[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100656730":3},{"organization":4,"outcomesModule":7,"designInfo":77,"detailedDescription":86,"studyPopulation":82,"armGroups":87,"interventions":100,"overallOfficials":82,"centralContacts":109,"locations":115,"responsibleParty":165,"collaborators":167,"id":171,"slug":172,"hasResults":173,"nctId":174,"briefTitle":175,"officialTitle":176,"acronym":177,"eligibilityCriteria":178,"healthyVolunteers":179,"sex":180,"minAge":181,"maxAge":82,"enrollmentInfo":182,"targetDuration":82,"studyType":185,"phases":186,"briefSummary":188,"conditions":189,"keywords":196,"overallStatus":215,"whyStopped":82,"lastUpdateSubmitDate":216,"lastUpdatePostDateStruct":217,"startDateStruct":220,"completionDateStruct":222,"leadSponsor":224,"locationsCount":225},{"fullName":5,"class":6},"Laval University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":19,"otherOutcomes":53},[9,13,16],{"measure":10,"description":11,"timeFrame":12},"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",{"measure":14,"description":15,"timeFrame":12},"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.",{"measure":17,"description":18,"timeFrame":12},"Technology acceptance","measured using a Technology Acceptance Model (TAM)-based questionnaire",[20,24,28,31,34,37,41,45,49],{"measure":21,"description":22,"timeFrame":23},"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",{"measure":25,"description":26,"timeFrame":27},"Physical activity level","Physical activity will be assessed using the Godin Leisure-Time Exercise Questionnaire.","Baseline, 3 months, and 6 months",{"measure":29,"description":30,"timeFrame":27},"Motivation toward physical activity","Motivation toward physical activity will be assessed using the Behavioral Regulation in Exercise Questionnaire (BREQ-2).",{"measure":32,"description":33,"timeFrame":23},"Patient activation","Patient activation and self-management will be assessed using the Patient Activation Measure (PAM-13).",{"measure":35,"description":36,"timeFrame":23},"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.",{"measure":38,"description":39,"timeFrame":40},"Patient self-management","Patient self-management will be assessed using the Self-Regulation Questionnaire (SRQ).","Baseline and 6 months.",{"measure":42,"description":43,"timeFrame":44},"Perceived stress (PSS)","Perceived stress will be assessed using the Perceived Stress Scale (PSS)","Baseline, monthly, and 6 months",{"measure":46,"description":47,"timeFrame":48},"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",{"measure":50,"description":51,"timeFrame":52},"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",[54,57,60,63,66,68,71,74],{"measure":55,"description":56,"timeFrame":23},"Waist circumference","Waist circumference will be measured using a measuring tape and reported in centimeters (cm).",{"measure":58,"description":59,"timeFrame":23},"Body weight","body weight will be measured using a calibrated digital scale and reported in kilograms (Kg).",{"measure":61,"description":62,"timeFrame":23},"Glycated hemoglobin (HbA1c)","Glycated hemoglobin (HbA1c) will be measured from blood samples and reported as a percentage (%).",{"measure":64,"description":65,"timeFrame":23},"Systolic blood pressure","Systolic blood pressure will be measured using an automated blood pressure monitor and reported in millimeters of mercury (mmHg).",{"measure":67,"description":65,"timeFrame":23},"Diastolic blood pressure",{"measure":69,"description":70,"timeFrame":23},"Total cholesterol","Total cholesterol will be measured from blood samples and reported in millimoles per liter (mmol\u002FL).",{"measure":72,"description":73,"timeFrame":23},"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\u002FL).",{"measure":75,"description":76,"timeFrame":23},"Triglycerides","Triglycerides will be measured from blood samples and reported in millimoles per liter (mmol\u002FL).",{"allocation":78,"interventionModel":79,"interventionModelDescription":80,"primaryPurpose":81,"observationalModel":82,"timePerspective":82,"maskingInfo":83},"RANDOMIZED","PARALLEL","Participants will be randomized in a 1:1 ratio to one of the two parallel groups. The intervention group will receive a digital health ecosystem integrating a smartwatch, remote health monitoring, and personalized lifestyle medicine support, in addition to usual primary care. The control group will receive usual primary care alone, supported by the existing electronic health record system.","HEALTH_SERVICES_RESEARCH",null,{"masking":84,"maskingDescription":85,"whoMasked":82},"NONE","Due to the nature of the intervention, participants, health care providers, and study personnel will not be blinded to group allocation. Participants assigned to the intervention group will receive the digital health ecosystem, including wearable technology and remote health monitoring, in addition to usual primary care, while participants assigned to the control group will receive usual primary care alone supported by the existing electronic health record system.","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.\n\nDespite 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.\n\nThe 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.\n\nThe 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.\n\nThis 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.\n\nParticipants 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.\n\nAn 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.\n\nWearable 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.\n\nThis 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.\n\nThe 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.\n\nImplementation 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.\n\nPatient 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.\n\nKnowledge 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.