What explains the older users’ continuance intention to use digital healthcare technologies in the UK?

Abstract

Research background: As the global population ages, healthcare systems face increasing pressure to manage age-related health conditions. Digital healthcare technologies (DHTs), such as wearable devices, eHealth services, telemedicine platforms, and mHealth apps, offer solutions for promoting healthy ageing and reducing healthcare problems. However, the continued use of these technologies by older adults remains challenging, with many users discontinuing use after initial adoption. Existing research has focused predominantly on adoption, with limited attention on factors influencing post-adoption behaviour and long-term engagement. This study addresses this gap by extending the Expectation Confirmation Model (ECM) to incorporate ageing-specific factors, aiming to understand the determinants of older users’ continuance intention to use Digital healthcare technologies. Aim and Objectives: This research aims to address the significant gaps in the understanding of older users’ continued use of digital healthcare technologies, particularly the lack of consideration for ageing-specific characteristics, needs, expectations, capabilities, and limitations in existing models. The study seeks to identify the factors influencing older users’ continuance intention to use these technologies by extending the Expectation Confirmation Model (ECM) and developing a theoretical framework. Additionally, it investigates the impact of ageing-specific constructs on the continued use of digital healthcare technologies among older users. Methodology: An exploratory sequential mixed-methods approach was adopted. The qualitative phase involved semi-structured interviews with older users of wearable technology devices in the UK to explore their experiences, motivations, and perceptions. Thematic analysis, using NVivo software, was employed to identify key themes that informed the subsequent quantitative phase. In the quantitative phase, a survey was conducted with a broader group of older users of digital healthcare technologies in the UK to test the relationships between the identified factors. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to assess the validity and reliability of the proposed theoretical model. Key Findings: The quantitative phase of this research, using PLS-SEM analysis, confirmed several key relationships. Health consciousness positively influences perceived usefulness, while confirmation of expectation significantly impacts both perceived usefulness and satisfaction. Perceived usefulness also has a strong positive effect on satisfaction. Additionally, satisfaction positively influences both ageing satisfaction and continuance intention to use digital healthcare technologies. Ageing satisfaction significantly predicts continuance intention and mediates the relationship between satisfaction and continuance intention. Lastly, technology anxiety moderates the relationship between satisfaction and continuance intention, weakening the likelihood of continued use for those with higher anxiety. Contributions: This study offers valuable insights into the factors influencing older users’ continued use of digital healthcare technologies, a crucial element for ensuring their long-term success. By incorporating ageing-specific factors into the Expectation Confirmation Model (ECM), this research enhances the theoretical understanding of post-adoption behaviour among older users, refining the model to better capture sustained use in the context of health management. Additionally, the study bridges disciplines, including information systems, gerontology, healthcare, and psychology, providing actionable insights for developers, healthcare providers, policymakers, and brand managers to design and promote age-appropriate technologies that support healthy ageing.