Evaluation and Implementation of Digital Health Ecosystem in Academic Health Center (e-IDEA)

Evaluation and Implementation of Digital Health Ecosystem in Primary Care and Remote Area and Space Exploration

Mobile health technologies, such as smartwatches, wearable sensors, and digital applications, have the potential to support healthier lifestyles and improve the management of health conditions. These technologies can collect information on physical activity, sleep, heart rate, heart rate variability, respiratory rate, and other health indicators. However, integrating data from these devices into routine health care and electronic health records remains challenging, particularly in primary care settings and remote communities.

The purpose of this study is to evaluate the implementation and feasibility of an integrated digital health ecosystem that combines wearable monitoring using smartwatches with a secure digital platform. The ecosystem is designed to collect, transmit, and display health information in a way that may support patients, health care providers, and researchers.

Participants will use wearable technologies and complete questionnaires while health-related information is collected through the digital platform. The study will assess the usability, acceptability, interoperability, and feasibility of implementing this technology in real-world settings, including primary care clinics and remote communities. The study will also explore the potential use of wearable-derived physiological data to support the assessment, monitoring, and management of physiological and psychological stress.

The knowledge gained from this study may help improve the integration of digital health technologies into routine care, support self-management and preventive health strategies, and facilitate access to health monitoring in underserved environments. Findings may also contribute to the development of digital health solutions for isolated and extreme environments, including future space exploration missions.

This research aligns with priorities identified by the Canadian Space Agency to advance technologies that support safe, healthy, and productive human activities in remote and extreme environments on Earth and during future space missions beyond low Earth orbit.

Study Overview

• Status: Not yet recruiting
• Conditions: Stress; Lifestyle Factors; Remote Consultation, Teleconsultation; Primary Care Patients With Chronic Conditions; Digital Health Intervention; Space Analogue
• Intervention / Treatment: Device: Integrated Digital Health Ecosystem; Other: Usual Primary Care

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Estimated): 60
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Caroline Rhéaume, MD, PhD; Phone Number: 418-656-2131; Email: caroline.rheaume@fmed.ulaval.ca

Study Locations

• Canada
  • Québec, Canada
    • Caroline Rhéaume
  • Quebec
    • Québec, Quebec, Canada, G1V0A6
      • Université Laval
      • Contact: Caroline Rhéaume, MD, PhD; Phone Number: 418-656-2131; Email: caroline.rheaume@fmed.ulaval.ca
      • Contact: Laïla Badr, MSc; Phone Number: 12286 418-663-5313; Email: laila.badr.ciussscn@ssss.gouv.qc.ca
      • Principal Investigator: Caroline Rhéaume, BSc, MSc, MD, PhD, FCFP
      • Sub-Investigator: Jean-Sébastien Paquet, MD, MSc
      • Sub-Investigator: Raphaëlle Giguère, BSc, MSc
      • Sub-Investigator: Léanie Moreau, MSc
      • Sub-Investigator: Marie-Pierre Gagnon, PhD
      • Sub-Investigator: Maxime Sasseville, PhD
      • Sub-Investigator: Darine Ayemed, PhD
      • Sub-Investigator: Christian Chabot
      • Sub-Investigator: Neila Mezghani, PhD
      • Sub-Investigator: Pierre Cordeau, PhD
      • Sub-Investigator: Alexandre Marois, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Digital Health Ecosystem; Participants randomized to the intervention group will receive usual primary care in addition to a digital health ecosystem integrating wearable technology, remote health monitoring, and personalized lifestyle medicine support throughout the 6-month study period	Device: Integrated Digital Health Ecosystem; Participants will use an integrated digital health ecosystem that combines wearable technology, remote health monitoring, a secure digital platform, and personalized lifestyle medicine support. The system enables the collection, transmission, visualization, and integration of health-related information to support patient self-management and communication with healthcare providers during the 6-month study period.
Active Comparator: Usual Primary Care; Participants randomized to the control group will receive usual primary care supported by the existing electronic health record system and will not receive the digital health ecosystem during the 6-month study period.	Other: Usual Primary Care; Participants will receive routine primary care according to standard clinical practice throughout the 6-month study period without access to the digital health ecosystem.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Appropriateness of the digital health ecosystem assessed using the Intervention Appropriateness Measure (IAM)	Appropriateness of the digital health ecosystem will be assessed using the Intervention Appropriateness Measure (IAM), a 4-item questionnaire. Each item is rated on a 5-point Likert scale, ranging from 1 (completely disagree) to 5 (completely agree). The four item scores will be averaged to generate a mean score ranging from 1 to 5, with higher scores indicating greater perceived appropriateness of the intervention (the digital health ecosystem).	6 months
Usability of the digital health ecosystem assessed using the System Usability Scale (SUS)	Usability of the digital health ecosystem will be assessed using the System Usability Scale (SUS), a validated 10-item questionnaire. Items are rated on a 5-point Likert scale. Item scores are converted and summed to generate an overall score ranging from 0 to 100, with higher scores indicating greater perceived usability of the digital health ecosystem.	6 months
Technology acceptance	measured using a Technology Acceptance Model (TAM)-based questionnaire	6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Lifestyle medicine behaviours	Lifestyle medicine behaviours across the six pillars will be assessed using a French lifestyle medicine questionnaire inspired by the American College of Lifestyle Medicine (ACLM).	Baseline and 6 months
Physical activity level	Physical activity will be assessed using the Godin Leisure-Time Exercise Questionnaire.	Baseline, 3 months, and 6 months
Motivation toward physical activity	Motivation toward physical activity will be assessed using the Behavioral Regulation in Exercise Questionnaire (BREQ-2).	Baseline, 3 months, and 6 months
Patient activation	Patient activation and self-management will be assessed using the Patient Activation Measure (PAM-13).	Baseline and 6 months
Psychological status assessed using the Depression Anxiety and Stress Scale-21 (DASS-21)	Depression, anxiety, and stress symptoms will be assessed using the 21-item Depression Anxiety and Stress Scale (DASS-21). The questionnaire includes three 7-item subscales assessing depression, anxiety, and stress. Each item is rated from 0 to 3. Raw scores for each subscale range from 0 to 21, with higher scores indicating greater symptom severity.	Baseline and 6 months
Patient self-management	Patient self-management will be assessed using the Self-Regulation Questionnaire (SRQ).	Baseline and 6 months.
Perceived stress (PSS)	Perceived stress will be assessed using the Perceived Stress Scale (PSS)	Baseline, monthly, and 6 months
Perceived stress (VAS-S)	Perceived stress will be assessed using Visual Analogue Scale for Stress (VAS-S). Participant rate perceived stress from 0 (no stress) to 10 (maximum stress). Higher scores indicates greater perceived stress.	Baseline, weekly, and 6 months
Physiological stress measured using wearable-derived physiological indicators	Physiological stress will be explored using wearable-derived indicators including heart rate variability and other available physiological metrics.	Continously throughout the 6-month study period

