An interventional study of Integrated Digital Health Ecosystem and Usual Primary Care in Digital Health Intervention, Remote Consultation, Teleconsultation and Lifestyle Factors, sponsored by Laval University. Not yet recruiting at 2 sites in Canada. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-09-21.
Sponsored by Laval University · Not applicable, Interventional, and Health services research
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Among 33 studies in Patient Participation with a site in Canada — 12 open to participants now, 13 under way, none of them phase 3. Patient Participation · Canada.
Among the 33 studies in Patient Participation with a site in Canada, this one is the 18th largest by planned enrollment of the 26 interventional ones that give a count, is one of 5 that started in 2026, is one of 24 with no phase, reaches primary completion later than 7 of the 12 other live ones, and was the 31st registered.
409 studies on the registry are indexed under Patient Participation; 100 are open to participants now.
Browse Patient Participation studies →12 of the 33 studies in Patient Participation with a site in Canada are open now. Recruiting first, the newest first:
University Health Network, Toronto · Sites in Canada
Ages 18–80
University of Alberta · Sites in Canada
Ages 18+
University Health Network, Toronto · Sites in Canada
Ages 18–40
University of British Columbia · Sites in Canada
Ages 6 mo–18 yrs
University Health Network, Toronto · Sites in Canada
Ages 18+ · Women only
A study can be open while its sites in Canada have not opened yet or have stopped. Only those recruiting at a site in Canada →
Of the 48 Patient Participation studies recruiting now, 32 (67%) set no upper age limit and 9 (19%) stop at 75 or younger; 10 take children under 18; 11 take healthy volunteers.
This study: 18 Years and older · all sexes · healthy volunteers too.
The full table on the hub →This tab as a report to print or save as PDF → With an account.
33 studies; the count open now beside each. This study's row is marked.
First in 2009; this study’s year is marked. 5 started in 2026.
By drug class, each counting the studies that test any of its medicines:
This study's planned enrollment of 60 is below the median of 128 across 322 interventional studies indexed under Patient Participation.
Median enrollment 149 across the 26 interventional studies, 75 across the 7 observational. Of the 16 that completed or were terminated, 2 (13%) have results posted.
This tab as a report to print or save as PDF → With an account.
1 of 33 have an industry lead sponsor. The 10 most frequent:
Laval University is the lead sponsor of 380 studies on the registry; 67 are open to participants now.
Laval University has 1 study filed under Patient Participation — fewer than ten, so there is no comparison to draw.
4 of the studies in Patient Participation with a site in Canada that are under way reach primary completion within a year, 0 of them phase 3. Phase 3 first, then by date:
| Study | Primary completion |
|---|---|
| NCT07686133University Health Network, TorontoDigital Education in Colorectal Cancer Patients | 2027-01 |
| NCT05371548Robyn TamblynSmart About Meds (SAM) RCT | 2027-03 |
| NCT07831837Laval UniversityEvaluation and Implementation of Digital Health Ecosystem in Academic Health Center | 2027-05-01 |
| NCT07818980University Health Network, TorontoCo-Adaptation of an Digital Pain Management Program for Canadian Veterans | 2027-09 |
Add all 4 to your calendar (.ics) → With an account.
Among the 33 studies in Patient Participation with a site in Canada, from their registry updates:
This tab as a report to print or save as PDF → With an account.
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.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
A reason a treatment, test or procedure should not be given to a particular person, such as an allergy, another illness or a medicine they take.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Contraindication
A reason a treatment, test or procedure should not be given to a particular person, such as an allergy, another illness or a medicine they take.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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 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 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.
Participants will receive routine primary care according to standard clinical practice throughout the 6-month study period without access to the digital health ecosystem.
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).
Time frame: 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.
Time frame: 6 months
Technology acceptance
measured using a Technology Acceptance Model (TAM)-based questionnaire
Time frame: 6 months
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).
Time frame: Baseline and 6 months
Physical activity level
Physical activity will be assessed using the Godin Leisure-Time Exercise Questionnaire.
Time frame: 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).
Time frame: Baseline, 3 months, and 6 months
Patient activation
Patient activation and self-management will be assessed using the Patient Activation Measure (PAM-13).
Time frame: 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.
Time frame: Baseline and 6 months
Patient self-management
Patient self-management will be assessed using the Self-Regulation Questionnaire (SRQ).
Time frame: Baseline and 6 months.
Perceived stress (PSS)
Perceived stress will be assessed using the Perceived Stress Scale (PSS)
Time frame: 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.
Time frame: 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.
Time frame: Continously throughout the 6-month study period
Waist circumference
Waist circumference will be measured using a measuring tape and reported in centimeters (cm).
Time frame: Baseline and 6 months
Body weight
body weight will be measured using a calibrated digital scale and reported in kilograms (Kg).
Time frame: Baseline and 6 months
Glycated hemoglobin (HbA1c)
Glycated hemoglobin (HbA1c) will be measured from blood samples and reported as a percentage (%).
Time frame: 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).
Time frame: 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).
Time frame: Baseline and 6 months
Total cholesterol
Total cholesterol will be measured from blood samples and reported in millimoles per liter (mmol/L).
Time frame: 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).
Time frame: Baseline and 6 months
Triglycerides
Triglycerides will be measured from blood samples and reported in millimoles per liter (mmol/L).
Time frame: Baseline and 6 months
Plan to share: No
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Start is estimated for Sep 2026; primary completion for May 2027, 7 months later.
Of 219 non-industry interventional studies with no phase under Patient Participation, half reached primary completion within 21 months of starting (the median), counting those still running; a quarter within 9 and three quarters within 34. This study's planned 7 months is below that.
Counting every sponsor, half of 226 studies reached it within 20 months; a quarter within 9 and three quarters within 34.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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