{"id":617351,"date":"2026-05-16T06:42:29","date_gmt":"2026-05-16T04:42:29","guid":{"rendered":"https:\/\/www.dynseo.com\/quelles-donnees-de-vie-reelle-peut-on-recolter-lors-dune-etude-clinique-dynseo-2\/"},"modified":"2026-05-16T06:44:48","modified_gmt":"2026-05-16T04:44:48","slug":"what-real-world-data-can-be-collected-during-a-clinical-study","status":"publish","type":"post","link":"https:\/\/www.dynseo.com\/en\/what-real-world-data-can-be-collected-during-a-clinical-study\/","title":{"rendered":"What real-world data can be collected during a clinical study?"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;Article HTML&#8221; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;0px||0px||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row admin_label=&#8221;Contenu&#8221; _builder_version=&#8221;4.16&#8243; width=&#8221;100%&#8221; max_width=&#8221;100%&#8221; custom_padding=&#8221;0px||0px||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; 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font-weight: 600; }\n.dbi-art-dffa9a .footer-links { display: flex; gap: 20px; justify-content: center; flex-wrap: wrap; margin-top: 15px; }\n.dbi-art-dffa9a .faq-item { background: white; border-radius: 15px; padding: 28px 32px; margin: 16px 0; box-shadow: 0 3px 15px rgba(94,94,215,0.08); border-left: 4px solid #a9e2e4; }\n.dbi-art-dffa9a .faq-item h4 { font-family: 'Montserrat', sans-serif; color: #5e5ed7; margin-bottom: 13px; font-size: 1.08rem; }\n.dbi-art-dffa9a .faq-item p { margin: 0; color: #555; }\n.dbi-art-dffa9a a { color: #5e5ed7; }\n@media (max-width: 768px) {\n.dbi-art-dffa9a .article-header h1 { font-size: 1.85rem; }\n.dbi-art-dffa9a .stats-grid { grid-template-columns: 1fr; }\n.dbi-art-dffa9a .article-header { padding: 45px 15px; }\n.dbi-art-dffa9a .container { padding: 15px; }\n.dbi-art-dffa9a h2 { font-size: 1.6rem; }\n}<\/p>\n<\/style>\n<div class=\"dbi-art-dffa9a\">\n<article>\n<header class=\"article-header\">\n<div class=\"article-category\">\ud83d\udd2c Mental health &amp; Clinical research \u2014 Real-world data<\/div>\n<h1>What real-world data can be collected during a clinical study?<\/h1>\n<pee class=\"subtitle\">Traditional clinical trials capture only a fraction of patients&#8217; reality. Real-world data \u2014 collected via mobile applications, connected devices, EMA, and registries \u2014 revolutionize our understanding of cognitive and mental disorders. A comprehensive guide for researchers, clinicians, and patients.<\/pee>\n<\/header>\n<div class=\"container\">\n<div class=\"intro-paragraph\">\nFor decades, clinical research has operated under a proven model: a randomized controlled trial, a carefully selected population, standardized measures at fixed intervals in a clinical setting. This model remains the gold standard for establishing causality \u2014 but it has a major limitation: it does not capture real life. Mental health, in particular, is deeply affected by daily contexts \u2014 work stress, sleep quality, social interactions, weather \u2014 that point-in-time assessments in consultations cannot grasp. Real-world data (RWD) fill this gap by transforming patients&#8217; daily lives into a laboratory.\n<\/div>\n<div class=\"stats-grid\">\n<div class=\"stat-card\"><span class=\"stat-number\">\u00d710<\/span><\/p>\n<div class=\"stat-label\">the amount of available digital health data doubles every 3-4 years \u2014 a revolution for research<\/div>\n<\/div>\n<div class=\"stat-card\"><span class=\"stat-number\">80%<\/span><\/p>\n<div class=\"stat-label\">of clinical studies will integrate RWD by 2030 according to forecasts from EMA and FDA<\/div>\n<\/div>\n<div class=\"stat-card\"><span class=\"stat-number\">EMA<\/span><\/p>\n<div class=\"stat-label\">the European Medicines Agency published its strategy on real-world data in 2023<\/div>\n<\/div>\n<\/div>\n<h2>Definitions: RWD, RWE and their fundamental differences<\/h2>\n<pee>Before exploring the types of data and collection methods, it is essential to clarify the vocabulary \u2014 as the terms are often incorrectly used interchangeably.<\/pee>\n<pee><strong>Real-World Data (RWD)<\/strong> \u2014 or real-world data \u2014 are all data related to patients&#8217; health status and care delivery collected outside of randomized controlled clinical trials. They can come from electronic health records, reimbursement databases, patient registries, wearable sensors, mobile applications, health social networks, or observational studies.<\/pee>\n<pee><strong>Real-World Evidence (RWE)<\/strong> \u2014 real-world evidence \u2014 are the clinical evidence generated by the rigorous analysis of RWD. RWD are the raw material; RWE are the result of applying scientific methodology to this material. Both the FDA and EMA have developed frameworks to accept RWE in marketing authorization submissions \u2014 a major transformation for the pharmaceutical industry and biomedical research.<\/pee>\n<h3>Why are RWD crucial in mental and cognitive health?<\/h3>\n<pee>Mental and cognitive disorders have characteristics that make them particularly difficult to study within the framework of traditional clinical trials. Intra-individual variability is considerable \u2014 a depressed patient may feel very differently from Monday to Friday, or depending on the season, or their relational context. This variability is invisible in a monthly consultation assessment. Similarly, the cognitive manifestations of disorders like ADHD, the aftermath of Stroke, or the early stages of Alzheimer&#8217;s disease are deeply contextual \u2014 the environment, fatigue, stress modulate them in real time.<\/pee>\n<pee>RWD allow us to capture this dynamic complexity. They make visible what happens between consultations, in the real lives of patients \u2014 which represents 99% of their existence.<\/pee>\n<h2>The major categories of real-world data in clinical settings<\/h2>\n<h3>1. Health system data (administrative and clinical data)<\/h3>\n<pee>These are the most commonly used RWD in observational research. They include electronic health records (EHR), reimbursement data from Health Insurance (SNDS in France \u2014 National Health Data System), patient registries (cancer registries, rare disease registries, Alzheimer registries), hospital databases (PMSI, drug databases), and prescription data. These databases are valuable for large-scale epidemiological studies \u2014 they allow the analysis of hundreds of thousands or even millions of care pathways. Their limitation is that they only capture what is coded and reimbursed \u2014 they miss subjective, behavioral, and contextual data.<\/pee>\n<div class=\"method-card blue\">\n<div class=\"method-badge badge-blue\">\ud83c\udfe5 The SNDS in France<\/div>\n<h4>The largest health data repository in Europe<\/h4>\n<pee>The National Health Data System (SNDS) covers all care reimbursements for the 67 million insured individuals in France, making it one of the largest health databases in the world. Its access is regulated by the Health Data Hub and requires authorization from the CNIL. For mental health research, it allows the study of care trajectories, treatment adherence, comorbidities, and large-scale hospitalizations \u2014 but does not contain data on symptoms, daily functioning, or quality of life.