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Waist circumference	Waist circumference will be measured using a measuring tape and reported in centimeters (cm).	Baseline and 6 months
Body weight	body weight will be measured using a calibrated digital scale and reported in kilograms (Kg).	Baseline and 6 months
Glycated hemoglobin (HbA1c)	Glycated hemoglobin (HbA1c) will be measured from blood samples and reported as a percentage (%).	Baseline and 6 months
Systolic blood pressure	Systolic blood pressure will be measured using an automated blood pressure monitor and reported in millimeters of mercury (mmHg).	Baseline and 6 months
Diastolic blood pressure	Systolic blood pressure will be measured using an automated blood pressure monitor and reported in millimeters of mercury (mmHg).	Baseline and 6 months
Total cholesterol	Total cholesterol will be measured from blood samples and reported in millimoles per liter (mmol/L).	Baseline and 6 months
Low-density lipoprotein cholesterol (LDL-C)	High-density lipoprotein cholesterol (HDL-C) will be measured from blood samples and reported in millimoles per liter (mmol/L).	Baseline and 6 months
Triglycerides	Triglycerides will be measured from blood samples and reported in millimoles per liter (mmol/L).	Baseline and 6 months

Collaborators and Investigators

• Sponsor: Laval University
• Collaborators: Canadian Space Agency

Study record dates

Study Major Dates

• Study Start (Estimated): September 20, 2026
• Primary Completion (Estimated): May 1, 2027
• Study Completion (Estimated): May 1, 2028

Study Registration Dates

• First Submitted: June 30, 2026
• First Submitted That Met QC Criteria: September 15, 2026
• First Posted (Actual): September 21, 2026

Study Record Updates

• Last Update Posted (Actual): September 21, 2026
• Last Update Submitted That Met QC Criteria: September 15, 2026
• Last Verified: September 1, 2026

More Information

Terms related to this study

• Keywords: Telemedicine; Primary Care; Heart Rate Variability; Artificial Intelligence; Electronic Health Records; Patient Engagement; Lifestyle Medicine; Stress management; Remote Patient Monitoring; Wearable Devices; Digital Health Technology; Mobile Health (mHealth); Hybrid Effectiveness-Implementation Design; Space Medicine; Digital Health Ecosystem; Interoperability; Remote Communities; Isolated, Confined and Extreme Environments (ICE); Autnonomous Health Monitoring
• Additional Relevant MeSH Terms: Behavior; Treatment Adherence and Compliance; Health Behavior; Patient Acceptance of Health Care; Patient Participation
• Other Study ID Numbers: 2025-355

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No