Multiple Long-Term Conditions (MLTC), defined as the coexistence of two or more chronic conditions, is increasingly prevalent in India. Despite this, the healthcare system remains largely focused on single-disease management, underscoring the urgent need for integrated, patient-centred approaches that are context-specific, equitable, and resource-sensitive.

India's public health infrastructure is undergoing significant reform through the Ayushman Bharat Yojana, which aims to upgrade 150,000 sub-centres and primary health centres into Health and Wellness Centres (HWCs). These centres are designed to provide comprehensive care including prevention, treatment, and rehabilitation to underserved populations. This transformation presents a strategic opportunity to embed multi-morbidity care into the evolving system, supported by the establishment of a Global Health Research Centre dedicated to MLTC.

The NIHR Global Health Research Centre for Multiple Long-Term Conditions aims to transform the health system in India and Nepal by improving care for individuals living with MLTC. With chronic conditions on the rise due to epidemiological transitions, particularly among adults aged ≥40, there is an urgent need for integrated, people-centred care models. This project is being implemented in Anakapalli district (Andhra Pradesh), Jodhpur (Rajasthan), Sonipat (Haryana) and Nepal, encompassing both rural and urban contexts.

The study is conducted among adult patients with MLTCs attending rural primary providers (Medical officers and Staff Nurse) delivering services at these facilities in India and Nepal. The intervention comprises an electronic decision support system (EDSS) to facilitate evidence-based clinical decision-making, assisted telemedicine model to enable timely specialist consultations, and a patient-facing mobile application-supported by community champions and care coordinators to enhance care coordination, self-management, and treatment adherence.

At this stage, we have completed the case-mix and health facility assessments, alongside the in-depth interviews to identify challenges faced by patients, caregivers, and health care providers. Currently, we are pilot testing the health intervention in 4 PHCs in India and 2 PHCs in Nepal among 180 participants (30 participants per site). Findings from this pilot will inform refinement of the intervention, study tools, and implementation strategies, and will provide critical evidence on contextual adaptability to support the design of a subsequent cluster randomized controlled trial (RCT).

In the full RCT, we will evaluate the effectiveness of a health system intervention comprising an electronic clinical decision support system, assisted telemedicine, a patient-facing application, and community champions. The study will be conducted across selected sites in India and Nepal using a cluster randomized controlled design, in which Primary Health Centres (PHCs) are allocated to either the intervention or usual care arm. The intervention includes structured clinical workflows, a digital decision support system, assisted telemedicine, and patient-facing mobile health tools to strengthen continuity and coordination of care.

Participants will engage with these components over a six-month implementation period. Data collection will include participant surveys and qualitative interviews, complemented by routine supervision checklists and system usage analytics to assess implementation processes and usability.

The study findings will generate robust evidence to inform scalable and context-appropriate models of integrated care for multiple long-term conditions (MLTCs) in primary care settings in low- and middle-income countries. By embedding digital tools and strengthening PHC systems, the intervention aims to improve quality of life, reduce fragmentation of care, and establish a sustainable model for MLTC management.

Implementation framework and study design: This study uses a cluster randomized controlled design in rural primary health centres to test an integrated digital health program for people with multiple long-term conditions. The main outcome is health-related quality of life, measured using the EuroQol five-dimension scale (EQ 5D). This tool asks participants about five areas of daily life mobility, self-care, usual activities, pain or discomfort, and anxiety or depression and includes a visual scale where people rate their overall health. Sample size calculations accounted for clustering at the facility level. The assumptions were a significance level of 0.05, statistical power of 90 percent, a mean score of 74.37 in the control group, a standard deviation of 15.1, a minimum detectable difference of 2.5 points, an intraclass correlation of 0.02, and a 20 percent attrition rate. Based on these parameters, the final sample size is 120 centres with 30 participants in each, giving a total of 3,600 participants. After recruitment, centres will be randomly assigned to either the intervention group or the control group. Participants in the intervention centres will receive the digital health program for 24 months, while those in the control centres will continue with routine care. The trial will be conducted over a 24 month period across selected primary health centres: thirty in Andhra Pradesh, thirty in Rajasthan, thirty in Haryana, and thirty in Nepal. A pilot phase is currently underway in a subset of centres two each in Andhra Pradesh, Rajasthan, and Nepal to refine implementation processes. This includes workflow integration, training delivery, and technical specifications. The intervention package consists of four components: (i) an electronic decision support system to incorporate evidence-based management of multiple long-term conditions into primary health centre workflows; (ii) assisted telemedicine, using both a fixed "hub" model and a portable "backpack" kit, to connect patients and health workers with remote specialists; (iii) a patient-facing mobile application to support self-management through education, reminders, and messaging; and (iv) trained community health champions to strengthen links between the health system and the community.