<\/pee>\n<\/div>\n<h3>2. Data collected by patients themselves (PRO)<\/h3>\n<pee>Patient-Reported Outcomes (PRO) are data reported directly by patients, without interpretation by a clinician \u2014 quality of life scores, pain levels, symptom intensity, satisfaction, treatment adherence. In mental health, they are particularly valuable because many key symptoms (mood, anxiety, energy, intrusive thoughts) are only accessible through self-reporting.<\/pee>\n<pee>Traditional paper questionnaires (PHQ-9 for depression, GAD-7 for anxiety, MADRS) remain clinical references. But their point-in-time administration in consultations does not capture temporal variability. That is why EMA methods (see below) are revolutionizing PRO collection in contemporary research.<\/pee>\n<h3>3. Digital behavioral data (Digital Biomarkers)<\/h3>\n<pee>One of the most spectacular innovations in recent years is the ability to collect <strong>digital biomarkers<\/strong> \u2014 objective measures of behavior and physiology captured continuously by digital devices. These data include heart rate and its variability (via smartwatches), patterns of physical activity and sedentary behavior (accelerometers), sleep quality and duration (actigraphs), geographic movement patterns (GPS), frequency of phone calls and messages, typing patterns (typing dynamics), and voice data (prosody, fluency, pauses).<\/pee>\n<pee>These passive digital biomarkers \u2014 collected without the patient having to &#8220;do anything&#8221; \u2014 are particularly valuable in mental health research. Studies have shown that changes in sleep, activity, and communication patterns can precede documented depressive or manic episodes by several days \u2014 opening new perspectives for relapse prevention.<\/pee>\n<h3>4. Data from digital cognitive tests<\/h3>\n<pee>Cognitive tests administered via mobile applications represent a revolution for research in cognitive neuroscience and psychiatry. Unlike annual neuropsychological assessments conducted in clinics, short digital tests can be administered daily or weekly \u2014 capturing the temporal variability of cognitive performance.<\/pee>\n<pee>Tests like the Trail Making Test, the Stroop, N-back working memory tests, or reaction time tests can be administered in 2 to 5 minutes on a smartphone. The collected data allow for the detection of subtle changes in cognitive performance that precede clinical manifestations \u2014 a promising application for early detection of Alzheimer&#8217;s disease, monitoring the aftermath of Stroke, or tracking treatment effectiveness.<\/pee>\n<pee>DYNSEO cognitive tests \u2014 <a href=\"https:\/\/www.dynseo.com\/en\/memory-test\/\" target=\"_blank\"><strong>Memory Test<\/strong><\/a>, <a href=\"https:\/\/www.dynseo.com\/en\/concentration-and-attention-test\/\" target=\"_blank\"><strong>Concentration and Attention Test<\/strong><\/a>, <a href=\"https:\/\/www.dynseo.com\/en\/executive-function-testing\/\" target=\"_blank\"><strong>Executive Functions Test<\/strong><\/a> \u2014 are examples of digital tools enabling regular and accessible assessment of cognitive functions outside the clinical context. These data, collected repeatedly, constitute a dynamic profile of cognitive evolution \u2014 valuable for both clinical follow-up and research.<\/pee>\n<h2>EMA (Ecological Momentary Assessment): the revolution of real-time capture<\/h2>\n<pee>Ecological Momentary Assessment (EMA) \u2014 also called experience sampling method \u2014 is a data collection method that involves asking participants about their state (mood, symptoms, behaviors, context) at multiple and varied moments in their daily lives, via a smartphone or a dedicated application.<\/pee>\n<h3>Why EMA changes everything for mental health research<\/h3>\n<pee>The fundamental problem of traditional clinical assessment is that it is retrospective and point-in-time. When a patient fills out a weekly depression questionnaire, they try to &#8220;average&#8221; their week \u2014 which generates considerable biases (recall bias, moment of assessment effect, anchoring bias). EMA solves this problem by capturing the person&#8217;s real state at the very moment they respond.<\/pee>\n<pee>In practice, EMA sends notifications several times a day (generally 3 to 8 times) at random or semi-random moments. The person responds to 5-15 short questions about their emotional state, symptoms, social context, and behaviors. The entire set of responses over several weeks constitutes a dense data curve that reveals patterns, triggers, cycles, and individual variability that point-in-time assessments would never have detected.<\/pee>\n<\/div>\n<div class=\"highlight-box\">\n<h4>\ud83d\udd2c Examples of what EMA can reveal that traditional assessments miss<\/h4>\n<pee><strong>In depression:<\/strong> the times of day when mood is consistently lower, triggering social situations, the relationship between the quality of sleep the previous night and mood the next morning.<!\u2013- [et_pb_br_holder] -\u2013><br \/>\n<strong>In ADHD:<\/strong> the moments of the day when attention is at its peak (allowing for the planning of demanding tasks), the impact of diet and exercise on concentration, triggers of impulsivity.<!\u2013- [et_pb_br_holder] -\u2013><br \/>\n<strong>In early Alzheimer&#8217;s:<\/strong> the first fluctuations in cognitive abilities, environmental factors that improve or deteriorate performance, the progression of difficulties over the weeks.<\/pee>\n<\/div>\n<h3>The challenges of EMA<\/h3>\n<pee>EMA is not without limitations. The <strong>burden<\/strong> on the participant is real \u2014 responding to notifications several times a day for weeks generates fatigue and can affect compliance. Dropout rates in EMA studies are high if the burden is not well calibrated. Selection biases (participants who complete are different from those who drop out) can affect external validity. And the confidentiality of very granular data (behaviors, locations, emotional states) raises significant ethical questions.<\/pee>\n<h2>Connected objects and wearables: passive sensors of real life<\/h2>\n<h3>Actigraphs and smartwatches<\/h3>\n<pee>Actigraphs (advanced pedometers) and smartwatches (Apple Watch, Garmin, Fitbit, Withings) continuously collect data on physical activity, sleep (duration, stages, nighttime awakenings), and heart rate. These passive data are particularly valuable in mental health research as they objectify constructs often reported subjectively: &#8220;I sleep poorly,&#8221; &#8220;I am exhausted,&#8221; &#8220;I do nothing anymore.