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National Institute for Health and Care Research Global Health Research Centre for Multiple Long-Term Conditions (NIHR-GHRC MLTC)

2026年5月7日 更新者：Dr. Dorairaj Prabhakaran、Centre for Chronic Disease Control, India

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.

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詳細な説明

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.

研究の種類

介入

入学 (推定)

3600

段階

  • 適用できない

連絡先と場所

このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。

研究連絡先

研究連絡先のバックアップ

  • 名前：Sailesh Mohan Dr, MD, PHD
  • 電話番号：+919650335597 08912500853
  • メール：smohan@ccdcindia.org

研究場所

  • インド
    • Andhra Pradesh
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Atchuthapuram Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Buchhayyapeta Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Burugupalem Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
        • コンタクト：
          • 電話番号：+919581409996
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Butchimpeta Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Cheedikada Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Chowduwada Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Chuchukonda Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Devarapalli Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Dimili Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Gavaravaram Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Golugonda Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Gullepalli Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Gunupudi Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Haripalem Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • K D Peta Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Kasimkota Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • L V Palem Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Mangavaram Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Pedagogada Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Penugollu Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Ravikamatham Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Sabbavaram Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • THAGARAMPUDI Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Thallapalem Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Thurakalapudi Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Vada Cheepurupalli Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Vaddadi Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Vechalam Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、530001
        • Vemulapudi Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
      • Visakhapatnam、Andhra Pradesh、インド、531115
        • KJ Puram Primary Health Care Center, Narsīpatnam, Anakapalli 531115
        • コンタクト：
          • Dr L V S S Prasad Pathrudu, MBBS
          • 電話番号：9490035458
    • Haryana
      • Sonīpat、Haryana、インド、131101
      • Sonīpat、Haryana、インド、131101
        • Badkhalsa, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Banwasa, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Baroda Mor, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
      • Sonīpat、Haryana、インド、131101
        • Bhatgaon, Rural primary health care centre
        • コンタクト：
          • 電話番号：+919581409996
        • コンタクト：
          • DR. ASHWANI MANN, MBBS
      • Sonīpat、Haryana、インド、131101
        • Bidhlana, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Butana, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Butana-zafrabad, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Dhatoli, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Dubeta, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Farmana, Rural primary health care centre
        • コンタクト：
          • Dr. Deepika Godia, MBBS
          • 電話番号：9837783911
      • Sonīpat、Haryana、インド、131101
        • Ferozpur-banger , Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Ganaur, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Jagsi, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Jakhauli, Rural primary health care centre
        • コンタクト：
          • Dr. Shashi Bala, MBBS
          • 電話番号：9212721086
      • Sonīpat、Haryana、インド、131101
        • Juan, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Khanda, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Khanpur- kalan , Rural primary health care centre
        • コンタクト：
          • DR HEMANT, MBBS
          • 電話番号：9416637360
      • Sonīpat、Haryana、インド、131101
        • Kharkhoda, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Kundli, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Mahra, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Mohana, Rural primary health care centre
