Overcoming obstacles: examining user resistance to home-based health monitoring systems among older adults

Home-based Health Monitoring Systems (HHMSs) have rapidly emerged as a promising tool for health management among older adults. By providing real-time, easily accessible health information, HHMSs can facilitate proactive health management and potentially improve health outcomes for older adults. However, despite the potential benefits of this technology, its adoption by older adults remains a complex challenge. Older adults’ unique perceptions and attitudes toward technology often create barriers to effectively using these systems. While numerous studies have explored the factors affecting technology acceptance among older adults, there is a relative lack of research into the factors contributing to resistance towards health monitoring technologies. The present study aims to fill this gap by investigating the factors influencing older adults’ resistance to HHMSs. To this end, the study integrates two key theoretical frameworks: status quo bias theory and task-technology fit theory. We conducted an empirical study investigating factors influencing older adults’ resistance to HHMSs. Data was collected from 483 seniors aged 55 and above through an online survey. Based on this data, a structural model of user resistance was proposed and tested using the partial least squares structural equation modeling (PLS-SEM) method. The results revealed that inertia and technology anxiety accounted for 43.5% of the variance in user resistance. Among these factors, technology anxiety was found to strongly impact user resistance (p < 0.01), suggesting that fears or apprehensions about using the technology can significantly deter older adults from adopting HHMSs. The study also found that inertia, switching costs, and switching benefits significantly influenced technology anxiety (all p values < 0.01). When older adults perceive higher benefits and lower costs of switching to a new technology, they may experience lower anxiety. Furthermore, if the required task and technology are fit, they are more willing to switch to the new technology. It’s worth noting that the study found that task-technology fit did not significantly affect technology anxiety and user resistance (p > 0.05), suggesting that even if a technology perfectly aligns with an older adult’s tasks, it may not necessarily reduce their anxiety or resistance towards it. This study dedicated that the development of HHMS should consider the relationship between task characteristics and technology capabilities, identify tasks in specific contexts, and gain insight into older adults’ emotional and psychological anxieties. It also provides valuable insights into designing effective, user-friendly, and easy-to-use systems for older adults.