• Mcculloch, Ben
• Brunner, Melissa
• Welsh, Monica
• Togher, Leanne
• Williams, Liz
• Morrow, Rosemary
• Murphy, Marie Therese
• Rietdijk, Rachael
• Miao, Melissa
• Salomon, Alexander
• Debono, Deborah
• Wright, Meg Rebecca

Abstract

<jats:sec><jats:title>Background</jats:title><jats:p>Acquired brain injuries (ABIs), such as stroke and traumatic brain injury, commonly cause cognitive-communication disorders, in which underlying cognitive difficulties also impair communication. As communication is an exchange with others, close others such as family and friends also experience the impact of cognitive-communication impairment. It is therefore an internationally recommended best practice for speech-language pathologists to provide communication support to both people with ABI and the people who communicate with them. Current research also identifies a need for neurorehabilitation professionals to support digital communication, such as social media use, after ABI. However, with &gt;135 million people worldwide affected by ABI, alternate and supplementary service delivery models are needed to meet these communication needs. The “Social Brain Toolkit” is a novel suite of 3 interventions to deliver communication rehabilitation via the internet. However, digital health implementation is complex, and minimal guidance exists for ABI.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>This study aimed to support the implementation of the Social Brain Toolkit by coproducing implementation knowledge with people with ABI, people who communicate with people with ABI, clinicians, and leaders in digital health implementation.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>A maximum variation sample (N=35) of individuals with living experience of ABI, close others, clinicians, and digital health implementation leaders participated in an explanatory sequential mixed methods design. Stakeholders quantitatively prioritized 4 of the 7 theoretical domains of the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework as being the most important for Social Brain Toolkit implementation. Qualitative interview and focus group data collection focused on these 4 domains. Data were deductively analyzed against the NASSS framework with stakeholder coauthors to determine implementation considerations and strategies. A collaborative autoethnography of the research was conducted. Interrelationships between considerations and strategies were identified through a post hoc network analysis.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Across the 4 prioritized domains of “condition,” “technology,” “value proposition,” and “adopters,” 48 digital health implementation considerations and 52 tailored developer and clinician implementation strategies were generated. Benefits and challenges of coproduction were identified. The post hoc network analysis revealed 172 unique relationships between the identified implementation considerations and strategies, with user and persona testing and responsive design identified as the potentially most impactful strategies.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>People with ABI, close others, clinicians, and digital health leaders coproduced new knowledge of digital health implementation considerations for adults with ABI and the people who communicate with them, as well as tailored implementation strategies. Complexity-informed network analyses offered a data-driven method to identify the 2 most potentially impactful strategies. Although the study was limited by a focus on 4 NASSS domains and the underrepresentation of certain demographics, the wealth of actionable implementation knowledge produced supports future coproduction of implementation research with mutually beneficial outcomes for stakeholders and researchers.</jats:p></jats:sec><jats:sec><jats:title>International Registered Report Identifier (IRRID)</jats:title><jats:p>RR2-10.2196/35080</jats:p></jats:sec>

Topics

• impedance spectroscopy
• size-exclusion chromatography