mHealth and global mental health: still waiting for the mH2 wedding?
Open accessAbstract
BACKGROUND: Two phenomena have become increasingly visible over the past decade: the significant global burden of disease arising from mental illness and the rapid acceleration of mobile phone usage in poorer countries. Mental ill-health accounts for a significant proportion of global disability-adjusted life years (DALYs) and years lived with disability (YLDs), especially in poorer countries where a number of factors combine to exacerbate issues of undertreatment. Yet poorer countries have also witnessed significant investments in, and dramatic expansions of, mobile coverage and usage over the past decade. DEBATE: The conjunction of high levels of mental illness and high levels of mobile phone usage in poorer countries highlights the potential for "mH(2)" interventions--i.e. mHealth (mobile technology-based) mental health interventions--to tackle global mental health challenges. However, global mental health movements and initiatives have yet to engage fully with this potential, partly because of scepticism towards technological solutions in general and partly because existing mH(2) projects in mental health have often taken place in a fragmented, narrowly-focused, and small-scale manner. We argue for a deeper and more sustained engagement with mobile phone technology in the global mental health context, and outline the possible shape of an integrated mH(2) platform for the diagnosis, treatment, and monitoring of mental health. SUMMARY: Existing and developing mH(2) technologies represent an underutilised resource in global mental health. If development, evaluation, and implementation challenges are overcome, an integrated mH2 platform would make significant contributions to mental healthcare in multiple settings and contexts.
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Use of digital technologies for mental health is not as common in most lower income countries, though interest and growth have increased in recent years.
“Use of digital technologies for mental health is not as common in most lower income countries18, though this has been an important area of increasing interest and growth in recent years.19,20”
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 [18]
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- Authors
- Farrington, C., Aristidou, A., & Ruggeri, K.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Farrington, Conor
- Venue
- Globalization and Health
- Published
- 2014
- Volume
- 10
- Issue
- 1
- Pages
- 17
- ISSN
- 1744-8603 (ISSN-L)
- PMC
- PMC3986901
- OpenAlex
- W2157529198
- Cited by
- 52
- References
- 46
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 1 · 2022: 2 · 2021: 5 · 2020: 5 · 2019: 7 · 2018: 2 · 2017: 5 · 2016: 10 · 2015: 10 · 2013: 1
- FWCI
- 12.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Globalization and Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- mobile health, global mental health, mobile phone technology, mental illness, low- and middle-income countries, mobile health intervention, mental health monitoring, disability-adjusted life years, years lived with disability
- MeSH terms
- Mental Disorders, Developing Countries, Persons with Disabilities, Humans, Mental Health, Mental Health Services, Global Health, World Health Organization, Cost of Illness, Quality-Adjusted Life Years, Cell Phone
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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