Evidence map›Paper›PMID 32457823›Full record

ArticleCurrent treatment options in psychiatry2019

Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention.

John A Naslund, Pattie P Gonsalves, Oliver Gruebner, Sachin R Pendse, Stephanie L Smith, Amit Sharma, Giuseppe Raviola

Registry-linked trialOpen access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current treatment options in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05415306 (An Emoji-based Attention Bias Modification Intervention for Depressive Symptom Severity in Young Adults), which is not on this map. Cited by 49 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 6 pooled it
6.3field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05415306 naunknown statusnot on this mapstarted 2022, after this paper: background citation

An Emoji-based Attention Bias Modification Intervention for Depressive Symptom Severity in Young Adults

TypeinterventionalSponsorUniversiti Tunku Abdul RahmanRan2022 to 2024Enrolled120ConditionsDepressive Symptoms, Mood DisordersArmsAttention Bias Modification, Deep-breathing training, Sham Training
3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 6 syntheses or guidelines pooled it, 100 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 6 institutions in 3 countries.

John A NaslundDepartment of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA.
Pattie P GonsalvesSangath, New Delhi, India.
Oliver GruebnerEpidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Sachin R PendseMicrosoft Research India, Bangalore, India.
Stephanie L SmithDepartment of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA.
Amit SharmaMicrosoft Research India, Bangalore, India.
Giuseppe RaviolaDepartment of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA.
Microsoft Research (India) · INBrigham and Women's Hospital · USHarvard Global Health Institute · USHarvard University · USSangath · INUniversity of Zurich · CH

Funding

ESSENCE (Enabling translation of Science to Service to ENhance Depression CarE) Scale-UpU19MH113211 · NIMH · HARVARD MEDICAL SCHOOL · PI PATEL, VIKRAM H · 2017 to 2023
$3.7M
NIMH NIH HHS U19 MH113211
6 · The paper itself

Abstract

purposeGlobally, individuals living with mental disorders are more likely to have access to a mobile phone than mental health care. In this commentary, we highlight opportunities for expanding access to and use of digital technologies to advance research and intervention in mental health, with emphasis on the potential impact in lower resource settings. RECENT

findingsDrawing from empirical evidence, largely from higher income settings, we considered three emerging areas where digital technology will potentially play a prominent role: supporting methods in data science to further our understanding of mental health and inform interventions, task sharing for building workforce capacity by training and supervising non-specialist health workers, and facilitating new opportunities for early intervention for young people in lower resource settings. Challenges were identified related to inequities in access, threats of bias in big data analyses, risks to users, and need for user involvement to support engagement and sustained use of digital interventions. SUMMARY: For digital technology to achieve its potential to transform the ways we detect, treat, and prevent mental disorders, there is a clear need for continued research involving multiple stakeholders, and rigorous studies showing that these technologies can successfully drive measurable improvements in mental health outcomes.

Indexed as

Artificial intelligenceBig dataDigital technologyEarly interventionGlobal mental healthTask sharing

Identifiers

PMID32457823
PMCPMC7250369
OpenAlexW2971639033

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.