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Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence
BACKGROUND: Mental health disorders affect 1 in 10 people globally, of whom approximately 300 million are affected by depression. At least half of the people affected by depression remain untreated. Although cognitive behavioral therapy (CBT) is an effective treatment, access to mental health specia...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367167/ https://www.ncbi.nlm.nih.gov/pubmed/34328431 http://dx.doi.org/10.2196/27619 |
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author | Martinengo, Laura Stona, Anne-Claire Griva, Konstadina Dazzan, Paola Pariante, Carmine Maria von Wangenheim, Florian Car, Josip |
author_facet | Martinengo, Laura Stona, Anne-Claire Griva, Konstadina Dazzan, Paola Pariante, Carmine Maria von Wangenheim, Florian Car, Josip |
author_sort | Martinengo, Laura |
collection | PubMed |
description | BACKGROUND: Mental health disorders affect 1 in 10 people globally, of whom approximately 300 million are affected by depression. At least half of the people affected by depression remain untreated. Although cognitive behavioral therapy (CBT) is an effective treatment, access to mental health specialists, habitually challenging, has worsened because of the COVID-19 pandemic. Internet-based CBT is an effective and feasible strategy to increase access to treatment for people with depression. Mental health apps may further assist in facilitating self-management for people affected by depression; however, accessing the correct app may be cumbersome given the large number and wide variety of apps offered by public app marketplaces. OBJECTIVE: This study aims to systematically assess the features, functionality, data security, and congruence with evidence of self-guided CBT-based apps targeting users affected by depression that are available in major app stores. METHODS: We conducted a systematic assessment of self-guided CBT-based apps available in Google Play and the Apple App Store. Apps launched or updated since August 2018 were identified through a systematic search in the 42matters database using CBT-related terms. Apps meeting the inclusion criteria were downloaded and assessed using a Samsung Galaxy J7 Pro (Android 9) and iPhone 7 (iOS 13.3.1). Apps were appraised using a 182-question checklist developed by the research team, assessing their general characteristics, technical aspects and quality assurance, and CBT-related features, including 6 evidence-based CBT techniques (ie, psychoeducation, behavioral activation, cognitive restructuring, problem solving, relaxation, and exposure for comorbid anxiety) as informed by a CBT manual, CBT competence framework, and a literature review of internet-based CBT clinical trial protocols. The results were reported as a narrative review using descriptive statistics. RESULTS: The initial search yielded 3006 apps, of which 98 met the inclusion criteria and were systematically assessed. There were 20 well-being apps; 65 mental health apps, targeting two or more common mental health disorders, including depression; and 13 depression apps. A total of 28 apps offered at least four evidence-based CBT techniques, particularly depression apps. Cognitive restructuring was the most common technique, offered by 79% (77/98) of the apps. Only one-third of the apps offered suicide risk management resources, whereas 17% (17/98) of the apps offered COVID-19–related information. Although most apps included a privacy policy, only a third of the apps presented it before account creation. In total, 82% (74/90) of privacy policies stated sharing data with third-party service providers. Half of the app development teams included academic institutions or health care providers. CONCLUSIONS: Only a few self-guided CBT-based apps offer comprehensive CBT programs or suicide risk management resources. Sharing of users’ data is widespread, highlighting shortcomings in health app market governance. To fulfill their potential, self-guided CBT-based apps should follow evidence-based clinical guidelines, be patient centered, and enhance users’ data security. |
format | Online Article Text |
id | pubmed-8367167 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-83671672021-08-24 Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence Martinengo, Laura Stona, Anne-Claire Griva, Konstadina Dazzan, Paola Pariante, Carmine Maria von Wangenheim, Florian Car, Josip J Med Internet Res Original Paper BACKGROUND: Mental health disorders affect 1 in 10 people globally, of whom approximately 300 million are affected by depression. At least half of the people affected by depression remain untreated. Although cognitive behavioral therapy (CBT) is an effective treatment, access to mental health specialists, habitually challenging, has worsened because of the COVID-19 pandemic. Internet-based CBT is an effective and feasible strategy to increase access to treatment for people with depression. Mental health apps may further assist in facilitating self-management for people affected by depression; however, accessing the correct app may be cumbersome given the large number and wide variety of apps offered by public app marketplaces. OBJECTIVE: This study aims to systematically assess the features, functionality, data security, and congruence with evidence of self-guided CBT-based apps targeting users affected by depression that are available in major app stores. METHODS: We conducted a systematic assessment of self-guided CBT-based apps available in Google Play and the Apple App Store. Apps launched or updated since August 2018 were identified through a systematic search in the 42matters database using CBT-related terms. Apps meeting the inclusion criteria were downloaded and assessed using a Samsung Galaxy J7 Pro (Android 9) and iPhone 7 (iOS 13.3.1). Apps were appraised using a 182-question checklist developed by the research team, assessing their general characteristics, technical aspects and quality assurance, and CBT-related features, including 6 evidence-based CBT techniques (ie, psychoeducation, behavioral activation, cognitive restructuring, problem solving, relaxation, and exposure for comorbid anxiety) as informed by a CBT manual, CBT competence framework, and a literature review of internet-based CBT clinical trial protocols. The results were reported as a narrative review using descriptive statistics. RESULTS: The initial search yielded 3006 apps, of which 98 met the inclusion criteria and were systematically assessed. There were 20 well-being apps; 65 mental health apps, targeting two or more common mental health disorders, including depression; and 13 depression apps. A total of 28 apps offered at least four evidence-based CBT techniques, particularly depression apps. Cognitive restructuring was the most common technique, offered by 79% (77/98) of the apps. Only one-third of the apps offered suicide risk management resources, whereas 17% (17/98) of the apps offered COVID-19–related information. Although most apps included a privacy policy, only a third of the apps presented it before account creation. In total, 82% (74/90) of privacy policies stated sharing data with third-party service providers. Half of the app development teams included academic institutions or health care providers. CONCLUSIONS: Only a few self-guided CBT-based apps offer comprehensive CBT programs or suicide risk management resources. Sharing of users’ data is widespread, highlighting shortcomings in health app market governance. To fulfill their potential, self-guided CBT-based apps should follow evidence-based clinical guidelines, be patient centered, and enhance users’ data security. JMIR Publications 2021-07-30 /pmc/articles/PMC8367167/ /pubmed/34328431 http://dx.doi.org/10.2196/27619 Text en ©Laura Martinengo, Anne-Claire Stona, Konstadina Griva, Paola Dazzan, Carmine Maria Pariante, Florian von Wangenheim, Josip Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.07.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Martinengo, Laura Stona, Anne-Claire Griva, Konstadina Dazzan, Paola Pariante, Carmine Maria von Wangenheim, Florian Car, Josip Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence |
title | Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence |
title_full | Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence |
title_fullStr | Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence |
title_full_unstemmed | Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence |
title_short | Self-guided Cognitive Behavioral Therapy Apps for Depression: Systematic Assessment of Features, Functionality, and Congruence With Evidence |
title_sort | self-guided cognitive behavioral therapy apps for depression: systematic assessment of features, functionality, and congruence with evidence |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367167/ https://www.ncbi.nlm.nih.gov/pubmed/34328431 http://dx.doi.org/10.2196/27619 |
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