&#8221;<\/pee>\n<pee>Studies have shown that heart rate variability (HRV) measured continuously is a proxy for the functioning of the autonomic nervous system \u2014 and reflects the state of stress, anxiety, and emotional regulation. Apps like Garmin Health or Apple Health generate daily HRV data that can serve as biomarkers in mental health studies.<\/pee>\n<h3>Voice sensors and speech analysis<\/h3>\n<pee>Automatic voice analysis represents one of the most promising frontiers of digital biomarkers in mental health. Vocal characteristics such as speech rate, pauses, pitch, energy, response latency, and intonation patterns change measurably in depression, schizophrenia, dementia, and other mental disorders. Machine learning algorithms trained on thousands of hours of recordings can detect these changes with a precision that favorably compares to standardized clinical assessments.<\/pee>\n<h3>Behavioral analyses via smartphone<\/h3>\n<pee>The smartphone itself is a sensor of daily behavior. The frequency and duration of calls, messaging patterns, geolocation (mobility, frequented places), ambient light (indicator of outdoor outings), and even micro-patterns of screen unlocking constitute dense behavioral data. Studies have shown that these passive data can predict episodes of depression, anxiety, and psychosis with remarkable accuracy \u2014 opening up prospects for early warning systems.<\/pee>\n<h2>Mobile health applications in clinical studies<\/h2>\n<pee>Mobile health applications \u2014 from simple mood tracking apps to validated cognitive stimulation tools \u2014 play a dual role in RWD studies: data collection (via usage logs and exercise results) and therapeutic intervention (whose adherence and effectiveness can be measured in real time).<\/pee>\n<h3>Emotional regulation and symptom tracking applications<\/h3>\n<pee>Apps like Daylio, Moodpath, or Woebot allow users to track their mood, behaviors, and thoughts daily. In a research context, the aggregated and anonymized data from these apps provide a valuable source of RWD to study emotional patterns in large populations.<\/pee>\n<pee>Clinical tools like the <a href=\"https:\/\/www.dynseo.com\/en\/our-tools\/emotion-thermometer\/\" target=\"_blank\"><strong>DYNSEO Emotion Thermometer<\/strong><\/a>, the <a href=\"https:\/\/www.dynseo.com\/nos-outils\/boite-a-outils-regulation\/\" target=\"_blank\"><strong>Emotional Regulation Toolkit<\/strong><\/a>, and the <a href=\"https:\/\/www.dynseo.com\/nos-outils\/strategies-retour-au-calme\/\" target=\"_blank\"><strong>12 Strategies for Calming Down<\/strong><\/a> allow for the collection of data on the actual use of regulation techniques \u2014 which strategy is chosen, in what contexts, with what effectiveness. These ecological usage data significantly enrich our understanding of the effectiveness of mental health interventions.<\/pee>\n<h3>Cognitive stimulation and testing applications<\/h3>\n<pee>Cognitive stimulation applications \u2014 like <a href=\"https:\/\/www.dynseo.com\/en\/brain-games-apps\/clint-brain-games-for-adults\/\" target=\"_blank\"><strong>CLINT<\/strong><\/a> for adults or <a href=\"https:\/\/www.dynseo.com\/en\/brain-games-apps\/scarlett-brain-games-for-seniors\/\" target=\"_blank\"><strong>SCARLETT<\/strong><\/a> for seniors \u2014 generate valuable data on longitudinal cognitive performance. Usage logs (frequency, session duration, exercise results, level reached, dropout) constitute RWD that allow for the study of engagement in cognitive stimulation, its evolution over time, and the factors associated with adherence or dropout.<\/pee>\n<pee>For research on digital interventions in Alzheimer&#8217;s, Parkinson&#8217;s, or after a Stroke, these real usage data provide an ecological dimension that laboratory efficacy studies cannot offer. An application may show excellent results in a controlled clinical trial \u2014 but if patients do not use it in real life, its population impact will be limited. RWD allows for precise study of these adoption and engagement issues.<\/pee>\n<h2>Methods for analyzing real-world data: methodological challenges<\/h2>\n<h3>The confounding bias: the central challenge<\/h3>\n<pee>The main limitation of RWD studies compared to randomized trials is the absence of randomization \u2014 and thus the potential presence of confounding biases. If patients receiving treatment A are systematically different from those receiving treatment B (younger, less ill, with better access to care), the comparison of their outcomes reflects these differences as much as the treatment effect. Several statistical techniques allow for the correction of these biases: propensity score matching, instrumental analyses, case-control studies, and structural causal models (Directed Acyclic Graphs).<\/pee>\n<h3>Time series analysis and longitudinal data<\/h3>\n<pee>EMA and wearable data generate dense time series \u2014 hundreds or thousands of measurement points per participant over weeks or months. Analyzing these data requires specialized statistical methods that capture their temporal structure: mixed models with random effects, vector autoregressive models (VAR) to study relationships between variables over time, network analysis to map dynamic interactions between symptoms.<\/pee>\n<div class=\"method-card teal\">\n<div class=\"method-badge badge-green\">\ud83d\udcca Network analysis in psychiatry<\/div>\n<h4>A methodological revolution for mental health<\/h4>\n<pee>The network approach, developed notably by Borsboom and Cramer, conceptualizes psychiatric disorders not as discrete entities (a &#8220;disease&#8221; causing symptoms) but as networks of interconnected symptoms that self-maintain. In this model, longitudinal RWD allows for the identification of which symptoms are the most &#8220;central&#8221; (most influencing others), which links activate first during a relapse, and which interventions could most effectively deactivate the pathological network. This approach opens up unprecedented personalized therapeutic perspectives.<\/pee>\n<\/div>\n<h3>Artificial intelligence and machine learning<\/h3>\n<pee>The volume and complexity of RWD have made machine learning and artificial intelligence approaches essential. Deep learning algorithms can detect patterns in vocal, behavioral, and physiological data that escape traditional statistical analysis. The <a href=\"https:\/\/www.dynseo.com\/en\/coach-ia-english\/\" target=\"_blank\"><strong>DYNSEO AI Coach<\/strong><\/a> illustrates this direction: an intelligent support system that learns usage patterns to personalize recommendations.<\/pee>\n<h2>The ethical and regulatory framework of RWD in health<\/h2>\n<h3>GDPR, HDS, and health data governance<\/h3>\n<pee>Health data are sensitive personal data, protected by the GDPR (General Data Protection Regulation) and, for hosted health data, by HDS certification (Health Data Host) in France. Any collection of health data in a research context requires the informed consent of participants, the approval of a Committee for the Protection of Persons (CPP), and often authorization from the CNIL (National Commission on Informatics and Liberty).