Co-Design and intervention development: The core intervention components were iteratively co-designed with stakeholders across three sites in India (Jodhpur, Rajasthan; Anakapalli, Andhra Pradesh) and one in Nepal. Over 15 co-design workshops were conducted between December 2024 and early 2026, culminating in a national codesigning workshop in New Delhi. In workshop participants were stratified into stakeholder groups to ensure broad representation: Group A (patients with MLTC and their caregivers/community representatives), Group B (primary healthcare providers, technical experts, and researchers), and Group C (policy makers/district/state officials). Workshops were held in accessible community venues (and online for policy makers) with careful advance mapping and consent of participants. Trained facilitators guided semi-structured discussions using journey mapping, brainstorming, voting/prioritization exercises, and live demonstrations of prototype technologies. These activities elicited user needs and system requirements which directly shaped the intervention package. Group A workshops (patients/caregivers) identified critical user preferences (e.g. trusted provider communication, self-care support, and community champions) and barriers (disappointment with fragmented care, out-of-pocket costs). Group B workshops (providers/experts) yielded practical design recommendations, such as integrating clinical guidelines into workflows, incorporating drug-interaction alerts, and defining standard teleconsultation formats with language and trust considerations. A joint workshop with both Groups A and B validated and prioritized intervention features: for example, "must-have" features included an editable EDSS dashboard, simple app navigation in local languages, offline data entry, and a reliable telemedicine referral pathway. Feedback on the patient-facing application emphasized low-literacy formats (audio/video, SMS/IVR options) and event-triggered reminders. Throughout, emerging insights were documented and fed back into design cycles ("design" and "adapt" phases of the ADAPT framework), ensuring that the EDSS algorithms, telemedicine workflows, and mHealth app reflected local context, language, and health system realities. In summary, the co-design process ensured that the intervention components are grounded in stakeholder experience and health system constraints. The final intervention package consists of an Electronic Decision Support System (EDSS), assisted telemedicine models (facility-based and portable "backpack" models), and a patient-facing mobile application, complemented by trained community champions and strengthened referral pathways. The co-design phase also produced stakeholder engagement structures (e.g. community advisory boards) and preparatory materials (training modules, user manuals) that will underpin implementation. Further, minor refinements to technical specifications (algorithm logic, user interfaces, and data flows) are being informed by ongoing pilot implementation, without altering the core intervention components of the RCT.

Workflow Integration at PHC Level: The EDSS is integrated into routine outpatient workflows, rather than functioning as a parallel system. Nurses and officers are instructed to use the system during normal clinical hours (e.g. during patient intake and consultation). For each patient encounter, PHC staff complete all mandatory fields in the EDSS before submitting the encounter. Usage logs (timestamps of logins, data entries, referral triggers) are captured continuously on the DigiSetu back-end and synchronized daily, creating an audit trail. Supervisors review log data weekly to ensure adherence to protocol. To support these workflows, standard operating procedures (SOPs) have been developed for each task. SOPs detail: (a) Case identification and case-mix classification (how to use the screening tool and record diagnoses); (b) Data collection protocols (guidance on REDCap and EDSS data entry, use of unique patient IDs); (c) Telemedicine workflow (criteria for tele-referral, scheduling process, documentation of consult notes); and (d) Patient app enrolment. These SOPs were co-created with implementers and iteratively refined during pilot workshops. For example, telemedicine SOPs explicitly define "who to refer" (e.g. uncontrolled hypertension or diabetes after 3 medication trials) and "when not to refer" e.g. acute emergencies). All staff nurses and MOs receive printed job aids summarizing key steps for each component (screenshots of EDSS pages, referral algorithms, consent checklists), which are reviewed during training.

