‘SMS’ for mental health – Feasibility and acceptability of using text messages for mental health promotion among young women from urban low income settings in India
Abstract
Objective The current study assesses the acceptability and feasibility of mobile text messages for promoting positive mental health and as a helpline among young women in urban slums of Bangalore.Methodology Forty girls in the age range of 16-18 years from urban slums received messages every day for a month. They could call or message back or give a 'missed call' to the same number whenever they had emotional problems or felt like talking to a counselor. The received responses in the form of return texts, missed calls and return phone calls were recorded. Feedback about the feasibility and acceptability of the mobile messages was collected after a month.Results 25 out of 40 (62.5%) participants called back, asking for mental health services and to say they felt good about the messages. 23 of 40 (57.5%) messaged back regarding their feelings. 62% reported that they felt supported with the mental health messages. Male family members of nearly half of the participants called back to check the authenticity of the source. Most women did not face any problems because of the messages.Conclusion This pilot qualitative study indicates that mobile text messages are a feasible and culturally acceptable method for mental health promotion and prevention among young women from urban slums in India. Issues such as consent from the woman and family, ensuring confidentiality and providing authentic and reliable support services, need to be taken into account before attempting to scale up such a service, particularly in vulnerable groups.
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Adapting effective mHealth programs for multiple minority populations requires attention to relevant cultural characteristics.
“Adapting effective mHealth programs for multiple minority populations requires attention to relevant cultural characteristics [2-4,6,9,10].”
From Development of a Culturally Tailored Text Message Maternal Health Program: TextMATCH (2017) · cites this paper as [6]
Traditional health promotion methods often fail to serve the group in question.
“(1) traditional health promotion methods often fail to serve the group in question”
From Development of a Culturally Tailored Text Message Maternal Health Program: TextMATCH (2017) · cites this paper as [6]
mHealth has proven acceptable or effective in other, similar areas, or in feasibility studies.
“(4) mHealth has proven acceptable or effective in other, similar areas, or in feasibility studies [2-8].”
From Development of a Culturally Tailored Text Message Maternal Health Program: TextMATCH (2017) · cites this paper as [6]
The low cost of mHealth interventions makes mHealth interventions feasible in low-resource environments.
“(2) the low cost of mHealth interventions make mHealth interventions feasible in low-resource environments”
From Development of a Culturally Tailored Text Message Maternal Health Program: TextMATCH (2017) · cites this paper as [6]
In instances where perceptions of culturally adapted mHealth interventions have been described, these perceptions are positive.
“In instances where perceptions of culturally adapted mHealth interventions have been described, these perceptions are positive [3,4,6,15-18].”
From Development of a Culturally Tailored Text Message Maternal Health Program: TextMATCH (2017) · cites this paper as [6]
Mobile phone use or use of specific capabilities like text messaging or voice calling are common within the group targeted.
“(3) mobile phone use or use of specific capabilities like text messaging or voice calling are common within the group targeted”
From Development of a Culturally Tailored Text Message Maternal Health Program: TextMATCH (2017) · cites this paper as [6]
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Understanding Text Messages for Anxiety Therapy Through Topic Modeling
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Factors Affecting Intention of Usage of Artificial Intelligence Powered mHealth (AIM)
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- Authors
- Chandra, P. S., Sowmya, H. R., Mehrotra, S., & Duggal, M.
- Affiliations
- National Institute of Mental Health and Neurosciences (IN)
- Translational Health Science and Technology Institute (IN)
- Corresponding author
- Chandra, Prabha S.
- Venue
- Asian Journal of Psychiatry
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 11
- Pages
- 59-64
- ISSN
- 1876-2018 (ISSN-L)
- OpenAlex
- W2034328890
- Cited by
- 72
- References
- 45
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 18 · 2022: 11 · 2021: 3 · 2020: 5 · 2019: 6 · 2018: 3 · 2017: 5 · 2016: 8 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 9.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Asian Journal of Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Department of Health Research (DHR) Ministry of Health and Family welfare, Government of India (DHR/Plan scheme/GIA/11/2012/dated 31/3/2012)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- text messaging, mental health promotion, young women, urban slums, low-income settings, feasibility, acceptability, telephone helpline, adolescent mental health, confidentiality, family consent
- MeSH terms
- Adolescent, Mental Disorders, Feasibility Studies, Female, Health Promotion, Humans, India, Patient Acceptance of Health Care, Pilot Projects, Poverty, Urban Population, Qualitative Research, Text Messaging
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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