<\/pee>\n<pee>The <strong>French Health Data Hub<\/strong> (GIE that facilitates access to SNDS data and their cross-referencing with other databases) has become the central tool for RWD research in France. Its use is governed by expert committees that assess scientific interest, the proportionality of the requested data, and the guarantees for the protection of individuals.<\/pee>\n<h3>Selection biases in digital data<\/h3>\n<pee>An important ethical and methodological challenge of digital RWD is their potential for representativeness bias. Users of smartwatches, smartphones, and health applications are not representative of the general population \u2014 they are on average younger, wealthier, more educated, and more engaged in their health. Studies relying on this data risk producing valid evidence for these populations but are difficult to generalize to elderly people, disadvantaged individuals, or those with low digital literacy.<\/pee>\n<div class=\"warning-box\">\n<h4>\u26a0\ufe0f The digital divide: a blind spot in RWD<\/h4>\n<pee>The most vulnerable individuals in mental health \u2014 elderly people with dementia, homeless individuals, people in great precariousness \u2014 are often the least represented in digital RWD. Studies that ignore this digital divide risk producing relevant evidence for the more advantaged populations but may exacerbate health inequalities by directing innovations towards populations that may need them the least.<\/pee>\n<\/div>\n<h2>Practical applications for mental and cognitive health research<\/h2>\n<h3>Early detection of dementia<\/h3>\n<pee>One of the most promising applications of RWD in clinical neuroscience is the early detection of cognitive disorders, years before the clinical manifestation of dementia. Research teams have shown that digital biomarkers \u2014 subtle changes in GPS movement patterns, typing speed, performance on short cognitive tests \u2014 can detect changes that precede the first clinical symptoms of Alzheimer&#8217;s disease by 2 to 5 years.<\/pee>\n<pee>Regular monitoring of cognitive performance through tests like the <a href=\"https:\/\/www.dynseo.com\/en\/memory-test\/\" target=\"_blank\"><strong>DYNSEO Memory Test<\/strong><\/a> and the <a href=\"https:\/\/www.dynseo.com\/en\/concentration-and-attention-test\/\" target=\"_blank\"><strong>Concentration Test<\/strong><\/a>, conducted monthly at home on tablet or smartphone, could constitute an ecological longitudinal monitoring protocol for at-risk populations.<\/pee>\n<h3>Monitoring interventions in psychiatry<\/h3>\n<pee>Real-time monitoring of responses to psychiatric treatments is another area where RWD is transforming practice. Instead of waiting for the monthly consultation to know if an antidepressant is starting to take effect or if a patient is relapsing, weekly EMA data allows for continuous therapeutic adjustment. The <a href=\"https:\/\/www.dynseo.com\/nos-outils\/fiche-restructuration-cognitive\/\" target=\"_blank\"><strong>DYNSEO Anxiety Cognitive Restructuring Sheet<\/strong><\/a> and the <a href=\"https:\/\/www.dynseo.com\/nos-outils\/boite-a-outils-regulation\/\" target=\"_blank\"><strong>Emotional Regulation Toolbox<\/strong><\/a> fit into this ecological intervention logic \u2014 providing tools usable in daily life and whose use itself constitutes relevant research data.<\/pee>\n<h3>Effectiveness of digital interventions<\/h3>\n<pee>RWD allows for the assessment of the actual effectiveness of digital interventions \u2014 CBT applications, cognitive stimulation tools, mindfulness programs \u2014 in ecological conditions. Engagement (number of sessions, duration, regularity), performance trajectory (improvement, plateau, decline), and predictive factors of adherence provide valuable data to improve these tools and personalize recommendations.<\/pee>\n<h2>Towards pragmatic and hybrid studies<\/h2>\n<pee>The future of clinical research is likely in <strong>hybrid studies<\/strong> that combine the rigor of randomized trials with the richness of RWD. Pragmatic trials collect data in real care conditions rather than in specialized research centers. Platform studies allow for the simultaneous evaluation of multiple interventions with adaptive adaptation. And &#8220;in silico&#8221; trials \u2014 which use digital twins or computational models powered by RWD \u2014 allow for the simulation of clinical trials before conducting them in real life, reducing costs and timelines.<\/pee>\n<div class=\"conclusion\">\n<h2>Conclusion: RWD, the new frontier of personalized medicine<\/h2>\n<pee>Real-world data transform our ability to understand mental and cognitive disorders in all their dynamic complexity. They allow us to move away from the &#8220;snapshot&#8221; model in consultations to access the &#8220;film&#8221; of the patient&#8217;s daily life. This methodological revolution holds the promise of a more personalized, preventive, and equitable medicine \u2014 provided that ethical challenges (data protection, digital divide, representativeness bias) are fully addressed. DYNSEO contributes to this ecosystem with quality digital tools \u2014 cognitive tests, stimulation applications, emotional regulation tools \u2014 whose usage data can feed into tomorrow&#8217;s research.<\/pee>\n<a href=\"https:\/\/www.dynseo.com\/en\/our-tests\/\" target=\"_blank\" class=\"cta-button\">Discover DYNSEO cognitive tests \u2192<\/a>\n<\/div>\n<h2>FAQ<\/h2>\n<div class=\"faq-item\">\n<h4>What are real-world data (RWD)?<\/h4>\n<pee>Health data collected outside of controlled clinical trials \u2014 medical records, reimbursements, mobile applications, sensors, registries. They capture the real life of patients outside the clinical context.<\/pee><\/div>\n<div class=\"faq-item\">\n<h4>Difference between RWD and RWE?<\/h4>\n<pee>RWD = raw data. RWE = scientific evidence generated by the rigorous analysis of RWD. The distinction is crucial for regulatory authorities (EMA, FDA).<\/pee><\/div>\n<div class=\"faq-item\">\n<h4>What is EMA and why is it valuable in mental health?<\/h4>\n<pee>Ecological Momentary Assessment: questionnaires sent multiple times a day via smartphone to capture the actual state in real time. Reveals the variability of symptoms that is invisible in point-in-time assessments during consultations.<\/pee><\/div>\n<div class=\"faq-item\">\n<h4>What ethical challenges do RWD pose?<\/h4>\n<pee>Data protection (GDPR, HDS), informed consent, risks of re-identification, digital divide, representativeness bias, ownership and governance of health data.<\/pee><\/div>\n<div class=\"faq-item\">\n<h4>Can mobile applications be used in clinical studies?<\/h4>\n<pee>Yes \u2014 for EMA, repeated cognitive tests, behavioral and emotional tracking. They require rigorous validation as measurement instruments and a strict ethical framework.