Procedures and delivery workflow: Participants will enrol through a structured visit-based approach at participating primary health centres. During wave 1, trained health workers will screen all adults aged ≥40 years using a standardized eligibility tool to identify individuals with two or more chronic conditions consistent with MLTCs. Eligibility screening will include confirmation of diagnosed conditions and basic demographic information (such as village name, phone number). Individuals meeting eligibility criteria will receive study information and will be invited to provide written informed consent. Wave 2 will serve as the baseline assessment visit and will be conducted after obtaining written informed consent. During this visit, trained research staff will conduct comprehensive baseline evaluations using standardized interviewer administered questionnaire. Data collected will include socio-demographic characteristics, medical history, and behavioural risk factors. Objective clinical measurements will include systolic and diastolic blood pressure and anthropometry (height, weight, and body mass index). Behavioural and patient reported outcomes will be assessed using validated instruments, including diet quality, physical activity, tobacco and alcohol use, depressive symptoms (PHQ-9), anxiety (GAD-7), health-related quality of life (EQ-5D), disability (WHODAS 2.0), frailty measures, self-efficacy, and treatment burden. These baseline measurements will serve as reference values for evaluating changes in predefined clinical, behavioural, and patient-reported outcomes at follow up. Wave 3, Fasting venous blood samples will be collected following standard operating procedures. Laboratory analyses will include glycaemic markers (fasting blood glucose and HbA1c), lipid profile, liver function tests and renal function tests, using standardised protocols to ensure comparability across sites. Participants will receive their test results within approximately 2 to 3 days of sample collection. Results will be provided as a printed report. A trained member of the study team (nurse, CCDC health worker) will explain the results to participants. Participants with abnormal findings will be counselled and referred to the nearest appropriate public health facility (e.g., PHC/CHC/District Hospital) for further evaluation and management as per standard care pathways. In cases of significantly abnormal or critical values, participants will be informed promptly and advised to seek immediate medical care, with the study team facilitating referral where feasible. The duration of intervention up to 12 to 18 months. End line assessments will replicate baseline procedures to enable evaluation of changes over time. Follow up data will be collected using the same standardized instruments and clinical protocols, ensuring consistency across timepoints and study sites.

Training and capacity building: All healthcare providers in intervention PHCs (medical officers, staff nurses, and auxiliary nurse-midwives) will undergo comprehensive training on the intervention components prior to RCT implementation. The training programme consists of a 3-4-day in-person workshop co-facilitated by clinical, public health, and digital health experts. The curriculum was co-developed by a multi-disciplinary Course Advisory Committee (45 members including clinicians, technologists, and community representatives) to cover: MLTC care principles, EDSS operation, telemedicine processes, and patient app overview. Training methods include lectures, interactive demonstrations of EDSS and app mock-ups, hands-on practice in simulation labs, and case scenario role-plays. Pre- and post-tests assess knowledge and confidence. A cascade training model will be employed: initially, "master trainers" (e.g. site investigators, district NCD programme officers) receive intensive instruction, then they train the PHC teams locally. State health authorities are engaged from the outset to embed the training into routine NCD programme capacity building. Custom training manuals and quick-reference job aids (in local languages) were developed and distributed to all trainees. For example, printed flowcharts outline the step-by-step process of a telemedicine consult or patient enrollment in the app. Training attendance and performance are tracked via checklists. In the initial pilot phase, 27 PHC staff (mostly nurses) completed the pilot training with post-training evaluation; similar numbers will be trained in Nepal. Refresher sessions are scheduled at 3 months, supplemented by on-site mentoring visits from research staff. Beyond initial implementation, ongoing capacity building is integrated into the project. Primary Health Centre teams participate in monthly learning sessions with research staff, sharing challenges and solutions. A district-level supervisory structure is in place: each PHC is paired with a mentor (a senior nurse or physician) who conducts quarterly site visits to review fidelity checklists, observe practice, and provide feedback. In parallel, research field coordinators receive training in Good Clinical Practice (GCP), data management, and participant engagement, with continuous skill-building over the course of the study. Community Champions and members of newly formed Community Advisory Boards (CABs) at each site (60 members across 6 pilot PHCs) also undergo training in MLTC awareness and community engagement strategies, ensuring local ownership and sustainability. A pilot phase of the training is currently underway in a subset of PHCs to refine training materials and delivery approaches. Insights from this phase are being incorporated into the final training strategy for the full RCT rollout.

