Increasing use of mental health services in remote areas using mobile technology: a pre-post evaluation of the SMART Mental Health project in rural India
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BACKGROUND: About 25% of the Indian population experience common mental disorders (CMD) but only 15-25% of them receive any mental health care. Stigma, lack of adequate mental health professionals and mental health services account for this treatment gap, which is worse in rural areas. Our project evaluated task shifting and mobile-technology based electronic decision support systems to enhance the ability of primary care health workers to provide evidence-based mental health care for stress, depression, and suicidal risk in 30 remote villages in the state of Andhra Pradesh, India. METHODS: The Systematic Medical Appraisal Referral and Treatment (SMART) Mental Health project between May 2014 and April 2016 trained lay village health workers (Accredited Social Health Activists - ASHAs) and primary care doctors to screen, diagnose and manage individuals with common mental disorders using an electronic decision support system. An anti-stigma campaign using multi-media approaches was conducted across the villages at the outset of the project. A pre-post evaluation using mixed methods assessed the change in mental health service utilization by screen positive individuals. This paper reports on the quantitative aspects of that evaluation. RESULTS: Training was imparted to 21 ASHAs and 2 primary care doctors. 5007 of 5167 eligible individuals were screened, and 238 were identified as being positive for common mental disorders and referred to the primary care doctors for further management. Out of them, 2 (0.8%) had previously utilized mental health services. During the intervention period, 30 (12.6%) visited the primary care doctor for further diagnosis and treatment, as advised. There was a significant reduction in the depression and anxiety scores between start and end of the intervention among those who had screened positive at the beginning. Stigma and mental health awareness in the broader community improved during the project. CONCLUSIONS: The intervention led to individuals being screened for common mental disorders by village health workers and increase in mental health service use by those referred to the primary care doctor. The model was deemed feasible and acceptable. The effectiveness of the intervention needs to be demonstrated using more robust randomized controlled trials, while addressing the issues identified that will facilitate scale up.
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In SMART, community health workers called Accredited Social Health Activists and primary care doctors were trained to screen, diagnose, and manage common mental disorders using an electronic decision support system.
“In this project, community health workers referred to as Accredited Social Health Activists (ASHAs) and primary care doctors were trained to screen, diagnose and manage common mental disorders using an electronic decision support system.28,29”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
The SMART Mental Health project in Andhra Pradesh, India, used task-sharing and mobile technology to support primary care health workers in providing evidence-based mental health care in remote villages.
“The Systematic Medical Appraisal Referral and Treatment (SMART) Mental Health project involved task-sharing and use of mobile technology to support primary care health workers in providing evidence-based mental health care in remote villages in Andhra Pradesh, India.”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
The SMART mobile decision support system was developed for basic Android devices and used an OpenMRS platform for secure cloud-based data sharing between community health workers and primary care doctors.
“This mobile decision support system was developed for use on basic Android devices, and used an OpenMRS platform allowing community health workers and primary care doctors to share clinical data securely using the cloud-based servers.28”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
In SMART, over 5000 patients were screened for common mental disorders, of whom 238 screened positive and were referred to primary care doctors.
“During this time, over 5000 patients were screened for common mental disorders of which 238 were identified as being positive and were referred to the primary care doctors for treatment.”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
In SMART, pre-recorded messages were sent to individuals who screened positive to promote adherence and continuation of care.
“Pre-recorded messages were also sent to individuals who screened positive to promote adherence and continuation of care as recommended by the community health workers and primary care doctors.”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
Among SMART participants who screened positive for depression or anxiety at baseline, GAD-7 anxiety scores decreased at 3 months (adjusted mean difference −1.3; SE=0.4; P=0.004).
“and a reduction in anxiety measured using the 7-item Generalized Anxiety Disorder (GAD-7) questionnaire (adjusted mean difference in GAD-7, −1.3; SE = 0.4; P = 0.004).28”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
The SMART program supported with mobile technology was considered feasible and acceptable for use in the rural setting.
“The SMART program supported with mobile technology was considered both feasible and acceptable for use in this rural setting.”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
Among SMART participants who screened positive for depression or anxiety at baseline, PHQ-9 depression scores decreased at 3 months (adjusted mean difference −3.6; SE=0.6; P<0.001).
“At 3-months follow up, there was a reduction in depression measured using the PHQ-9 (adjusted mean difference in PHQ-9, −3.6; SE = 0.6; P < 0.001)”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
In SMART, 21 community health workers and two primary care doctors were trained over two years across 30 villages.
“As part of the program, 21 community health workers and two primary care doctors were trained over a two-year period across 30 villages.”
From Digital Technology for Building Capacity of Nonspecialist Health Workers for Task Sharing and Scaling Up Mental Health Care Globally (2019) · cites this paper as [28]
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Directly engaging with People with lived experiences of mental illness from the communities in India
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Self-citationTechnologies that empower women for better access to healthcare in India – A scoping review
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Ethical issues in m-Health applications in community health work in India — a scoping review
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Mental Health Literacy in Rural India
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Global mental health and the DIADA project
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Positive mental health for all serving the under-served
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Salud Mental Global y el Proyecto DIADA
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Technology-based mental healthcare models: A systematic review of the literature
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PERSPECTIVAS PARA O USO DA TELEMEDICINA NO ATENDIMENTO DE SAÚDE MENTAL NA ATENÇÃO PRIMÁRIA
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What to do Without a Control Group: You have to go latent, but not all latents are equalPreprint
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Luitel, N. P. (2023) · Cambridge Prisms Global Mental Health · doi:10.1017/gmh.2023.69.pr6
Luitel, N. P. (2023) · Cambridge Prisms Global Mental Health · doi:10.1017/gmh.2023.69.pr1
(2024) · Cambridge Prisms Global Mental Health · doi:10.1017/gmh.2024.35.pr5
(2024) · Cambridge Prisms Global Mental Health · doi:10.1017/gmh.2024.35.pr6
(2024) · Cambridge Prisms Global Mental Health · doi:10.1017/gmh.2024.35.pr3
- Authors
- Maulik, P. K., Kallakuri, S., Devarapalli, S., Vadlamani, V. K., Jha, V., & Patel, A.
- Affiliations
- The George Institute for Global Health (GB)
- George Institute for Global Health (IN)
- The University of Sydney (AU)
- Corresponding author
- Maulik, Pallab K
- Venue
- Journal of Global Health
- Published
- 2017-03-18
- Volume
- 7
- Issue
- 1
- Pages
- 010408-010408
- ISSN
- 2047-2978 (ISSN-L)
- 2047-2986
- PMC
- PMC5370210
- OpenAlex
- W2603376457
- Cited by
- 125
- References
- 35
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 14 · 2023: 20 · 2022: 11 · 2021: 32 · 2020: 10 · 2019: 9 · 2018: 7 · 2017: 1
- FWCI
- 10.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Global Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Information Systems › ICT in Developing Communities
- Keywords
- mental health service utilization, common mental disorders, task shifting, electronic decision support, mobile health, Accredited Social Health Activists, primary care, mental health screening, mental health stigma, anti-stigma campaigns, depression, anxiety, suicide risk, rural India, Andhra Pradesh
- MeSH terms
- Adult, Anxiety, Awareness, Mental Disorders, Community Health Workers, Depression, Female, Humans, India, Male, Mass Screening, Mental Health Services, Middle Aged, Primary Health Care, Referral and Consultation, Rural Population, Outcome Assessment, Health Care, Social Stigma, Mobile Applications
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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