Matthias Ertl ^1,2* Lilla Gurtner ³

• ¹ Department of Psychology, University of Bern, Bern, Switzerland
• ² Lucerne Cantonal Hospital, University of Lucerne, Lucerne, Switzerland
• ³ Interdisciplinary Center for Sustainable Development and Environment (CDE), Faculty of Science and Natural Sciences, University of Bern, Bern, Bern, Switzerland

The repercussions for healthcare systems stem from two primary factors. First, an aging population means increasing prevalence of various disorders and conditions (e.g., Alzheimer's disease, stroke), as well as impairments (e.g., hearing loss) in need of treatment. In fact, a recent, global study (2) estimated that the number of people potentially in need of rehabilitation services already increased by 63% between 1990 and 2019, due to a growing and aging world population. Second, this escalating demand is set to collide with a shortage of healthcare professionals. The World Health Organization (WHO) foresees a deficit of 10 million health workers by 2030, impacting nations across all socioeconomic development levels, with a particularly bleak outlook for low-and lower-middle-income countries (3). Global inequalities already cause significant migration of health care workers from the Global South to the Global North, leaving the health care work force in the originating countries depleted (4). This confluence of heightened demand combined with a diminishing workforce poses a considerable challenge for healthcare systems (which are already contending with personnel shortages, low wages and high demands in flexibility, responsibility and physical and mental burden). If not prevented, it leads to a reduction in service provision and/or quality, which increases human suffering.Although the details may vary with respect to countries and regions, it is evident that many societies worldwide will face an aging population combined with a contracting workforce in the forthcoming decades, exerting significant ramifications on the healthcare sector. Consequently, there is an imperative need to formulate and implement mitigation strategies addressing this challenge across various domains, including rehabilitation. rehabilitation practice is relatively low. For example, the rates of prosthetic usage in upper-limb amputees has been estimated around 56%, meaning that 44% of patients abandoned their prosthetic (12). Thus, despite the improvements of the technology, the clinical reality still shows a high rejection rate of costintensive prosthetic devices. A review on the usage of mobile technologies (e.g., wearable sensors) reported even higher rejection rates of up to 65%, and explained this, among other reasons, by a lack of technology acceptance (13).Taken together, there is today a substantial disparity between the pace of technological innovations in rehabilitation and their tangible impact on day-to-day clinical practices. Keeping in mind the challenges of an aging population outlined above, the low adoption rates are even more concerning and make the acceptance of technology within the rehabilitation context a pivotal factor. Interventions to increase acceptance of existing (and future) technologies have the potential to improve therapy outcomes. Crucially, raising technology acceptance leads to more efficient use of existing resources. Compared to developing, scaling and commercializing yet another device, such interventions are cost-effective and rapidly deployable.The development of telemedicine in Ghana is a case in point (14; 15). This highlights interventions to increase technology acceptance as a lever for speeding up the translation of technological innovation to rehabilitation practice. However, technology acceptance has been largely overlooked in the rehabilitation context and needs to be addressed more actively. The Technology Acceptance Model (TAM) has been developed by economists to model the acceptance and usage of computer technology by a decision maker (16). The model suggests that only two predictors, perceived usefulness and perceived ease of use, determine the intention to use a technology. More recent models, for example the Unified Theory of Acceptance and Use of Technology (UTAUT) (17), assume a larger number of predictors (performance expectancy, effort expectancy, social influence, facilitating conditions) and identified moderators (gender, age, experience, voluntariness of use). Event though TAM has been extended to the realm of rehabilitation (18) (likely due to the absence of a more suitable alternative), both models cannot be used directly to create interventions to increase technology acceptance: Both models have been developed for situations that vary significantly from a rehabilitation context. For example, both models typically assume that the decision whether or not to use a technology is made by a single decision maker and is only made once. However, within the domain of rehabilitation, a patient's decision to adopt and use a specific technology is a complex, ongoing, and long-lasting process. Furthermore, the decision involves a multitude of stakeholders beyond patients, such as medical staff, physiotherapists, psychologists, political entities, and insurance companies. These stakeholders possess diverse preferences and, at least partially, conflicting interests. In addition, the scope of TAM is the perceived usefulness, defined as the extent to which a person believes that a technology will help them to perform their job, which is a rather restricted scope and does not translate readily to private technology use of for example elderly and retired patients.Therefore, the available, relatively simple models of technology acceptance can not capture the rehabilitation context well enough to generate interventions that target technology acceptance. To address this issue, we propose three next steps forward: 1) Adapting prevailing technology acceptance models to better map the intricacies of the rehabilitation context.2) Empirically assessing and validating existing and future technology acceptance models. To move forward in these directions, we argue that the human sciences (i.e. history, philosophy, sociology, psychology, justice studies, evolutionary biology, biochemistry, neurosciences, folklorists, and anthropology), and therein particularly psychology, have a largely untapped potential to contribute. First, it is noteworthy that a substantial portion of the factors and moderators in existing technology acceptance models show stronger associations with human sciences, such as psychology (e.g., expectancies) and sociology (e.g., social influence), as opposed to engineering or economics. Expectations and reasoning about the potential consequences of actions (e.g., using a technology) are clearly mental cognitive processes, and therefore psychologists and neuroscientists are best trained to investigate and model them. In particular, psychology, driven by its primary mission of elucidating and predicting human behavior, has a rich body of theories and concepts that can lead to next-generation theories of technology acceptance. For example, integrating perception of agency and self-efficacy of patients can shift the focus from the patient as a passive recipient of technology, towards a more holistic understanding of patients as actors in a multi-stakeholder setting. In addition, integrating social identity theory sees the patient as a social being for which technology adoption has not only health, but also social consequences (for example, using a prosthetic makes one's "weakness" clearly visible, a price that not everyone might be willing to pay). Thus, psychological theories can enrich development of future models of technology acceptance.Second, current and future models of technology acceptance need to be tested in clinical settings, for which the human sciences, and again, particularly psychology, are well-equipped with a rich methodological spectrum. Specifically, these methods allow capturing the social structures within which patients find themselves (for example, grouped in hospitals or retirement homes). This allows much more accurate inference about the underlying factors driving technology acceptance. That lack of incorporating the latest psychological advancements into decision-making processes in the medical field has extensively been criticized (19) and first attempts in this direction have already been published (20).