<\/pee><\/div>\n<\/div>\n<footer class=\"article-footer\">\n<h3>DYNSEO Resources \u2014 Mental Health &amp; Research<\/h3>\n<div class=\"footer-links\">\n<a href=\"https:\/\/www.dynseo.com\/en\/our-tests\/\" target=\"_blank\">Cognitive tests<\/a><br \/>\n<a href=\"https:\/\/www.dynseo.com\/en\/brain-games-apps\/clint-brain-games-for-adults\/\" target=\"_blank\">CLINT Application<\/a><br \/>\n<a href=\"https:\/\/www.dynseo.com\/en\/our-tools\/\" target=\"_blank\">All tools<\/a><br \/>\n<a href=\"https:\/\/www.dynseo.com\/en\/our-training-courses\/\" target=\"_blank\">Training<\/a>\n<\/div>\n<\/footer>\n<\/article>\n<\/div>\n<p>[\/et_pb_code][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":412655,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"[et_pb_section fb_built=\"1\" admin_label=\"Article HTML\" _builder_version=\"4.16\" custom_padding=\"0px||0px||false|false\" global_colors_info=\"{}\"][et_pb_row admin_label=\"Contenu\" _builder_version=\"4.16\" width=\"100%\" max_width=\"100%\" custom_padding=\"0px||0px||false|false\" global_colors_info=\"{}\"][et_pb_column type=\"4_4\" _builder_version=\"4.16\" global_colors_info=\"{}\"][et_pb_code admin_label=\"HTML import\u00e9\" _builder_version=\"4.16\" global_colors_info=\"{}\"]<style type=\"text\/css\">\n@import url('https:\/\/fonts.googleapis.com\/css2?family=Montserrat:wght@600;700;800&family=Poppins:wght@400;500;600&display=swap');\n        * { margin: 0; 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color: #5e5ed7; margin-bottom: 13px; font-size: 1.08rem; }\n.dbi-art-dffa9a .faq-item p { margin: 0; color: #555; }\n.dbi-art-dffa9a a { color: #5e5ed7; }\n@media (max-width: 768px) {\n.dbi-art-dffa9a .article-header h1 { font-size: 1.85rem; }\n.dbi-art-dffa9a .stats-grid { grid-template-columns: 1fr; }\n.dbi-art-dffa9a .article-header { padding: 45px 15px; }\n.dbi-art-dffa9a .container { padding: 15px; }\n.dbi-art-dffa9a h2 { font-size: 1.6rem; }\n}\n\n<\/style>\n<div class=\"dbi-art-dffa9a\">\n<article>\n<header class=\"article-header\">\n<div class=\"article-category\">\ud83d\udd2c Mental health &amp; Clinical research \u2014 Real-world data<\/div>\n<h1>What real-world data can be collected during a clinical study?<\/h1>\n<p class=\"subtitle\">Traditional clinical trials capture only a fraction of patients' reality. Real-world data \u2014 collected via mobile applications, connected devices, EMA, and registries \u2014 revolutionize our understanding of cognitive and mental disorders. A comprehensive guide for researchers, clinicians, and patients.<\/p>\n<\/header>\n\n<div class=\"container\">\n\n<div class=\"intro-paragraph\">\nFor decades, clinical research has operated under a proven model: a randomized controlled trial, a carefully selected population, standardized measures at fixed intervals in a clinical setting. This model remains the gold standard for establishing causality \u2014 but it has a major limitation: it does not capture real life. Mental health, in particular, is deeply affected by daily contexts \u2014 work stress, sleep quality, social interactions, weather \u2014 that point-in-time assessments in consultations cannot grasp. Real-world data (RWD) fill this gap by transforming patients' daily lives into a laboratory.\n<\/div>\n\n<div class=\"stats-grid\">\n<div class=\"stat-card\"><span class=\"stat-number\">\u00d710<\/span><div class=\"stat-label\">the amount of available digital health data doubles every 3-4 years \u2014 a revolution for research<\/div><\/div>\n<div class=\"stat-card\"><span class=\"stat-number\">80%<\/span><div class=\"stat-label\">of clinical studies will integrate RWD by 2030 according to forecasts from EMA and FDA<\/div><\/div>\n<div class=\"stat-card\"><span class=\"stat-number\">EMA<\/span><div class=\"stat-label\">the European Medicines Agency published its strategy on real-world data in 2023<\/div><\/div>\n<\/div>\n\n<h2>Definitions: RWD, RWE and their fundamental differences<\/h2>\n\n<p>Before exploring the types of data and collection methods, it is essential to clarify the vocabulary \u2014 as the terms are often incorrectly used interchangeably.<\/p>\n\n<p><strong>Real-World Data (RWD)<\/strong> \u2014 or real-world data \u2014 are all data related to patients' health status and care delivery collected outside of randomized controlled clinical trials. They can come from electronic health records, reimbursement databases, patient registries, wearable sensors, mobile applications, health social networks, or observational studies.<\/p>\n\n<p><strong>Real-World Evidence (RWE)<\/strong> \u2014 real-world evidence \u2014 are the clinical evidence generated by the rigorous analysis of RWD. RWD are the raw material; RWE are the result of applying scientific methodology to this material. Both the FDA and EMA have developed frameworks to accept RWE in marketing authorization submissions \u2014 a major transformation for the pharmaceutical industry and biomedical research.<\/p>\n\n<h3>Why are RWD crucial in mental and cognitive health?<\/h3>\n\n<p>Mental and cognitive disorders have characteristics that make them particularly difficult to study within the framework of traditional clinical trials. Intra-individual variability is considerable \u2014 a depressed patient may feel very differently from Monday to Friday, or depending on the season, or their relational context. This variability is invisible in a monthly consultation assessment. Similarly, the cognitive manifestations of disorders like ADHD, the aftermath of Stroke, or the early stages of Alzheimer's disease are deeply contextual \u2014 the environment, fatigue, stress modulate them in real time.<\/p>\n\n<p>RWD allow us to capture this dynamic complexity. They make visible what happens between consultations, in the real lives of patients \u2014 which represents 99% of their existence.<\/p>\n\n<h2>The major categories of real-world data in clinical settings<\/h2>\n\n<h3>1. Health system data (administrative and clinical data)<\/h3>\n\n<p>These are the most commonly used RWD in observational research. They include electronic health records (EHR), reimbursement data from Health Insurance (SNDS in France \u2014 National Health Data System), patient registries (cancer registries, rare disease registries, Alzheimer registries), hospital databases (PMSI, drug databases), and prescription data. These databases are valuable for large-scale epidemiological studies \u2014 they allow the analysis of hundreds of thousands or even millions of care pathways. Their limitation is that they only capture what is coded and reimbursed \u2014 they miss subjective, behavioral, and contextual data.