Evaluation and Implementation of Digital Health Ecosystem in Academic Health Center

The rapid evolution of digital health technologies has created new opportunities to support preventive care, patient self-management, and health care system transformation. Wearable devices, mobile applications, remote monitoring tools, and interoperable digital platforms can provide continuous health information that may support both patients and health care providers in making informed decisions. These technologies are particularly relevant for lifestyle medicine, chronic disease prevention, remote patient monitoring, and health care delivery in underserved environments.

Despite increasing adoption of wearable technologies by the general population, significant barriers remain to their effective integration into routine clinical practice. Challenges include limited interoperability between devices and electronic health records, difficulties integrating data into clinical workflows, concerns related to usability and acceptability, and uncertainty regarding implementation in real-world health care settings. These challenges may be even greater in rural and remote communities where access to health services is often limited.

The Evaluation and Implementation of a Digital Health Ecosystem (e-IDEA) study aims to evaluate the implementation and feasibility of an interoperable digital health ecosystem that integrates wearable monitoring technologies with a secure digital platform capable of collecting, transmitting, storing, and displaying health information. The ecosystem is intended to support communication between patients and health care providers while facilitating health monitoring, lifestyle interventions, and clinical decision-making.

The study is funded through the Canadian Space Agency and is aligned with national priorities related to the development of innovative health technologies capable of supporting human health in remote, isolated, and extreme environments. Technologies that facilitate autonomous health monitoring may play an important role not only in remote Earth-based settings but also in future long-duration space exploration missions where access to direct medical support is limited.

This project uses a Hybrid Type 1 Effectiveness-Implementation design, allowing simultaneous evaluation of implementation outcomes and selected health-related outcomes. The study will be conducted in primary care and remote care settings where participants will be provided access to the digital health ecosystem and wearable monitoring technologies. Participants will be randomized to receive either the integrated digital health ecosystem in addition to usual primary care or usual primary care alone.

Participants will use wearable devices capable of collecting physiological and lifestyle-related information. Data collected may include measures such as physical activity, sleep patterns, heart rate, heart rate variability, blood pressure, oxygen saturation, body temperature, and other health indicators available through the digital platform. Participants will also complete validated questionnaires assessing health status, lifestyle behaviours, patient activation, technology acceptance, usability, implementation outcomes, self-regulation, physical activity, and perceived stress.

An exploratory component of the study will focus on stress assessment and monitoring. Stress is increasingly recognized as an important determinant of physical and mental health, influencing cardiovascular health, sleep quality, cognitive performance, emotional well-being, and overall quality of life. Traditional approaches to stress assessment often rely on self-reported questionnaires and may not adequately capture fluctuations in stress over time, particularly in remote, isolated, or operational environments.

Wearable technologies enable the continuous collection of physiological and behavioral data, including heart rate, heart rate variability, sleep characteristics, physical activity, and other physiological measures that may provide objective indicators of stress. Because there is currently no universally accepted gold standard for stress assessment, combining wearable-derived physiological data with validated self-reported stress measures may provide a more comprehensive understanding of stress in real-world settings. The study will therefore explore the relationship between wearable-derived physiological data and validated self-reported measures of stress and evaluate the feasibility of developing artificial intelligence (AI) and machine-learning models capable of estimating perceived stress in real-world settings.

This exploratory objective is particularly relevant for primary care, remote communities, and isolated, confined, and extreme (ICE) environments, including future space missions where access to traditional psychological assessment and healthcare services may be limited. The knowledge generated may contribute to the development of autonomous health monitoring systems capable of supporting early identification of stress and informing future preventive interventions.

The study will generate evidence regarding the implementation of an interoperable digital health ecosystem in routine clinical practice, with particular emphasis on implementation processes, user experience, interoperability, and integration within primary care and remote healthcare settings.

Implementation outcomes will be evaluated using established implementation science frameworks, including the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, supported by quantitative and qualitative methods. Feedback from participants, healthcare providers, and other stakeholders will be used to identify barriers and facilitators to implementation, inform iterative refinement of the digital health ecosystem, and optimize future deployment. A strengths, weaknesses, opportunities, and threats (SWOT) analysis will complement these evaluations.

Patient engagement principles will be integrated throughout the project. Stakeholders, including patients and health care providers, will contribute to the evaluation of user experience, implementation processes, and recommendations for future deployment.

Knowledge generated through this study is expected to contribute to the evidence base supporting the integration of interoperable digital health technologies into routine care. Findings may inform future implementation strategies in primary care, rural and remote communities, virtual care programs, and other settings where access to health services is constrained.

Furthermore, the study will generate preliminary evidence regarding the use of wearable-derived physiological signals and artificial intelligence approaches to monitor stress in clinical and operational environments. These findings may support future research aimed at developing personalized and scalable stress-monitoring solutions for healthcare, remote environments, and space exploration.

In addition, the study will provide valuable insights regarding the use of digital health ecosystems in isolated, confined, and extreme environments. These findings may inform the future development of autonomous health monitoring systems for human space exploration beyond low Earth orbit, while simultaneously generating practical benefits for underserved populations on Earth.

Inclusion criteria

• Adults aged 18 years or older.
• Registered patient receiving care at the participating primary care clinic (GMF-U Saint-Charles-Borromée).
• Have at least one stable chronic medical condition or cardiovascular risk factor requiring longitudinal follow-up.
• Able to safely engage in physical activity, as determined by their treating healthcare provider.
• Able to understand and communicate in English or French.
• Able and willing to provide written informed consent.
• Willing to comply with study procedures and scheduled follow-up visits.

Exclusion criteria

• Severe cognitive impairment or any condition preventing informed consent or study participation.
• Unstable or acute medical condition requiring immediate treatment or hospitalization.
• Medical contraindication to participating in physical activity, as determined by the treating healthcare
• Inability to use the digital health platform or wearable device despite appropriate support.
• Any condition that, in the opinion of the investigator, would compromise the participant's safety or the integrity of the study.