\n\nFurthermore, 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.\n\nIn 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.",[88,94],{"label":89,"type":90,"description":91,"interventionNames":92},"Digital Health Ecosystem","EXPERIMENTAL","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",[93],"Device: Integrated Digital Health Ecosystem",{"label":95,"type":96,"description":97,"interventionNames":98},"Usual Primary Care","ACTIVE_COMPARATOR","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.",[99],"Other: Usual Primary Care",[101,106],{"type":102,"name":103,"description":104,"armGroupLabels":105,"otherNames":82},"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.",[89],{"type":6,"name":95,"description":107,"armGroupLabels":108,"otherNames":82},"Participants will receive routine primary care according to standard clinical practice throughout the 6-month study period without access to the digital health ecosystem.",[95],[110],{"name":111,"role":112,"phone":113,"phoneExt":82,"email":114},"Caroline Rhéaume, MD, PhD","CONTACT","418-656-2131","caroline.rheaume@fmed.ulaval.ca",[116,160],{"facility":117,"status":82,"city":118,"state":119,"zip":120,"country":121,"countryCode":122,"cosmosGeoPoint":123,"geoPoint":128,"contacts":129},"Université Laval","Québec","Quebec","G1V0A6","Canada","CA",{"type":124,"coordinates":125},"Point",[126,127],-71.21454,46.81228,{"lat":127,"lon":126},[130,131,136,139,142,144,146,148,150,152,154,156,158],{"name":111,"role":112,"phone":113,"phoneExt":82,"email":114},{"name":132,"role":112,"phone":133,"phoneExt":134,"email":135},"Laïla Badr, MSc","418-663-5313","12286","laila.badr.ciussscn@ssss.gouv.qc.ca",{"name":137,"role":138,"phone":82,"phoneExt":82,"email":82},"Caroline Rhéaume, BSc, MSc, MD, PhD, FCFP","PRINCIPAL_INVESTIGATOR",{"name":140,"role":141,"phone":82,"phoneExt":82,"email":82},"Jean-Sébastien Paquet, MD, MSc","SUB_INVESTIGATOR",{"name":143,"role":141,"phone":82,"phoneExt":82,"email":82},"Raphaëlle Giguère, BSc, MSc",{"name":145,"role":141,"phone":82,"phoneExt":82,"email":82},"Léanie Moreau, MSc",{"name":147,"role":141,"phone":82,"phoneExt":82,"email":82},"Marie-Pierre Gagnon, PhD",{"name":149,"role":141,"phone":82,"phoneExt":82,"email":82},"Maxime Sasseville, PhD",{"name":151,"role":141,"phone":82,"phoneExt":82,"email":82},"Darine Ayemed, PhD",{"name":153,"role":141,"phone":82,"phoneExt":82,"email":82},"Christian Chabot",{"name":155,"role":141,"phone":82,"phoneExt":82,"email":82},"Neila Mezghani, PhD",{"name":157,"role":141,"phone":82,"phoneExt":82,"email":82},"Pierre Cordeau, PhD",{"name":159,"role":141,"phone":82,"phoneExt":82,"email":82},"Alexandre Marois, PhD",{"facility":161,"status":82,"city":118,"state":82,"zip":82,"country":121,"countryCode":122,"cosmosGeoPoint":162,"geoPoint":164,"contacts":82},"Caroline Rhéaume",{"type":124,"coordinates":163},[126,127],{"lat":127,"lon":126},{"type":138,"investigatorFullName":161,"investigatorTitle":166,"investigatorAffiliation":5,"oldNameTitle":82,"oldOrganization":82},"Principal investigator, MD, PhD",[168],{"name":169,"class":170},"Canadian Space Agency","UNKNOWN","100656730","evaluation-and-implementation-of-digital-health-ecosystem-in-academic-health-center-100656730",false,"NCT07831837","Evaluation and Implementation of Digital Health Ecosystem in Academic Health Center","Evaluation and Implementation of Digital Health Ecosystem in Primary Care and Remote Area and Space Exploration","e-IDEA","Inclusion Criteria:\n\n* Adults aged 18 years or older.\n* Registered patient receiving care at the participating primary care clinic (GMF-U Saint-Charles-Borromée).\n* Have at least one stable chronic medical condition or cardiovascular risk factor requiring longitudinal follow-up.\n* Able to safely engage in physical activity, as determined by their treating healthcare provider.\n* Able to understand and communicate in English or French.\n* Able and willing to provide written informed consent.\n* Willing to comply with study procedures and scheduled follow-up visits.\n\nExclusion Criteria:\n\n* Severe cognitive impairment or any condition preventing informed consent or study participation.\n* Unstable or acute medical condition requiring immediate treatment or hospitalization.\n* Medical contraindication to participating in physical activity, as determined by the treating healthcare\n* Inability to use the digital health platform or wearable device despite appropriate support.\n* Any condition that, in the opinion of the investigator, would compromise the participant's safety or the integrity of the study.",true,"ALL","18 Years",{"count":183,"type":184},60,"ESTIMATED","INTERVENTIONAL",[187],"NA","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.\n\nThe 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.\n\nParticipants 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.\n\nThe 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.\n\nThis 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.",[190,191,192,193,194,195],"Digital Health Intervention","Remote Consultation, Teleconsultation","Lifestyle Factors","Stress","Primary Care Patients With Chronic Conditions","Space Analogue",[89,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214],"Digital Health Technology","Wearable Devices","Remote Patient Monitoring","Electronic Health Records","Mobile Health (mHealth)","Interoperability","Lifestyle Medicine","Primary Care","Hybrid Effectiveness-Implementation Design","Patient Engagement","Telemedicine","Artificial Intelligence","Stress management","Heart Rate Variability","Remote Communities","Isolated, Confined and Extreme Environments (ICE)","Space Medicine","Autnonomous Health Monitoring","NOT_YET_RECRUITING","2026-09-15",{"date":218,"type":219},"2026-09-21","ACTUAL",{"date":221,"type":184},"2026-09-20",{"date":223,"type":184},"2028-05-01",{"name":5,"class":6},2]