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Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review

BACKGROUND: The rapid growth of digital health apps has necessitated new regulatory approaches to ensure compliance with safety and effectiveness standards. Nonadherence and heterogeneous user engagement with digital health apps can lead to trial estimates that overestimate or underestimate an app’s...

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Autores principales: Grayek, Emily, Krishnamurti, Tamar, Hu, Lydia, Babich, Olivia, Warren, Katherine, Fischhoff, Baruch
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687688/
https://www.ncbi.nlm.nih.gov/pubmed/37966871
http://dx.doi.org/10.2196/46237
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author Grayek, Emily
Krishnamurti, Tamar
Hu, Lydia
Babich, Olivia
Warren, Katherine
Fischhoff, Baruch
author_facet Grayek, Emily
Krishnamurti, Tamar
Hu, Lydia
Babich, Olivia
Warren, Katherine
Fischhoff, Baruch
author_sort Grayek, Emily
collection PubMed
description BACKGROUND: The rapid growth of digital health apps has necessitated new regulatory approaches to ensure compliance with safety and effectiveness standards. Nonadherence and heterogeneous user engagement with digital health apps can lead to trial estimates that overestimate or underestimate an app’s effectiveness. However, there are no current standards for how researchers should measure adherence or address the risk of bias imposed by nonadherence through efficacy analyses. OBJECTIVE: This systematic review aims to address 2 critical questions regarding clinical trials of software as a medical device (SaMD) apps: How well do researchers report adherence and engagement metrics for studies of effectiveness and efficacy? and What efficacy analyses do researchers use to account for nonadherence and how appropriate are their methods? METHODS: We searched the Food and Drug Administration’s registration database for registrations of repeated-use, patient-facing SaMD therapeutics. For each such registration, we searched ClinicalTrials.gov, company websites, and MEDLINE for the corresponding clinical trial and study articles through March 2022. Adherence and engagement data were summarized for each of the 24 identified articles, corresponding to 10 SaMD therapeutics. Each article was analyzed with a framework developed using the Cochrane risk-of-bias questions to estimate the potential effects of imperfect adherence on SaMD effectiveness. This review, funded by the Richard King Mellon Foundation, is registered on the Open Science Framework. RESULTS: We found that although most articles (23/24, 96%) reported collecting information about SaMD therapeutic engagement, of the 20 articles for apps with prescribed use, only 9 (45%) reported adherence information across all aspects of prescribed use: 15 (75%) reported metrics for the initiation of therapeutic use, 16 (80%) reported metrics reporting adherence between the initiation and discontinuation of the therapeutic (implementation), and 4 (20%) reported the discontinuation of the therapeutic (persistence). The articles varied in the reported metrics. For trials that reported adherence or engagement, there were 4 definitions of initiation, 8 definitions of implementation, and 4 definitions of persistence. All articles studying a therapeutic with a prescribed use reported effectiveness estimates that might have been affected by nonadherence; only a few (2/20, 10%) used methods appropriate to evaluate efficacy. CONCLUSIONS: This review identifies 5 areas for improving future SaMD trials and studies: use consistent metrics for reporting adherence, use reliable adherence metrics, preregister analyses for observational studies, use less biased efficacy analysis methods, and fully report statistical methods and assumptions.
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spelling pubmed-106876882023-11-30 Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review Grayek, Emily Krishnamurti, Tamar Hu, Lydia Babich, Olivia Warren, Katherine Fischhoff, Baruch JMIR Mhealth Uhealth Review BACKGROUND: The rapid growth of digital health apps has necessitated new regulatory approaches to ensure compliance with safety and effectiveness standards. Nonadherence and heterogeneous user engagement with digital health apps can lead to trial estimates that overestimate or underestimate an app’s effectiveness. However, there are no current standards for how researchers should measure adherence or address the risk of bias imposed by nonadherence through efficacy analyses. OBJECTIVE: This systematic review aims to address 2 critical questions regarding clinical trials of software as a medical device (SaMD) apps: How well do researchers report adherence and engagement metrics for studies of effectiveness and efficacy? and What efficacy analyses do researchers use to account for nonadherence and how appropriate are their methods? METHODS: We searched the Food and Drug Administration’s registration database for registrations of repeated-use, patient-facing SaMD therapeutics. For each such registration, we searched ClinicalTrials.gov, company websites, and MEDLINE for the corresponding clinical trial and study articles through March 2022. Adherence and engagement data were summarized for each of the 24 identified articles, corresponding to 10 SaMD therapeutics. Each article was analyzed with a framework developed using the Cochrane risk-of-bias questions to estimate the potential effects of imperfect adherence on SaMD effectiveness. This review, funded by the Richard King Mellon Foundation, is registered on the Open Science Framework. RESULTS: We found that although most articles (23/24, 96%) reported collecting information about SaMD therapeutic engagement, of the 20 articles for apps with prescribed use, only 9 (45%) reported adherence information across all aspects of prescribed use: 15 (75%) reported metrics for the initiation of therapeutic use, 16 (80%) reported metrics reporting adherence between the initiation and discontinuation of the therapeutic (implementation), and 4 (20%) reported the discontinuation of the therapeutic (persistence). The articles varied in the reported metrics. For trials that reported adherence or engagement, there were 4 definitions of initiation, 8 definitions of implementation, and 4 definitions of persistence. All articles studying a therapeutic with a prescribed use reported effectiveness estimates that might have been affected by nonadherence; only a few (2/20, 10%) used methods appropriate to evaluate efficacy. CONCLUSIONS: This review identifies 5 areas for improving future SaMD trials and studies: use consistent metrics for reporting adherence, use reliable adherence metrics, preregister analyses for observational studies, use less biased efficacy analysis methods, and fully report statistical methods and assumptions. JMIR Publications 2023-11-15 /pmc/articles/PMC10687688/ /pubmed/37966871 http://dx.doi.org/10.2196/46237 Text en ©Emily Grayek, Tamar Krishnamurti, Lydia Hu, Olivia Babich, Katherine Warren, Baruch Fischhoff. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 15.11.2023. 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 JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Review
Grayek, Emily
Krishnamurti, Tamar
Hu, Lydia
Babich, Olivia
Warren, Katherine
Fischhoff, Baruch
Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review
title Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review
title_full Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review
title_fullStr Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review
title_full_unstemmed Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review
title_short Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review
title_sort collection and analysis of adherence information for software as a medical device clinical trials: systematic review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687688/
https://www.ncbi.nlm.nih.gov/pubmed/37966871
http://dx.doi.org/10.2196/46237
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