        • コンタクト：
          • Dr. Jitender, MBBS
      • Sonīpat、Haryana、インド、131101
        • Mundlana, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Murthal, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Nahari, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Purkhas, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Rukhi, Rural primary health care centre
        • コンタクト：
      • Sonīpat、Haryana、インド、131101
        • Sargathal Shamri, Rural primary health care centre
        • コンタクト：
          • KAJAL SIWACH, MBBS
          • 電話番号：8958608388
      • Sonīpat、Haryana、インド、131101
        • Sisana, Rural primary health care centre
        • コンタクト：
    • Jodupur
      • Phalodi、Jodupur、インド、342801
        • Artiya kalla, Primary Health Care Center
        • コンタクト：
        • コンタクト：
          • Kumawat
      • Phalodi、Jodupur、インド、342801
        • Bala, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Bhatinda, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Bhavi, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Binawas, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Binjwariya, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Bisalpur
        • コンタクト：
          • DR BHUVNESH VYAS, MBBS
          • 電話番号：9414880512
      • Phalodi、Jodupur、インド、342801
        • Dangiyawas, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Danwara Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
      • Phalodi、Jodupur、インド、342801
        • Dhanari Kalla, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Gangani, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Guda vishnoiya, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Hariyadhana, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Jalupura, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Jhalamand, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Khangta, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Khawaspura, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Kherapa, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Koshana, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Narwa, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Pandit Ji Ki Dhani , Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
      • Phalodi、Jodupur、インド、342801
        • Salwa Kallan, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Salwa Khurad, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Satlana, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Sointra, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Soyla, Primary Health Care Center
        • コンタクト：
      • Phalodi、Jodupur、インド、342801
        • Tilwasani, Primary Health Care Center
        • コンタクト：
  • ネパール
    • Bagmati
      • Bharatpur、Bagmati、ネパール、44200
        • Bhandara Hospital
        • コンタクト：
      • Bharatpur、Bagmati、ネパール、44200
        • Ichchhakamana Basic Hospital
        • コンタクト：
      • Bharatpur、Bagmati、ネパール、44200
        • Kalika Municipal Hospital
        • コンタクト：
      • Bharatpur、Bagmati、ネパール、44200
        • Khairahani Municipal Hospital
        • コンタクト：
      • Bharatpur、Bagmati、ネパール、44200
        • Shivnagar PHC
        • コンタクト：
      • Bharatpur、Bagmati、ネパール、45206
      • Bharatpur、Bagmati、ネパール、45206
        • Bhimphedi PHC
        • コンタクト：
      • Bharatpur、Bagmati、ネパール、45206
      • Bharatpur、Bagmati、ネパール、45206
        • Padampokhari Municipal Hospital
        • コンタクト：
      • Bharatpur、Bagmati、ネパール、45206
      • Kathmandu、Bagmati、ネパール、1244600
        • Bishnudevi hospital
        • コンタクト：
      • Kathmandu、Bagmati、ネパール、1244600
        • Dakshinkali Nagar Hospital
        • コンタクト：
      • Kathmandu、Bagmati、ネパール、1244600
        • Nagarjung Municipal hospital
        • コンタクト：
          • Dr Shital Shrestha, MBBS
          • 電話番号：9840013499
          • メール：sital@gmail.com
      • Kathmandu、Bagmati、ネパール、1244600
      • Kavre、Bagmati、ネパール、45206
        • Banepa Municipal hospital
        • コンタクト：
      • Kavre、Bagmati、ネパール、45206
        • Bhumlu Basic Hospital
        • コンタクト：
      • Kavre、Bagmati、ネパール、45206
      • Kavre、Bagmati、ネパール、45206
        • Dhuseni Siwalaya Primary Hospital
        • コンタクト：
      • Kavre、Bagmati、ネパール、45206
      • Kavre、Bagmati、ネパール、45206
        • Panauti Municipal Hospital
        • コンタクト：
      • Kavre、Bagmati、ネパール、45206
      • Kavre、Bagmati、ネパール、45206
        • Sunthan PHC
        • コンタクト：
      • Lalitpur、Bagmati、ネパール、45206
        • Badegau PHC
        • コンタクト：
      • Lalitpur、Bagmati、ネパール、45206
        • Lele PHC
        • コンタクト：
      • Lalitpur、Bagmati、ネパール、45206
        • Lubhu PHC
        • コンタクト：
      • Lalitpur、Bagmati、ネパール、45206
      • Nuwākot、Bagmati、ネパール、45206
      • Nuwākot、Bagmati、ネパール、45206
        • Kakani PHC
        • コンタクト：
      • Nuwākot、Bagmati、ネパール、45206
      • Nuwākot、Bagmati、ネパール、45206

