Factors influencing older adults’ acceptance and usability of assistive technology services: A longitudinal multilevel analysis

• 78 participants in a 12-month real-world assistive technology study. • Two user profiles differ in digital skills, technostress, and loneliness. • Mixed-effects models identified independent predictors over time. • Technostress and loneliness are consistent negative predictors over time. Assistive technologies have the potential to enhance the quality of life of older adults and their caregivers. However, long-term adoption in real-life settings remains limited, and the non-technological factors influencing sustained use are not fully explored. This study investigates how socio-demographic, psychological, and caregiving-related variables affect the acceptance and usability of assistive technologies over time. Seventy-eight community-dwelling older adults participated in a one-year pilot evaluating a socialization platform and a health/environmental monitoring system. Usability (SUS) and acceptance (Almere Model constructs) were assessed at baseline, 6 months, and 12 months. Baseline socio-demographic and psychosocial variables were used to derive user profiles through k-means clustering. Linear mixed-effects models examined profile-dependent longitudinal trajectories, complemented by regression-based models testing independent predictors. Two distinct baseline profiles were identified, differing in age, digital skills, technostress, loneliness, and perceived support. Profile membership predicted divergent longitudinal trajectories across usability and multiple acceptance domains. The digitally resilient profile demonstrated higher usability and more favourable changes over time. Across regression models, technostress and loneliness consistently emerged as robust negative predictors, independent of time and scenario. Attrition analyses did not reveal systematic baseline differences between completers and dropouts. Technology acceptance among older adults is structured by baseline psychosocial and digital configurations rather than exposure alone. Integrating baseline profiling with longitudinal modelling provides a framework to understand heterogeneity in gerontechnology adoption and highlights modifiable factors that may support sustained engagement.