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Closing the Psychological Treatment Gap During the COVID-19 Pandemic With a Supportive Text Messaging Program: Protocol for Implementation and Evaluation.

Authors :
Agyapong VIO
Hrabok M
Vuong W
Gusnowski A
Shalaby R
Mrklas K
Li D
Urichuk L
Snaterse M
Surood S
Cao B
Li XM
Greiner R
Greenshaw AJ
Source :
JMIR research protocols [JMIR Res Protoc] 2020 Jun 22; Vol. 9 (6), pp. e19292. Date of Electronic Publication: 2020 Jun 22.
Publication Year :
2020

Abstract

Background: Coronavirus disease (COVID-19) has spread globally with far-reaching, significant, and unprecedented impacts on health and everyday life. Threats to mental health, psychological safety, and well-being are now emerging, increasing the impact of this virus on world health. Providing support for these challenges is difficult because of the high number of people requiring support in the context of a need to maintain physical distancing. This protocol describes the use of SMS text messaging (Text4Hope) as a convenient, cost-effective, and accessible population-level mental health intervention. This program is evidence-based, with prior research supporting good outcomes and high user satisfaction.<br />Objective: The project goal is to implement a program of daily supportive SMS text messaging (Text4Hope) to reduce distress related to the COVID-19 crisis, initially among Canadians. The prevalence of stress, anxiety, and depressive symptoms; the demographic correlates of the same; and the outcomes of the Text4Hope intervention in mitigating distress will be evaluated.<br />Methods: Self-administered anonymous online questionnaires will be used to assess stress (Perceived Stress Scale), anxiety (Generalized Anxiety Disorder-7 scale [GAD-7]), and depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]). Data will be collected at baseline (onset of SMS text messaging), the program midpoint (6 weeks), and the program endpoint (12 weeks).<br />Results: Data analysis will include parametric and nonparametric techniques, focusing on primary outcomes (ie, stress, anxiety, and depressive symptoms) and metrics of use, including the number of subscribers and user satisfaction. Given the large size of the data set, machine learning and data mining methods will also be used.<br />Conclusions: This COVID-19 project will provide key information regarding prevalence rates of stress, anxiety, and depressive symptoms during the pandemic; demographic correlates of distress; and outcome data related to this scalable population-level intervention. Information from this study will be valuable for practitioners and useful for informing policy and decision making regarding psychological interventions during the pandemic.<br />International Registered Report Identifier (irrid): DERR1-10.2196/19292.<br /> (©Vincent Israel Opoku Agyapong, Marianne Hrabok, Wesley Vuong, April Gusnowski, Reham Shalaby, Kelly Mrklas, Daniel Li, Liana Urichuk, Mark Snaterse, Shireen Surood, Bo Cao, Xin-Min Li, Russ Greiner, Andrew James Greenshaw. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 22.06.2020.)

Details

Language :
English
ISSN :
1929-0748
Volume :
9
Issue :
6
Database :
MEDLINE
Journal :
JMIR research protocols
Publication Type :
Academic Journal
Accession number :
32501805
Full Text :
https://doi.org/10.2196/19292