Digital coaching for healthcare is challenging due to the heterogeneous nature of data sources. This often leads to the development of ad-hoc pipelines customised to different combinations of formats, which are hard to maintain and easily fall out of date. In this paper, we present MatKG and HeLiFit (Health, Lifestyle and Fitness), which consist of a pipeline and an extended ontological model for scalable construction of a Knowledge Graph integrating electronic medical records, medical devices with consumer behavioural, and bio data. This fully-developed solution effectively addresses the challenge of semantic interoperability between healthcare institutions and consumer technology providers, using standards such as FHIR and RML supporting the construction of the cross-organisation health data space needed for powering a new generation of AI solutions. Its design and development were driven by a wide range of use cases and an equivalent number of digital coaching solutions for promoting health and lifestyle recommendations on different patient cohorts and healthcare institutions in 11 countries across Europe and Asia. We extended HeLiFit to accommodate a broader range of applications, including sleep and nutrition recommendations. This infrastructure is currently being employed by thousands of users and different pools of experts engaged in the validation of the generated recommendations. Moreover, in line with Open Science principles, we have made our system available as an off-the-shelf scalable solution that can fast-track innovation in the field of semantic AI for healthcare.