<\/p>\n\n<div class=\"method-card blue\">\n<div class=\"method-badge badge-blue\">\ud83c\udfe5 The SNDS in France<\/div>\n<h4>The largest health data repository in Europe<\/h4>\n<p>The National Health Data System (SNDS) covers all care reimbursements for the 67 million insured individuals in France, making it one of the largest health databases in the world. Its access is regulated by the Health Data Hub and requires authorization from the CNIL. For mental health research, it allows the study of care trajectories, treatment adherence, comorbidities, and large-scale hospitalizations \u2014 but does not contain data on symptoms, daily functioning, or quality of life.<\/p>\n<\/div>\n\n<h3>2. Data collected by patients themselves (PRO)<\/h3>\n\n<p>Patient-Reported Outcomes (PRO) are data reported directly by patients, without interpretation by a clinician \u2014 quality of life scores, pain levels, symptom intensity, satisfaction, treatment adherence. In mental health, they are particularly valuable because many key symptoms (mood, anxiety, energy, intrusive thoughts) are only accessible through self-reporting.<\/p>\n\n<p>Traditional paper questionnaires (PHQ-9 for depression, GAD-7 for anxiety, MADRS) remain clinical references. But their point-in-time administration in consultations does not capture temporal variability. That is why EMA methods (see below) are revolutionizing PRO collection in contemporary research.<\/p>\n\n<h3>3. Digital behavioral data (Digital Biomarkers)<\/h3>\n\n<p>One of the most spectacular innovations in recent years is the ability to collect <strong>digital biomarkers<\/strong> \u2014 objective measures of behavior and physiology captured continuously by digital devices. These data include heart rate and its variability (via smartwatches), patterns of physical activity and sedentary behavior (accelerometers), sleep quality and duration (actigraphs), geographic movement patterns (GPS), frequency of phone calls and messages, typing patterns (typing dynamics), and voice data (prosody, fluency, pauses).<\/p>\n\n<p>These passive digital biomarkers \u2014 collected without the patient having to \"do anything\" \u2014 are particularly valuable in mental health research. Studies have shown that changes in sleep, activity, and communication patterns can precede documented depressive or manic episodes by several days \u2014 opening new perspectives for relapse prevention.<\/p>\n\n<h3>4. Data from digital cognitive tests<\/h3>\n\n<p>Cognitive tests administered via mobile applications represent a revolution for research in cognitive neuroscience and psychiatry. Unlike annual neuropsychological assessments conducted in clinics, short digital tests can be administered daily or weekly \u2014 capturing the temporal variability of cognitive performance.<\/p>\n\n<p>Tests like the Trail Making Test, the Stroop, N-back working memory tests, or reaction time tests can be administered in 2 to 5 minutes on a smartphone. The collected data allow for the detection of subtle changes in cognitive performance that precede clinical manifestations \u2014 a promising application for early detection of Alzheimer's disease, monitoring the aftermath of Stroke, or tracking treatment effectiveness.<\/p>\n\n<p>DYNSEO cognitive tests \u2014 <a href=\"https:\/\/www.dynseo.com\/test-memoire\/\" target=\"_blank\"><strong>Memory Test<\/strong><\/a>, <a href=\"https:\/\/www.dynseo.com\/test-concentration-attention\/\" target=\"_blank\"><strong>Concentration and Attention Test<\/strong><\/a>, <a href=\"https:\/\/www.dynseo.com\/test-des-fonctions-executives\/\" target=\"_blank\"><strong>Executive Functions Test<\/strong><\/a> \u2014 are examples of digital tools enabling regular and accessible assessment of cognitive functions outside the clinical context. These data, collected repeatedly, constitute a dynamic profile of cognitive evolution \u2014 valuable for both clinical follow-up and research.<\/p>\n\n<h2>EMA (Ecological Momentary Assessment): the revolution of real-time capture<\/h2>\n\n<p>Ecological Momentary Assessment (EMA) \u2014 also called experience sampling method \u2014 is a data collection method that involves asking participants about their state (mood, symptoms, behaviors, context) at multiple and varied moments in their daily lives, via a smartphone or a dedicated application.<\/p>\n\n<h3>Why EMA changes everything for mental health research<\/h3>\n\n<p>The fundamental problem of traditional clinical assessment is that it is retrospective and point-in-time. When a patient fills out a weekly depression questionnaire, they try to \"average\" their week \u2014 which generates considerable biases (recall bias, moment of assessment effect, anchoring bias). EMA solves this problem by capturing the person's real state at the very moment they respond.<\/p>\n\n<p>In practice, EMA sends notifications several times a day (generally 3 to 8 times) at random or semi-random moments. The person responds to 5-15 short questions about their emotional state, symptoms, social context, and behaviors. The entire set of responses over several weeks constitutes a dense data curve that reveals patterns, triggers, cycles, and individual variability that point-in-time assessments would never have detected.<\/p>\n<\/div>\n<div class=\"highlight-box\">\n<h4>\ud83d\udd2c Examples of what EMA can reveal that traditional assessments miss<\/h4>\n<p><strong>In depression:<\/strong> the times of day when mood is consistently lower, triggering social situations, the relationship between the quality of sleep the previous night and mood the next morning.<br>\n<strong>In ADHD:<\/strong> the moments of the day when attention is at its peak (allowing for the planning of demanding tasks), the impact of diet and exercise on concentration, triggers of impulsivity.<br>\n<strong>In early Alzheimer's:<\/strong> the first fluctuations in cognitive abilities, environmental factors that improve or deteriorate performance, the progression of difficulties over the weeks.<\/p>\n<\/div>\n\n<h3>The challenges of EMA<\/h3>\n\n<p>EMA is not without limitations. The <strong>burden<\/strong> on the participant is real \u2014 responding to notifications several times a day for weeks generates fatigue and can affect compliance. Dropout rates in EMA studies are high if the burden is not well calibrated. Selection biases (participants who complete are different from those who drop out) can affect external validity. And the confidentiality of very granular data (behaviors, locations, emotional states) raises significant ethical questions.<\/p>\n\n<h2>Connected objects and wearables: passive sensors of real life<\/h2>\n\n<h3>Actigraphs and smartwatches<\/h3>\n\n<p>Actigraphs (advanced pedometers) and smartwatches (Apple Watch, Garmin, Fitbit, Withings) continuously collect data on physical activity, sleep (duration, stages, nighttime awakenings), and heart rate. These passive data are particularly valuable in mental health research as they objectify constructs often reported subjectively: \"I sleep poorly,\" \"I am exhausted,\" \"I do nothing anymore.\"<\/p>\n\n<p>Studies have shown that heart rate variability (HRV) measured continuously is a proxy for the functioning of the autonomic nervous system \u2014 and reflects the state of stress, anxiety, and emotional regulation. Apps like Garmin Health or Apple Health generate daily HRV data that can serve as biomarkers in mental health studies.