Günther, F., Wong, D. orcid.org/0000-0001-8117-9193, Alison-Davies, S. et al. (1 more author) (2023) Identifying factors associated with user retention and outcomes of a digital intervention for substance use disorder: A retrospective analysis of real-world data. JAMIA Open, 6 (3). ooad072. ISSN 2574-2531
Abstract
Objective.
Successful delivery of digital health interventions is affected by multiple real-world factors. These factors may be identified in routinely collected, ecologically valid data from these interventions. We propose ideas for exploring these data, focussing on interventions targeting complex, comorbid conditions.
Materials and Methods.
This study retrospectively explores pre-post data collected between 2016 and 2019 from users of digital cognitive behavioural therapy (CBT) - containing psychoeducation and practical exercises - for substance use disorder (SUD) at UK addiction services. To identify factors associated with heterogenous user responses to the technology, we employed multivariable and multivariate regressions and random forest models of user reported questionnaire data.
Results.
The dataset contained information from 14,078 individuals of which 12,529 reported complete data at baseline and 2,925 did so again after engagement with the CBT. 93% screened positive for dependence on one of 43 substances at baseline, and 73% screened positive for anxiety or depression. Despite pre-post improvements independent of user sociodemographics, women reported more frequent and persistent symptoms of SUD, anxiety and depression. Retention - minimum two use events recorded - was associated more with deployment environment than user characteristics. Prediction accuracy of post-engagement outcomes was acceptable (AUC: 0.74-0.79), depending non-trivially on user characteristics.
Discussion.
Traditionally, performance of digital health interventions is determined in controlled trials. Our analysis showcases multivariate models with which real-world data from these interventions can be explored and sources of user heterogeneity in retention and symptom reduction uncovered.
Conclusion.
Real-world data from digital health interventions contain information on natural user-technology interactions which could enrich results from controlled trials.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | digital health intervention, secondary use, substance use disorder, real-world uptake, real-world data exploration |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Centre for Health Services Research (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Oct 2023 14:23 |
Last Modified: | 20 Oct 2023 14:34 |
Published Version: | https://academic.oup.com/jamiaopen/article/6/3/ooa... |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/jamiaopen/ooad072 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202622 |