemoglobin (HbA1c). Unit: % Typical Range: 4-14% Interpretation: Higher values indicate poorer glycaemic control
Time frame: HbA1C outcomes will be assessed at baseline (recruitment) and at 24 months (endline)
Fasting Plasma Glucose
Mean change in the fasting blood glucose from EDTA samples will be measured by using NGSP-certified high-performance liquid chromatography methods. Units: mg/dL
Time frame: Glycaemic control outcomes will be assessed at baseline (recruitment) and at 24 months (endline).
Total cholesterol
Mean change in the total cholesterol. Unit: mg/dL
Time frame: Total cholesterol outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Renal function (Estimated glomerular filtration rate)
Mean change in eGFR (Estimated glomerular filtration rate) and renal function assessed using serum creatinine measured with methods traceable to isotope dilution mass spectrometry, eFGR is calculated through serum creatinine. Unit: mL/min/1.73 m² Interpretation: Higher values indicate better kidney function
Time frame: Renal function outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Liver function (Total Bilirubin)
Liver function assessed using standard biochemical assays Uniits: mg/dL
Time frame: Liver function (Total Bilirubin) outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Cardiovascular risk
Composite cardiovascular risk score derived using established algorithms (e.g., WHO/ISH or Framingham-based models), incorporating age, sex, systolic blood pressure, lipid levels, diabetes status, and tobacco use. Unit: % (predicted 10-year risk) Range: 0-100% Interpretation: Higher values indicate greater cardiovascular risk
Time frame: Cardiovascular risk outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Tobacco use
Tobacco usage. Measured tool: Assessed using instruments from the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) study. Unit: Categorical (current user/non-user) or frequency
Time frame: Changes in tobacco use will be assessed at baseline (recruitment) and at 24 months (endline).
Alcohol Consumption
Measurement tool: Assessed using instruments from the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) study. Unit: Standard drinks per week.
Time frame: Changes in alcohol consumption will be assessed at baseline (recruitment) and at 24 months (endline
Diet Quality
Diet quality outcome will be assessed using tool used by Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) Measurement Tool: CARRS Diet Assessment Tool Scale Range: Typically, 0-100 (depending on scoring adaptation) Interpretation: Higher scores indicate healthier diet
Time frame: Changes in diet quality will be assessed at baseline (recruitment) and at 24 months (endline)
Physical Activity
Measurement Tool: Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) study Unit: MET-minutes/week Interpretation: Higher values indicate greater physical activity
Time frame: Changes in the Physical activity will be assessed at baseline and 24 months (endline)
Depression
Change in mean depression score will be assessed: Measurement tool: Patient Health Questionnaire (PHQ-9) Range: 0 to 27 Interpretation: Higher scores indicate more severe depression Unit: Scale score
Time frame: Mean change in PHQ-9 score (Depression) between baseline and 24 months
Disability and Functioning
Measurement tool: World Health Organization Disability Assessment Schedule 2.0 Range: 0 to 48 (raw score) or 0-100 standardized Interpretation: Higher scores indicate greater disability Unit: Scale score
Time frame: Mean change in disability and functioning scores assessed between baseline and 24-month follow-up (Endline).
Frailty score (Fried Frailty Phenotype),
Measurement tool: Frailty (Fried Frailty Phenotype) Range: 0 to 5 Categories: 0 = Robust, 1-2 =pre-frail, ≥3 = Frail Interpretation: Higher scores indicate greater frailty
Time frame: Frailty score is measured between baseline and 24 months (endline)
Self-efficacy
Measurement tool (Scale): Self-Efficacy for Managing Chronic Disease (6-item scale) Range: 1 to 10 (mean score) Interpretation: Higher scores indicate greater self-efficacy Unit: Scale score
Time frame: Self-efficacy outcome assessed between baseline and [24month] endline
Body Weight
Unit: kilograms (kg), measured by using validated measuring unit
Time frame: Body Weight outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Height
Unit: meters (m), height measurements by using the validated height measuring instrument using Stadiometer
Time frame: Height outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Body Mass Index (BMI)
Calculated as weight (kg) divided by height squared (m²), Unit: kg/m² Interpretation: Higher values indicate higher adiposity
Time frame: BMI outcome will be assessed at baseline (recruitment) and at 24 months (endline
Economic Burden
Unit: Local currency and converted to Dollar during analysis Description: Direct and indirect healthcare costs measured using a structured cost assessment tool Interpretation: Higher values indicate greater financial burden
Time frame: Change in economic burden measured between baseline and 24-month endline
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PDFs hosted by the registry, ClinicalTrials.gov.
Plan to share: Yes — All data shared for analysis and dissemination will be fully de-identified before transfer. Personal identifiers including names, addresses and contact information will be removed and replaced with a unique participant identification (PID) code, so that individual participants cannot be directly or indirectly identified. The de-identified dataset will be used solely for research and analytical purposes, and all data-handling procedures will follow applicable ethical guidelines and data-protection standards to safeguard participant confidentiality and privacy.
Supporting information: Study protocol, Statistical analysis plan, Informed consent form, Clinical study report, Analytic code
Time frame: Beginning 6 months and ending 5 years after the publication of results.
Access criteria: Only bonafide researchers with a valid research question shall be provided data. Interested researchers shall write to Prof. Prabhakaran (dprabhakaran@ccdcindia.org) with a concept note and necessary approvals as applicable.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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Centre for Chronic Disease Control, India