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| Name | Class |
|---|---|
| Wellcome Trust | OTHER |
| London School of Hygiene and Tropical Medicine | OTHER |
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Major barriers to controlling cardiovascular diseases (CVDs) in India and elsewhere are: low detection rates, inadequate use of evidence based interventions and low adherence with these interventions. Primary health care is the appropriate setting for improving the prevention and management of these chronic conditions. The investigators will develop and evaluate an innovative mobile health (mHealth) software application -'m-WELLCARE'- which provides a patient health profile, decision support for clinical care, monitoring and feedback for use in Indian Community Health Centers (CHCs). The investigators will conduct this research following the steps proposed by the medical research council (MRC) for evaluation of complex interventions. Technical development of m-WELLCARE will be conducted, user acceptability appraised and potential barriers overcome. m-WELLCARE will be evaluated in CHCs of two states, Haryana and Karnataka. The use made of m-WELLCARE, its impact on patterns of health care received and changes in risk factors achieved will be evaluated.
Cardiovascular disease (CVD) and diabetes are the leading causes of premature (<60 years) adult deaths in India with projections indicating an almost 3-fold increase to 18 million premature years of life lost by 2030. CVD and diabetes will result in $336.6 billion in lost national income in India over the next decade. The major barriers to the control of these conditions in India are the low detection rates early in the course of the condition, inadequate use of evidence based interventions and low adherence with these interventions. After detection of these conditions, the long-term health outcomes of persons affected is heavily dependent on adherence with care guidelines and is a major priority.
Harnessing the potential of Smartphone technology would be a solution for addressing these challenges at the community level by improving the quality of care. There are several advantages for Smartphone technology that makes it an ideal tool for improving the quality care at the government facilities. Smartphones/tablet computers are low-cost, requires less investment in infrastructure and are ubiquitous used by the masses.
Primary health care settings are best suited to address the prevention and management of hypertension/diabetes and its risk factors. Given these reasons, the Government of India, is planning to scale-up the National Program on prevention and control of cancer, diabetes, cardiovascular diseases and stroke (NPCDCS) giving a major thrust to screening, diagnosis and management of hypertension and diabetes at community level by starting NCD clinics at the Community Health Centres and assigning new roles to the Health Workers at the sub-centers.
In the above context, the investigators plan to develop a tablet computer application for the Medical Officers and Nurse enabling them to deliver high quality care at Community Health Centres (CHCs). The tablet computer application will be capable of running clinical risk scores for identifying people at high risk of diabetes, cardiovascular disease, and computing personalized management plan using evidence-based clinical management guidelines. The feasibility and effectiveness of such a novel application is to be formally evaluated in order to develop a robust clinical decision support system for the Nurses and Medical Officers at the public health facilities.
In brief, the investigators plan to implement the research project in the 20 CHCs each in 2 states in India i.e.Haryana and Karnataka. Out of these, 10 CHCs will receive the mWellcare interventions. In the intervention arm, the NPCDCS Nurses will register 30 years+ patient diagnosed with hypertension and diabetes using tablet computer based Decision Support Software (DSS). For the patients identified with hypertension/diabetes or at high risk, the software will provide individual tailored management plan that would include treatment plan, lifestyle advice and follow up schedule. Thus, Medical Officers at CHCs will be able to prescribe a guideline based management plan for these patients with the help of DSS. The software will store relevant health parameters of patients at local database (tablet computer) and central server that could be accessed during the follow-up visits of the patients or whenever required.
To make meaningful comparison on the impact of the new technology enabled services in improving the quality of care of diabetes and hypertension at the CHCs, the investigators will collect data from 10 more CHCs that provide routine/usual care to the patients. A structured training will be conducted for Medical Officers and Nurse at CHCs (both from the intervention & usual care arm) on evidence based management of hypertension and diabetes prior to the start of the project.
The intervention will be carried out for a period of 1 year that would include regular follow up. The effect of the intervention will be assessed at six and twelve month comparing the blood pressure and blood glucose values of the patients in two groups.