Intervention Components and digital architecture: The EDSS is built on the CCDC's DigiSetu platform, expanding prior modules (hypertension, diabetes, CVD) to cover MLTC-relevant conditions (e.g. asthma, osteoarthritis, mental health, sensory impairments, substance use). It provides a structured clinical workflow at the PHC: nurses enter patient vitals, history and lab results into the EDSS; the system generates guideline-based treatment plans; and medical officers review, override if needed, and finalize management. The EDSS features an at-a-glance dashboard showing key diagnoses, risk status, pending follow-ups and alerts for missed visits or deterioration. Key design features include offline data entry with automatic syncing (for low-connectivity settings), state-aligned essential-drug databases (with the ability for PHC staff to update availability), and risk-stratification algorithms that flag high-risk patients and guideline-based referral criteria. The EDSS is explicitly designed as an assistive tool - clinicians retain full override authority to exercise their judgment. Back-end audit trails log every action and decision for monitoring. The assisted telemedicine component has two models: a facility-based model providing real-time specialist consultations within the PHC (via teleconference) and a portable "backpack" model enabling outreach to remote community settings. In both models, nurses or mid-level providers collect structured clinical data and basic investigations prior to the teleconsult, reducing physician cognitive burden. The telemedicine platform integrates electronic health records (EDSS data), point-of-care diagnostics (e.g. glucometer, digital stethoscope), and decision support summaries. Care pathways are defined by SOPs (e.g. which patients qualify for tele-referral, how consultations are scheduled and documented). Quality features include offline scheduling with sync (to avoid cancelled consults), and a PPP-based pool of specialists to improve availability (with defined incentives and schedules). All tele consult requests and outputs (prescriptions, specialist recommendations) are logged and routed back into the PHC workflow to reinforce continuity of care. Importantly, prescriptions are automatically checked against PHC stock - the system will flag if a specialist-recommended drug is unavailable, minimizing patient out-of-pocket costs. The patient-facing mobile application (the Ai.M Healthy app by ClinAlly) supports MLTC self-management. Core functions include linkage with the national ABHA Health ID (to import health records securely), personalized medication and visit reminders, symptom tracking, and a content library of lifestyle and adherence support. Based on co-design feedback, the app uses audio-visual, low-literacy content (short videos and interactive prompts) in local languages. Users can log self-reported behaviors via simple yes/no/tick inputs, triggering context-specific feedback. The app is "event-triggered" rather than continuously burdensome: notifications occur around clinic visits, medication changes, or scheduled follow-ups. For patients without smartphones, the system falls back on SMS/IVR reminders and engages caregivers or frontline workers (ASHAs/ANMs) to relay key messages. Critically, the app is interoperable with EDSS and telemedicine records, for example, it displays the patient's current care plan and follow-up dates, so reminders align with the PHC's instructions. Overall, the intervention is implemented on a secure, cloud-enabled platform compliant with national digital health standards. Data entry at PHCs and in the patient app is encrypted end-to-end and stored on secure servers. The architecture follows the WHO digital health evaluation framework: it is assessed for technical/infrastructure fit (offline sync, data security, interoperability) and workforce/workflow fit (user interface design aligned with OPD routines). System readiness was confirmed in a prior phase: health facility assessments at 20 PHCs (using IPHS 2022 standards) highlighted gaps which the intervention explicitly addresses (e.g. provision of digital tablets, training on record-keeping). In sum, the digital tools are fully integrated into PHC workflows rather than operating in parallel, with APIs linking EDSS, telemedicine, and patient app data to minimize duplication.

Quality Assurance and Supervision: A robust quality assurance (QA) system is established. Supervision protocols require real-time monitoring of key processes. At each PHC, a designated study coordinator conducts weekly reviews of enrollment logs and EDSS entries to verify completeness. Monthly centralized monitoring by the research center includes data audits: for example, random records are cross-checked between REDCap and EDSS to detect missing or discrepant entries. The EDSS platform automatically generates backend audit trails for every user action. These logs feed into structured fidelity checklists developed from Carroll's framework. Performance indicators (e.g. % of EDSS encounters with all mandatory fields, % of patients referred per protocol) are compiled into dashboards for review. Supervisors observe at least 10 patient encounters per PHC during the pilot to assess "quality of delivery" e.g. whether MOs appropriately justify any EDSS plan modifications.

