Evidence map›Paper›PMID 35596546›Full record

ArticleJMIR research protocols2022

Advancing Posttraumatic Stress Disorder Diagnosis and the Treatment of Trauma in Humanitarian Emergencies via Mobile Health: Protocol for a Proof-of-Concept Nonrandomized Controlled Trial.

Janaina V Pinto, Caroline Hunt, Brian O'Toole

Abstract read
In one paragraph

Article in JMIR research protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

3 authors.

Janaina V PintoFaculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-3976-5196
Caroline HuntSchool of Psychology, Faculty of Science, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0001-7988-1207
Brian O'TooleFaculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-3825-0772

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDecentralized health systems in low- and middle-income countries (LMICs) affected by humanitarian crises lack resources and a qualified workforce to attend to the overwhelming demand for mental health care in emergencies. Innovative approaches that are safe, cost-effective, and scalable are needed to address the burden of traumatic stress caused by emergencies. High mobile phone ownership rates combined with the precision of neural, cognitive, and biometric measures of trauma and their feasible integration with artificial intelligence makes digital app interventions a promising pathway to promote precision diagnosis and high-impact care.

objectiveThis study aimed to advance methods for the objective diagnosis and treatment of trauma in emergencies across LMICs by examining neural, cognitive, and biometric markers and the efficacy of the eResilience app, a neuroscience-informed mobile health mental health app intervention, via changes in clinical symptomatology, cognitive performance, and brain activity.

methodsTrauma-exposed African refugees residing in Australia were selected for this study. A research software version of the eResilience app with advanced monitoring capabilities was designed for this trial. Participants completed the eResilience app at home during a 7-day period. Clinical, cognitive, and electrophysiological data were collected at baseline, along with posttest measurements to examine biomarkers of trauma and the efficacy of the proposed digital intervention for the treatment of trauma and its potential outcomes, including depression, anxiety, physical symptoms, self-harm, substance misuse, and cognitive impairment. In addition, biofeedback, well-being, and subjective stress data points were collected via the app during the treatment week, followed by clinical interviews at 1, 3, 6, and 12 months after the intervention.

resultsData collection was conducted between 2018 and 2020. A total of 100 participants exposed to war were screened; 75 (75%) were enrolled and assigned to a trauma-exposed control (38/75, 51%) or posttraumatic stress disorder condition (37/75, 49%); and 70 (70%) completed all baseline, treatment, and posttest assessments. A total of 89% (62/70) of those who completed the intervention opted to enroll in the 3-, 6-, and 12-month follow-ups. Data collection is complete. As of May 2022, the results of all proposed analyses are being prepared for publication. If proven efficacious, this proof-of-concept clinical trial will inform fully powered randomized clinical trials in LMICs to further develop artificial intelligence-powered, app-based diagnostic and prognostic features and determine the app's cross-cultural efficacy for the treatment of trauma in emergency settings.

conclusionsThis protocol provides researchers with a comprehensive background of the study rationale, a detailed guideline for replication studies interested in examining the feasibility and efficacy of the eResilience app across varied demographics, and a robust framework for investigating low-cost objective diagnostic markers in mental health interventions. Methodological limitations and suggestions are also provided.

trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12616001205426; https://tinyurl.com/yckwc4d7. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/38223.

Indexed as

biometriccognitionEEGelectroencephalogramelectrophysiologyemergencieshealth applicationhealth interventionhealth systemhumanitarianmental healthmHealthmobile healthmobile phoneneuroscienceposttraumatic stress disorderPTSDtechnologytrauma

Identifiers

PMID35596546
PMCPMC9244657

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LicenceCC BY
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Registered trials

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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.