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Wang R, Rouleau G, Booth GL, Brazeau AS, El-Dassouki N, Taylor M, Cafazzo JA, Greenberg M, Nakhla M, Shulman R, Desveaux L. Understanding Whether and How a Digital Health Intervention Improves Transition Care for Emerging Adults Living With Type 1 Diabetes: Protocol for a Mixed Methods Realist Evaluation. JMIR Res Protoc 2023; 12:e46115. [PMID: 37703070 PMCID: PMC10534286 DOI: 10.2196/46115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/27/2023] [Accepted: 07/24/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Emerging adults living with type 1 diabetes (T1D) face a series of challenges with self-management and decreased health system engagement, leading to an increased risk of acute complications and hospital admissions. Effective and scalable strategies are needed to support this population to transfer seamlessly from pediatric to adult care with sufficient self-management capability. While digital health interventions for T1D self-management are a promising strategy, it remains unclear which elements work, how, and for which groups of individuals. OBJECTIVE This study aims to evaluate the design and implementation of a multicomponent SMS text message-based digital health intervention to support emerging adults living with T1D in real-world settings. The objectives are to identify the intervention components and associated mechanisms that support user engagement and T1D health care transition experiences and determine the individual characteristics that influence the implementation process. METHODS We used a realist evaluation embedded alongside a randomized controlled trial, which uses a sequential mixed methods design to analyze data from multiple sources, including intervention usage data, patient-reported outcomes, and realist interviews. In step 1, we conducted a document analysis to develop a program theory that outlines the hypothesized relationships among "individual-level contextual factors, intervention components and features, mechanisms, and outcomes," with special attention paid to user engagement. Among them, intervention components and features depict 10 core characteristics such as transition support information, problem-solving information, and real-time interactivity. The proximal outcomes of interest include user engagement, self-efficacy, and negative emotions, whereas the distal outcomes of interest include transition readiness, self-blood glucose monitoring behaviors, and blood glucose. In step 2, we plan to conduct semistructured realist interviews with the randomized controlled trial's intervention-arm participants to test the hypothesized "context-intervention-mechanism-outcome" configurations. In step 3, we plan to triangulate all sources of data using a coincidence analysis to identify the necessary combinations of factors that determine whether and how the desired outcomes are achieved and use these insights to consolidate the program theory. RESULTS For step 1 analysis, we have developed the initial program theory and the corresponding data collection plan. For step 2 analysis, participant enrollment for the randomized controlled trial started in January 2023. Participant enrollment for this realist evaluation was anticipated to start in July 2023 and continue until we reached thematic saturation or achieved informational power. CONCLUSIONS Beyond contributing to knowledge on the multiple pathways that lead to successful engagement with a digital health intervention as well as target outcomes in T1D care transitions, embedding the realist evaluation alongside the trial may inform real-time intervention refinement to improve user engagement and transition experiences. The knowledge gained from this study may inform the design, implementation, and evaluation of future digital health interventions that aim to improve transition experiences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/46115.
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Affiliation(s)
- Ruoxi Wang
- Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada
| | - Geneviève Rouleau
- Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada
- Département des Sciences Infirmières, Université du Québec en Outaouais, St-Jérôme, QC, Canada
- Faculté des sciences infirmières, l'Université de Montréal, Montreal, QC, Canada
| | - Gillian Lynn Booth
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | | | - Noor El-Dassouki
- Centre for Digital Therapeutics, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Madison Taylor
- Centre for Digital Therapeutics, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Joseph A Cafazzo
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Digital Therapeutics, Toronto General Hospital, University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Marley Greenberg
- Department of Philosophy, Joint Centre for Bioethics, University of Toronto, Toronto, ON, Canada
- Diabetes Action Canada, Toronto, ON, Canada
| | - Meranda Nakhla
- Division of Endocrinology, Montreal Children's Hospital, McGill University, Montréal, QC, Canada
- Research Institute of the McGill University Health Centre, Montréal, QC, Canada
| | - Rayzel Shulman
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Child Health Evaluative Sciences, SickKids Research Institute, Toronto, ON, Canada
- Division of Endocrinology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Laura Desveaux
- Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
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Pedersen K, Schlichter BR. Improving Predictability and Effectiveness in Preventive Digital Health Interventions: Scoping Review. Interact J Med Res 2023; 12:e40205. [PMID: 37471129 PMCID: PMC10401197 DOI: 10.2196/40205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 11/01/2022] [Accepted: 06/09/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Lifestyle-related diseases caused by inadequate diet and physical activity cause premature death, loss of healthy life years, and increased health care costs. Randomized controlled trial (RCT) studies indicate that preventive digital health interventions (P-DHIs) can be effective in preventing these health problems, but the results of these studies are mixed. Adoption studies have identified multiple factors related to individuals and the context in which they live that complicate the transfer of positive results from RCT studies to practical use. Implementation studies have revealed barriers to the large-scale implementation of mobile health (mHealth) solutions in general. Consequently, there is no clear path to delivering predictable outcomes from P-DHIs and achieving effectiveness when scaling up interventions to reduce health problems in society. OBJECTIVE This research aimed to expand our understanding of how to increase the outcome predictability of P-DHIs by focusing on physical activity and diet behaviors and amplify our understanding of how to improve effectiveness in large-scale implementations. METHODS The research objective was pursued through a multidisciplinary scoping review. This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) as a guide. A comprehensive search of Web of Science and PubMed limited to English-language journal articles published before January 2022 was conducted. Google Scholar was used for hand searches. Information systems theory was used to identify key constructs influencing outcomes of IT in general. Public health and mHealth literature were used to identify factors influencing the adoption of, outcomes from, and implementation of P-DHIs. Finally, the P-DHI investment model was developed based on information systems constructs and factors from the public health and mHealth literature. RESULTS In total, 203 articles met the eligibility criteria. The included studies used a variety of methodologies, including literature reviews, interviews, surveys, and RCT studies. The P-DHI investment model suggests which constructs and related factors should be emphasized to increase the predictability of P-DHI outcomes and improve the effectiveness of large-scale implementations. CONCLUSIONS The research suggests that outcome predictability could be improved by including descriptions of the constructs and factors in the P-DHI investment model when reporting from empirical studies. Doing so would increase our understanding of when and why P-DHIs succeed or fail. The effectiveness of large-scale implementations may be improved by using the P-DHI investment model to evaluate potential difficulties and possibilities in implementing P-DHIs to create better environments for their use before investing in them and when designing and implementing them. The cost-effectiveness of large-scale implementations is unknown; implementations are far more complicated than just downloading and using apps, and there is uncertainty accompanying implementations given the lack of coordinated control over the constructs and factors that influence the outcome.
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Affiliation(s)
- Keld Pedersen
- Information Systems, Department of Management, Aarhus University, Aarhus C, Denmark
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