Data management All quantitative data are collected using secure electronic systems with audit trails. Baseline and survey data are entered into REDCap at point-of-care. EDSS and telemedicine encounter data are logged in DigiSetu with unique participant IDs. The patient app usage data (log-ins, reminder responses) are capture. A single codebook defines all variables across platforms. To minimize missing data, all critical fields are mandatory in the digital forms; research staff are trained to resolve missing items immediately by direct inquiry. At the central office, periodic data checks identify missing or inconsistent values; statistical imputation (e.g. multiple imputation for random missingness) will be applied if needed during analysis to ensure valid inferences. The ongoing pilot phase includes approximately 30 participants per PHC (total ~180 participants) and is intended to assess feasibility, data completeness, and implementation processes rather than effectiveness outcomes. Loss-to-follow-up is expected to be low given the 6-month duration; all efforts (e.g. multiple contact methods, community follow-up) will be used to minimize attrition. Recruitment and retention rates will be monitored monthly.

Evaluation of outcomes: Primary outcome: The primary outcome is change in health related quality of life, assessed using the EQ-5D visual analogue scale (EQ-5D VAS). Secondary outcome: Clinical outcomes: clinical outcomes will include cardiometabolic and anthropometric measures collected using standardized protocols. 1. Blood pressure control measured using validated digital blood pressure monitors. 2. Glycaemic control assessed using fasting blood glucose and glycated haemoglobin (HbA1c). HbA1c will be analysed from EDTA samples using NGSP-certified high-performance liquid chromatography methods. 3. Lipid profile including total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides measured using standardized enzymatic assays 4. Renal function assessed using serum creatinine measured with methods traceable to isotope dilution mass spectrometry. 5. Liver function assessed using standard biochemical assays. 6. Body mass index calculated from measured height and weight. 7. Cardiovascular risk: A composite cardiovascular disease risk score will be derived using established algorithms incorporating age, blood pressure, antihypertensive medication treatment status, fasting glucose, lipid profile, and tobacco use. 8. Tobacco use and alcohol consumption assessed using Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) study instruments. 9. Diet quality and physical activity assessed using PCARRS-based tools. 10. Health related quality of life, encompassing both physical and mental health domains measured by SF-12 questionnaire. 11. Depression measured using the Patient Health questionnaire (PHQ-9) 12. Anxiety measured using the Generalized Anxiety Disorder scale (GAD-7) 13. Disability and functioning assessed using the WHO Disability Assessment Schedule (WHODAS 2.0, 12-item). 14. Frailty assessed using the Fried Frailty Phenotype scale. 15. Self-efficacy assessed using the Self-Efficacy for Managing Chronic Disease (6-item scale). 16. Health system and economic outcomes: Health system and economic outcomes will include healthcare utilization, treatment burden, and economic burden. These will be measured using structured instruments adapted from previously validated tools and will inform cost-effectiveness analyses of the intervention.

Inclusion Criteria:

• Adults aged 40 years or above

• Attending the Primary Health Centre (PHC) during the enrollment period

• Diagnosed with two or more of the following chronic conditions:

  1. Hypertension
  2. Diabetes mellitus
  3. Depression
  4. Anxiety
  5. Chronic obstructive pulmonary disease (COPD)
  6. Asthma
  7. Vision impairment
  8. Hearing impairment
  9. Osteoarthritis
  10. Chronic back pain

Exclusion Criteria:

• Age < 40 years
• Presence of only one or none of the listed chronic conditions
• Pregnant or breastfeeding women
• Severe cognitive impairment or dementia that prevents informed consent or or reliable participation
• Bedridden or terminally ill individuals with a life expectancy < 6 month
• Current participation in another clinical or interventional research study may interfere with study outcomes
• Severe psychiatric illness (e.g., psychosis or bipolar disorder) other than depression or anxiety
• Unable or unwilling to provide written informed consent
• Severe communication barriers that prevent participation in interviews or questionnaires, even with assistance

All data shared for the purposes of analysis and dissemination will be fully de-identified prior to transfer. Personal identifiers, including names, addresses, and contact information, will be removed from the dataset and replaced with a unique participant identification (PID) code. This process will ensure 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 adhere to applicable ethical guidelines and data protection standards to ensure confidentiality and privacy of participants.

01-09-2026 till 30-12-2027

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.

During recruitment and baseline assessment, data collectors are unaware of future allocation, as randomization occurs only after completion of recruitment. This ensures that baseline data collection is conducted without knowledge of group assignment. Randomization is performed at the cluster (Primary Health Centre) level, with allocation concealment maintained during the process to prevent selection bias.

Following allocation, the study is open-label, and participants as well as providers are aware of their assigned group. However, outcome assessment relies on standardized tools and objective measurements, and data analysis may be conducted by analysts blinded to allocation, where feasible.

No additional parties beyond those described above are masked in this trial.