<\/p>\n\n<h3>Voice sensors and speech analysis<\/h3>\n\n<p>Automatic voice analysis represents one of the most promising frontiers of digital biomarkers in mental health. Vocal characteristics such as speech rate, pauses, pitch, energy, response latency, and intonation patterns change measurably in depression, schizophrenia, dementia, and other mental disorders. Machine learning algorithms trained on thousands of hours of recordings can detect these changes with a precision that favorably compares to standardized clinical assessments.<\/p>\n\n<h3>Behavioral analyses via smartphone<\/h3>\n\n<p>The smartphone itself is a sensor of daily behavior. The frequency and duration of calls, messaging patterns, geolocation (mobility, frequented places), ambient light (indicator of outdoor outings), and even micro-patterns of screen unlocking constitute dense behavioral data. Studies have shown that these passive data can predict episodes of depression, anxiety, and psychosis with remarkable accuracy \u2014 opening up prospects for early warning systems.<\/p>\n\n<h2>Mobile health applications in clinical studies<\/h2>\n\n<p>Mobile health applications \u2014 from simple mood tracking apps to validated cognitive stimulation tools \u2014 play a dual role in RWD studies: data collection (via usage logs and exercise results) and therapeutic intervention (whose adherence and effectiveness can be measured in real time).<\/p>\n\n<h3>Emotional regulation and symptom tracking applications<\/h3>\n\n<p>Apps like Daylio, Moodpath, or Woebot allow users to track their mood, behaviors, and thoughts daily. In a research context, the aggregated and anonymized data from these apps provide a valuable source of RWD to study emotional patterns in large populations.<\/p>\n\n<p>Clinical tools like the <a href=\"https:\/\/www.dynseo.com\/nos-outils\/thermometre-des-emotions\/\" target=\"_blank\"><strong>DYNSEO Emotion Thermometer<\/strong><\/a>, the <a href=\"https:\/\/www.dynseo.com\/nos-outils\/boite-a-outils-regulation\/\" target=\"_blank\"><strong>Emotional Regulation Toolkit<\/strong><\/a>, and the <a href=\"https:\/\/www.dynseo.com\/nos-outils\/strategies-retour-au-calme\/\" target=\"_blank\"><strong>12 Strategies for Calming Down<\/strong><\/a> allow for the collection of data on the actual use of regulation techniques \u2014 which strategy is chosen, in what contexts, with what effectiveness. These ecological usage data significantly enrich our understanding of the effectiveness of mental health interventions.<\/p>\n\n<h3>Cognitive stimulation and testing applications<\/h3>\n\n<p>Cognitive stimulation applications \u2014 like <a href=\"https:\/\/www.dynseo.com\/en\/brain-games-apps\/clint-brain-games-for-adults\/\" target=\"_blank\"><strong>CLINT<\/strong><\/a> for adults or <a href=\"https:\/\/www.dynseo.com\/en\/brain-games-apps\/scarlett-brain-games-for-seniors\/\" target=\"_blank\"><strong>SCARLETT<\/strong><\/a> for seniors \u2014 generate valuable data on longitudinal cognitive performance. Usage logs (frequency, session duration, exercise results, level reached, dropout) constitute RWD that allow for the study of engagement in cognitive stimulation, its evolution over time, and the factors associated with adherence or dropout.<\/p>\n\n<p>For research on digital interventions in Alzheimer's, Parkinson's, or after a Stroke, these real usage data provide an ecological dimension that laboratory efficacy studies cannot offer. An application may show excellent results in a controlled clinical trial \u2014 but if patients do not use it in real life, its population impact will be limited. RWD allows for precise study of these adoption and engagement issues.<\/p>\n\n<h2>Methods for analyzing real-world data: methodological challenges<\/h2>\n\n<h3>The confounding bias: the central challenge<\/h3>\n\n<p>The main limitation of RWD studies compared to randomized trials is the absence of randomization \u2014 and thus the potential presence of confounding biases. If patients receiving treatment A are systematically different from those receiving treatment B (younger, less ill, with better access to care), the comparison of their outcomes reflects these differences as much as the treatment effect. Several statistical techniques allow for the correction of these biases: propensity score matching, instrumental analyses, case-control studies, and structural causal models (Directed Acyclic Graphs).<\/p>\n\n<h3>Time series analysis and longitudinal data<\/h3>\n\n<p>EMA and wearable data generate dense time series \u2014 hundreds or thousands of measurement points per participant over weeks or months. Analyzing these data requires specialized statistical methods that capture their temporal structure: mixed models with random effects, vector autoregressive models (VAR) to study relationships between variables over time, network analysis to map dynamic interactions between symptoms.<\/p>\n<div class=\"method-card teal\">\n<div class=\"method-badge badge-green\">\ud83d\udcca Network analysis in psychiatry<\/div>\n<h4>A methodological revolution for mental health<\/h4>\n<p>The network approach, developed notably by Borsboom and Cramer, conceptualizes psychiatric disorders not as discrete entities (a \"disease\" causing symptoms) but as networks of interconnected symptoms that self-maintain. In this model, longitudinal RWD allows for the identification of which symptoms are the most \"central\" (most influencing others), which links activate first during a relapse, and which interventions could most effectively deactivate the pathological network. This approach opens up unprecedented personalized therapeutic perspectives.<\/p>\n<\/div>\n\n<h3>Artificial intelligence and machine learning<\/h3>\n\n<p>The volume and complexity of RWD have made machine learning and artificial intelligence approaches essential. Deep learning algorithms can detect patterns in vocal, behavioral, and physiological data that escape traditional statistical analysis. The <a href=\"https:\/\/www.dynseo.com\/coach-ia\/\" target=\"_blank\"><strong>DYNSEO AI Coach<\/strong><\/a> illustrates this direction: an intelligent support system that learns usage patterns to personalize recommendations.<\/p>\n\n<h2>The ethical and regulatory framework of RWD in health<\/h2>\n\n<h3>GDPR, HDS, and health data governance<\/h3>\n\n<p>Health data are sensitive personal data, protected by the GDPR (General Data Protection Regulation) and, for hosted health data, by HDS certification (Health Data Host) in France. Any collection of health data in a research context requires the informed consent of participants, the approval of a Committee for the Protection of Persons (CPP), and often authorization from the CNIL (National Commission on Informatics and Liberty).