参加基準

研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。

適格基準

就学可能な年齢

  • 大人
  • 高齢者

健康ボランティアの受け入れ

いいえ

説明

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

研究計画

このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。

研究はどのように設計されていますか？

デザインの詳細

  • 主な目的：ヘルスサービス研究
  • 割り当て：ランダム化
  • 介入モデル：並列代入
  • マスキング：なし（オープンラベル）

武器と介入

参加者グループ / アーム
介入・治療
実験的：Intervention arm
Care delivery will be supported by a codesigned integrated intervention package comprising an electronic decision support system (EDSS) embedded within clinical consultations, assisted telemedicine enabling specialist input and a patient-facing mobile application designed to support self-management. Healthcare providers will use the decision support platform during consultations to generate structured treatment plans, which may be modified based on clinical judgement. Telemedicine consultations will be initiated when specialist input is required, and patients will receive digital reminders and educational support through the patient-facing mobile application.
他の：電子意思決定支援システム（EDSS）
高血圧、糖尿病、精神疾患、呼吸器疾患、腰痛、薬物使用、視覚・聴覚障害のためのアルゴリズムが開発されました。 研究者たちは、国内外のガイドラインおよび低・中所得国（LMIC）のガイドラインを検討し、スクリーニングや検査から診断、治療、紹介、フォローアップまで、ケアの全経路を網羅するフローチャートを作成しました。 複数の専門家によるレビューの後、最終的なフローチャートは構造化されたデータセットとワークフロー変数に変換され、EDSSの基礎を形成しました。これは、医療従事者が一貫したケアを提供するためのステップバイステップのガイドとなります。
他の：Assisted Telemedicine
Assisted telemedicine enables participants to access teleconsultations with support from health staff through a facility-based model, where patients visit PHCs and connect with remote specialist doctors via telemedicine hubs.
他の：Patient-facing app for self-management, supported by care coordination through Community Champions and Care Coordinators.
The patient-facing app enables participants to track key health indicators, receive medication and appointment reminders, and access educational content. Community champions help to develop patient networks to improve disease management and empower them in their self-care.
介入なし：Control arm
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.

この研究は何を測定していますか？

主要な結果の測定

結果測定
メジャーの説明
時間枠
Health-Related Quality of Life (EQ-5D VAS)
時間枠：Health-related quality outcome will be assessed at baseline (recruitment) and at 24 months (endline).

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
Health-related quality outcome will be assessed at baseline (recruitment) and at 24 months (endline).

二次結果の測定

結果測定
メジャーの説明
時間枠
Systolic Blood Pressure (SBP)
時間枠：Systolic Blood Pressure outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Unit: mmHg Description: Mean change in systolic blood pressure measured using validated digital monitors
Systolic Blood Pressure outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Diastolic Blood Pressure (DBP)
時間枠：Change in the diastolic blood pressure from baseline to 24-month endline.
Mean change in diastolic blood pressure measured using validated digital monitors
Change in the diastolic blood pressure from baseline to 24-month endline.
Glycated Haemoglobin (HbA1c)
時間枠：HbA1C outcomes will be assessed at baseline (recruitment) and at 24 months (endline)

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
HbA1C outcomes will be assessed at baseline (recruitment) and at 24 months (endline)
Fasting Plasma Glucose
時間枠：Glycaemic control outcomes will be assessed at baseline (recruitment) and at 24 months (endline).

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
Glycaemic control outcomes will be assessed at baseline (recruitment) and at 24 months (endline).
Total cholesterol
時間枠：Total cholesterol outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Mean change in the total cholesterol. Unit: mg/dL
Total cholesterol outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Renal function (Estimated glomerular filtration rate)
時間枠：Renal function outcome will be assessed at baseline (recruitment) and at 24 months (endline).

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
Renal function outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Liver function (Total Bilirubin)
時間枠：Liver function (Total Bilirubin) outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Liver function assessed using standard biochemical assays Uniits: mg/dL
Liver function (Total Bilirubin) outcome will be assessed at baseline (recruitment) and at 24 months (endline).
Cardiovascular risk
時間枠：Cardiovascular risk outcome will be assessed at baseline (recruitment) and at 24 months (endline)

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
Cardiovascular risk outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Tobacco use
時間枠：Changes in tobacco use will be assessed at baseline (recruitment) and at 24 months (endline).