This project has considerable importance because of the fact that the utility of tablet computer based clinical decision support systems in the management of hypertension and diabetes at the public health facilities has not been evaluated in developing countries so far. If found successful, the technology has the potential to be upscale not only in Haryana and Karnataka but across the country in government and private healthcare settings.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| mWELLCARE software arm | Experimental | The doctor and nurse care coordinators (NCCs) in the mWELLCARE intervention arm will be trained on the use of mWELLCARE software loaded on a tablet computer. Patients diagnosed with hypertension and/or diabetes will be registered by the nurse using mWellcare application. The nurse will record patient parameters, medical history, medication etc and generate a management plan (including drug recommendation, lifestyle advise) using the mWellcare application based on standard treatment guidelines. The doctor will review the recommendation and agree or disagree giving reasons. Patient will be followed up using SMS. |
|
| Usual care arm | Active Comparator | In the control arm or the usual care arm CHCs, the doctor and Nurse will get "refresher" training in the detection, management and follow up of hypertension and diabetes patients based on standard guidelines. They will be provided with charts for quick reference to standard treatment guidelines. Patients diagnosed with hypertension and/or diabetes will be managed by the doctor at the CHC. The nurse will assist in recording blood pressure, height, weight etc, providing lifestyle advise and follow up advice to patients. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| mWELLCARE | Other | mWELLCARE intervention arm will include a software application loaded on a tablet computer that will be used by Nurse Care Coordinators (posted in community health centers) in the course of their jobs to register patients with hypertension or diabetes, to generate clinical decision support recommendations, to track these patients over time and to improve follow-up care. Decision support recommendations will be printed and given to a doctor, who will make the final call on the management plan that will be used for the patient. Registered patients will also receive customized messages on their mobile phone. In addition, at sites where network connectivity permits, the doctor may also be equipped with a doctor's app on a tablet that will be largely the same as the NCC app. |
| Measure | Description | Time Frame |
|---|---|---|
| Systolic blood Pressure | Difference in mean change in systolic blood pressure between the two treatment arms | Baseline and 12 months |
| Glycated haemoglobin (HbA1c) | Difference in mean change in glycated haemoglobin(HbA1c) between the two treatment arms | Baseline and 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| Depression | Proportion of patients with moderate and severe depression measured using PHQ-9 score | Baseline and 12 months |
| Smoking | proportion of smokers |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Dorairaj Prabhakaran | Public Health Foundation of India | Principal Investigator |
| Vikram Patel | London School of Hygeine and Tropical Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| CHC Assandh | Āsandh | Haryana | 132039 | India | ||
| CHC Ballah |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 19382860 | Background | Krishna S, Boren SA, Balas EA. Healthcare via cell phones: a systematic review. Telemed J E Health. 2009 Apr;15(3):231-40. doi: 10.1089/tmj.2008.0099. | |
| 18212285 | Background | D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008 Feb 12;117(6):743-53. doi: 10.1161/CIRCULATIONAHA.107.699579. Epub 2008 Jan 22. |
| Label | URL |
|---|---|
| Curioso, W., New technologies and public health in developing countries: the Cell PREVEN project, in The Internet and health care: theory, research and practice, M. Murero and R. Rice, Editors. 2006, Lawrence Erlbaum Associates: Mahwah (NJ). | View source |
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|
|
| Usual Care | Other | Usual care at the community health centers |
|
| Baseline and 12 months |
| Body Mass Index (BMI) | Difference in BMI | Baseline and 12 months |
| Alcohol use | Change in alcohol use to be measured using WHO- AUDIT questionnaire | Baseline and 12 months |
| Fasting blood sugar | Difference in mean change in fasting blood sugar | Baseline and 12 months |
| Total cholesterol | Difference in mean change in total cholesterol | Baseline and 12 months |
| CVD risk | Difference in mean change in predicted 10 year risk of cardiovascular disease using re-caliberated Framingham Risk Score | Baseline and 12 months |
| Cost | Costs associated with delivering intervention compared to usual care | Baseline and 12 months |
| Ballah |
| Haryana |
| 132040 |
| India |
| CHC Brara | Brara | Haryana | 133201 | India |
| CHC Chauramastpur | Chauramastpur | Haryana | 134003 | India |
| CHC Gharaunda | Gharaunda | Haryana | 132114 | India |
| CHC Indri | Indri | Haryana | 132041 | India |
| CHC Jhansa | Jhānsa | Haryana | 136130 | India |
| CHC Ladwa | Lādwa | Haryana | 136132 | India |
| CHC Mathana | Mathāna | Haryana | 136131 | India |
| CHC Mullana | Mullana | Haryana | 133207 | India |
| CHC Mustafabad | Mustafābād | Haryana | 133103 | India |
