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BACKGROUND Due to growing pressure on the healthcare system, a shift in rehabilitation to the home setting is essential. However, efficient support in home-based rehabilitation is still lacking. The COVID-19 pandemic has further exacerbated these challenges, and affected individuals and healthcare professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet the clients’ needs and ease the growing pressure on the healthcare system it is necessary to give an overview of existing, relevant and future solutions shaping the constantly evolving market of technologies for home-based DR. OBJECTIVE This scoping review identifies digital technologies for home-based DR, predicts new/emerging DR trends and reports the influences of the COVID-19 pandemic on Digital Rehabilitation. METHODS The scoping review followed the framework of Arksey and O’Malley with the improvements of Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO and Cochrane library. The search spanned from January 2015 to January 2022. A bibliometric analysis was performed to give an overview of the included references and a co-occurrence analysis identified the technologies for home-based DR. A full-text-analysis of all included reviews filtered the trends for home-based DR. A grey literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic regarding the development of DR. RESULTS 2.437 records were included in the bibliometric analysis, 95 for full-text-analysis and 40 as a result of the grey literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital/mobile applications are already used in home-based DR, but AI/machine learning, exoskeletons, and digital/mobile applications represent new/emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches, but has not led to the development of new technologies. CONCLUSIONS Multiple tools are available and implemented for home-based DR, but some technologies face limitations in the application of home-based rehabilitation. However, AI and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the healthcare system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients’ needs and adhere to framework conditions even regardless of exceptional situations such as the COVID-19 pandemic. CLINICALTRIAL |