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
Changes in tobacco use will be assessed at baseline (recruitment) and at 24 months (endline).
Alcohol Consumption
時間枠：Changes in alcohol consumption will be assessed at baseline (recruitment) and at 24 months (endline

Measurement tool: Assessed using instruments from the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) study.

Unit: Standard drinks per week.
Changes in alcohol consumption will be assessed at baseline (recruitment) and at 24 months (endline
Diet Quality
時間枠：Changes in diet quality will be assessed at baseline (recruitment) and at 24 months (endline)
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
Changes in diet quality will be assessed at baseline (recruitment) and at 24 months (endline)
Physical Activity
時間枠：Changes in the Physical activity will be assessed at baseline and 24 months (endline)
Measurement Tool: Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) study Unit: MET-minutes/week Interpretation: Higher values indicate greater physical activity
Changes in the Physical activity will be assessed at baseline and 24 months (endline)
Depression
時間枠：Mean change in PHQ-9 score (Depression) between baseline and 24 months

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
Mean change in PHQ-9 score (Depression) between baseline and 24 months
Disability and Functioning
時間枠：Mean change in disability and functioning scores assessed between baseline and 24-month follow-up (Endline).
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
Mean change in disability and functioning scores assessed between baseline and 24-month follow-up (Endline).
Frailty score (Fried Frailty Phenotype),
時間枠：Frailty score is measured between baseline and 24 months (endline)
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
Frailty score is measured between baseline and 24 months (endline)
Self-efficacy
時間枠：Self-efficacy outcome assessed between baseline and [24month] endline
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
Self-efficacy outcome assessed between baseline and [24month] endline
Body Weight
時間枠：Body Weight outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Unit: kilograms (kg), measured by using validated measuring unit
Body Weight outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Height
時間枠：Height outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Unit: meters (m), height measurements by using the validated height measuring instrument using Stadiometer
Height outcome will be assessed at baseline (recruitment) and at 24 months (endline)
Body Mass Index (BMI)
時間枠：BMI outcome will be assessed at baseline (recruitment) and at 24 months (endline
Calculated as weight (kg) divided by height squared (m²), Unit: kg/m² Interpretation: Higher values indicate higher adiposity
BMI outcome will be assessed at baseline (recruitment) and at 24 months (endline
Economic Burden
時間枠：Change in economic burden measured between baseline and 24-month endline
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
Change in economic burden measured between baseline and 24-month endline

協力者と研究者

ここでは、この調査に関係する人々や組織を見つけることができます。

スポンサー

Centre for Chronic Disease Control, India

協力者

University of Leicester
Kathmandu Medical College and Teaching Hospital
All India Institute of Medical Sciences, Jodhpur

捜査官

  • 主任研究者：Kamlesh Khunti, MD, DM、University of Leicester
  • 主任研究者：Prabhakaran Dorairaj, MD, DM、Center for Chronic Disease Control

研究記録日

これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。

主要日程の研究

研究開始 (推定)

2026年5月5日

一次修了 (推定)

2026年7月30日

研究の完了 (推定)

2028年8月30日

試験登録日

最初に提出

2026年4月25日

QC基準を満たした最初の提出物

2026年5月7日

最初の投稿 (実際)

2026年5月12日

学習記録の更新

投稿された最後の更新 (実際)

2026年5月12日

QC基準を満たした最後の更新が送信されました

2026年5月7日

最終確認日

2026年5月1日

詳しくは

本研究に関する用語

キーワード

追加の関連 MeSH 用語

その他の研究ID番号

  • NIHR203257

個々の参加者データ (IPD) の計画

個々の参加者データ (IPD) を共有する予定はありますか?

はい

IPD プランの説明

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.

IPD 共有時間枠

01-09-2026 till 30-12-2027

IPD 共有アクセス基準

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.

IPD 共有サポート情報タイプ

  • STUDY_PROTOCOL
  • SAP
  • ICF
  • ANALYTIC_CODE
  • CSR

医薬品およびデバイス情報、研究文書

米国FDA規制医薬品の研究

いいえ

米国FDA規制機器製品の研究

いいえ

この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。

購読する