| CHC Naharpur | Nāharpur | Haryana | 135001 | India |
| CHC Nilokheri | Nīlokheri | Haryana | 132116 | India |
| CHC Nissing | Nīsang | Haryana | 132024 | India |
| CHC Pehowa | Pehowa | Haryana | 136128 | India |
| CHC Radaur | Radaur | Haryana | India |
| CHC Sadhaura | Sādhaura | Haryana | 133204 | India |
| CHC Shahzadpur | Shahzādpur | Haryana | 134202 | India |
| CHC Shahbad | Shāhābād | Haryana | 136135 | India |
| CHC Taraori | Tirāwari | Haryana | 132116 | India |
| CHC Anandapuram | Anantapur | Karnataka | 577412 | India |
| CHC Aynur | Aynur | Karnataka | 577221 | India |
| CHC Anavatti | Ānavatti | Karnataka | 577413 | India |
| Taluk Hospital Bhadravathi | Bhadravathi | Karnataka | 577301 | India |
| CHC CN Halli | CN Halli | Karnataka | 572214 | India |
| CHC Gubbi | Gubbi | Karnataka | 572216 | India |
| CHC Holehonnuru | Holehonnuru | Karnataka | 577227 | India |
| Taluk Hospital Hosanagara | Hosanagara | Karnataka | 577418 | India |
| CHC Kannangi | Kannangi | Karnataka | 577226 | India |
| General Hospital Koratagere | Koratagere | Karnataka | 572129 | India |
| General Hospital Kunigal | Kunigal | Karnataka | 572130 | India |
| CHC M.N.Kote | M.N.Kote | Karnataka | 572222 | India |
| General Hospital Madhugiri | Madhugiri | Karnataka | 572132 | India |
| General Hospital Pavagada | Pāvugada | Karnataka | 561202 | India |
| Taluk Hospital Sagar | Sāgar | Karnataka | 577401 | India |
| CHC Shiralkoppa | Shiralkoppa | Karnataka | 577428 | India |
| General Hospital Sira | Sīra | Karnataka | 572137 | India |
| CHC Kannangi | Thirthahalli | Karnataka | 577432 | India |
| General Hospital Tiptur | Tiptūr | Karnataka | 572201 | India |
| CHC Turuvekere | Turuvekere | Karnataka | 572227 | India |
| 20639295 | Background | Chalkidou K, Levine R, Dillon A. Helping poorer countries make locally informed health decisions. BMJ. 2010 Jul 16;341:c3651. doi: 10.1136/bmj.c3651. No abstract available. |
| 17597964 | Background | Wee HL, Loke WC, Li SC, Fong KY, Cheung YB, Machin D, Luo N, Thumboo J. Cross-cultural adaptation and validation of Singapore Malay and Tamil versions of the EQ-5D. Ann Acad Med Singap. 2007 Jun;36(6):403-8. |
| 30586732 | Derived | Prabhakaran D, Jha D, Prieto-Merino D, Roy A, Singh K, Ajay VS, Jindal D, Gupta P, Kondal D, Goenka S, Jacob P, Singh R, Kumar BGP, Perel P, Tandon N, Patel V; Members of the Research Steering Committee,Investigators,Members of the Data Safety and Monitoring Board. Effectiveness of an mHealth-Based Electronic Decision Support System for Integrated Management of Chronic Conditions in Primary Care: The mWellcare Cluster-Randomized Controlled Trial. Circulation. 2019 Jan 15;139(3):380-391. doi: 10.1161/CIRCULATIONAHA.118.038192. Epub 2018 Nov 10. |
| 30253691 | Derived | Jindal D, Gupta P, Jha D, Ajay VS, Goenka S, Jacob P, Mehrotra K, Perel P, Nyong J, Roy A, Tandon N, Prabhakaran D, Patel V. Development of mWellcare: an mHealth intervention for integrated management of hypertension and diabetes in low-resource settings. Glob Health Action. 2018;11(1):1517930. doi: 10.1080/16549716.2018.1517930. |
| 28801393 | Derived | Jha D, Gupta P, Ajay VS, Jindal D, Perel P, Prieto-Merino D, Jacob P, Nyong J, Venugopal V, Singh K, Goenka S, Roy A, Tandon N, Patel V, Prabhakaran D. Protocol for the mWellcare trial: a multicentre, cluster randomised, 12-month, controlled trial to compare the effectiveness of mWellcare, an mHealth system for an integrated management of patients with hypertension and diabetes, versus enhanced usual care in India. BMJ Open. 2017 Aug 11;7(8):e014851. doi: 10.1136/bmjopen-2016-014851. |
| Curioso, W. and P. Mechael, Enhancing 'M-Health' With South-To-South Collaborations. Health Affairs, 2010(29): p. 264-267. | View source |
| Vital Wave Consulting, mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World. . 2009, UN Foundation-Vodafone Foundation Partnership: Washington, D.C. and Berkshire, UK. | View source |
| Hanson, K., et al., Expanding access to priority health interventions: a framework for understanding the constraints to scaling-up. J of International Development, 2003. 15(1): p. 1-14. | View source |
| Kaplan, W., Can the ubiquitous power of mobile phones be used to improve health outcomes in developing countries? . Global Health, 2006(2): p. 9. | View source |
| Rigby, M., Impact of telemedicine must be defined in developing countries. bmj, 2002. 324(7328): p. 47. | View source |
| http://www.whoindia.org/LinkFiles/NMH\_Resources\_CVD\_RISK\_MANAGEMENT\_BOOKLET.pdf | View source |
| mhGAP Intervention Guide for mental, neurological and substance use disorders in non-specialist health settings. ver 1.0. World Health Organisation Geneva, 2010. | View source |
| Free C, Phillips G, Watson L, Gallo L, Lambert F, Patel V, Edwards P. The Effectiveness Of Mobile Health Technologies for Improving Health and Health Services: A Systematic Review. Report for Department of Health, England (in preparation) | View source |
| ID | Term |
|---|---|
| D006973 | Hypertension |
| D003920 | Diabetes Mellitus |
| D003863 | Depression |
| D000437 | Alcoholism |
| D000073296 | Noncommunicable Diseases |
| ID | Term |
|---|---|
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |
| D019973 | Alcohol-Related Disorders |
| D019966 | Substance-Related Disorders |
| D064419 | Chemically-Induced Disorders |
| D001523 | Mental Disorders |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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