<\/p>\n\n<p>The <strong>French Health Data Hub<\/strong> (GIE that facilitates access to SNDS data and their cross-referencing with other databases) has become the central tool for RWD research in France. Its use is governed by expert committees that assess scientific interest, the proportionality of the requested data, and the guarantees for the protection of individuals.<\/p>\n\n<h3>Selection biases in digital data<\/h3>\n\n<p>An important ethical and methodological challenge of digital RWD is their potential for representativeness bias. Users of smartwatches, smartphones, and health applications are not representative of the general population \u2014 they are on average younger, wealthier, more educated, and more engaged in their health. Studies relying on this data risk producing valid evidence for these populations but are difficult to generalize to elderly people, disadvantaged individuals, or those with low digital literacy.<\/p>\n<div class=\"warning-box\">\n<h4>\u26a0\ufe0f The digital divide: a blind spot in RWD<\/h4>\n<p>The most vulnerable individuals in mental health \u2014 elderly people with dementia, homeless individuals, people in great precariousness \u2014 are often the least represented in digital RWD. Studies that ignore this digital divide risk producing relevant evidence for the more advantaged populations but may exacerbate health inequalities by directing innovations towards populations that may need them the least.<\/p>\n<\/div>\n\n<h2>Practical applications for mental and cognitive health research<\/h2>\n\n<h3>Early detection of dementia<\/h3>\n\n<p>One of the most promising applications of RWD in clinical neuroscience is the early detection of cognitive disorders, years before the clinical manifestation of dementia. Research teams have shown that digital biomarkers \u2014 subtle changes in GPS movement patterns, typing speed, performance on short cognitive tests \u2014 can detect changes that precede the first clinical symptoms of Alzheimer's disease by 2 to 5 years.<\/p>\n\n<p>Regular monitoring of cognitive performance through tests like the <a href=\"https:\/\/www.dynseo.com\/test-memoire\/\" target=\"_blank\"><strong>DYNSEO Memory Test<\/strong><\/a> and the <a href=\"https:\/\/www.dynseo.com\/test-concentration-attention\/\" target=\"_blank\"><strong>Concentration Test<\/strong><\/a>, conducted monthly at home on tablet or smartphone, could constitute an ecological longitudinal monitoring protocol for at-risk populations.<\/p>\n\n<h3>Monitoring interventions in psychiatry<\/h3>\n\n<p>Real-time monitoring of responses to psychiatric treatments is another area where RWD is transforming practice. Instead of waiting for the monthly consultation to know if an antidepressant is starting to take effect or if a patient is relapsing, weekly EMA data allows for continuous therapeutic adjustment. The <a href=\"https:\/\/www.dynseo.com\/nos-outils\/fiche-restructuration-cognitive\/\" target=\"_blank\"><strong>DYNSEO Anxiety Cognitive Restructuring Sheet<\/strong><\/a> and the <a href=\"https:\/\/www.dynseo.com\/nos-outils\/boite-a-outils-regulation\/\" target=\"_blank\"><strong>Emotional Regulation Toolbox<\/strong><\/a> fit into this ecological intervention logic \u2014 providing tools usable in daily life and whose use itself constitutes relevant research data.<\/p>\n\n<h3>Effectiveness of digital interventions<\/h3>\n\n<p>RWD allows for the assessment of the actual effectiveness of digital interventions \u2014 CBT applications, cognitive stimulation tools, mindfulness programs \u2014 in ecological conditions. Engagement (number of sessions, duration, regularity), performance trajectory (improvement, plateau, decline), and predictive factors of adherence provide valuable data to improve these tools and personalize recommendations.<\/p>\n\n<h2>Towards pragmatic and hybrid studies<\/h2>\n\n<p>The future of clinical research is likely in <strong>hybrid studies<\/strong> that combine the rigor of randomized trials with the richness of RWD. Pragmatic trials collect data in real care conditions rather than in specialized research centers. Platform studies allow for the simultaneous evaluation of multiple interventions with adaptive adaptation. And \"in silico\" trials \u2014 which use digital twins or computational models powered by RWD \u2014 allow for the simulation of clinical trials before conducting them in real life, reducing costs and timelines.<\/p>\n<div class=\"conclusion\">\n<h2>Conclusion: RWD, the new frontier of personalized medicine<\/h2>\n<p>Real-world data transform our ability to understand mental and cognitive disorders in all their dynamic complexity. They allow us to move away from the \"snapshot\" model in consultations to access the \"film\" of the patient's daily life. This methodological revolution holds the promise of a more personalized, preventive, and equitable medicine \u2014 provided that ethical challenges (data protection, digital divide, representativeness bias) are fully addressed. DYNSEO contributes to this ecosystem with quality digital tools \u2014 cognitive tests, stimulation applications, emotional regulation tools \u2014 whose usage data can feed into tomorrow's research.<\/p>\n<a href=\"https:\/\/www.dynseo.com\/nos-tests\/\" target=\"_blank\" class=\"cta-button\">Discover DYNSEO cognitive tests \u2192<\/a>\n<\/div>\n\n<h2>FAQ<\/h2>\n<div class=\"faq-item\"><h4>What are real-world data (RWD)?<\/h4><p>Health data collected outside of controlled clinical trials \u2014 medical records, reimbursements, mobile applications, sensors, registries. They capture the real life of patients outside the clinical context.<\/p><\/div>\n<div class=\"faq-item\"><h4>Difference between RWD and RWE?<\/h4><p>RWD = raw data. RWE = scientific evidence generated by the rigorous analysis of RWD. The distinction is crucial for regulatory authorities (EMA, FDA).<\/p><\/div>\n<div class=\"faq-item\"><h4>What is EMA and why is it valuable in mental health?<\/h4><p>Ecological Momentary Assessment: questionnaires sent multiple times a day via smartphone to capture the actual state in real time. Reveals the variability of symptoms that is invisible in point-in-time assessments during consultations.<\/p><\/div>\n<div class=\"faq-item\"><h4>What ethical challenges do RWD pose?<\/h4><p>Data protection (GDPR, HDS), informed consent, risks of re-identification, digital divide, representativeness bias, ownership and governance of health data.<\/p><\/div>\n<div class=\"faq-item\"><h4>Can mobile applications be used in clinical studies?<\/h4><p>Yes \u2014 for EMA, repeated cognitive tests, behavioral and emotional tracking. They require rigorous validation as measurement instruments and a strict ethical framework.<\/p><\/div>\n\n<\/div>\n<footer class=\"article-footer\">\n<h3>DYNSEO Resources \u2014 Mental Health &amp; Research<\/h3>\n<div class=\"footer-links\">\n<a href=\"https:\/\/www.dynseo.com\/nos-tests\/\" target=\"_blank\">Cognitive tests<\/a>\n<a href=\"https:\/\/www.dynseo.com\/en\/brain-games-apps\/clint-brain-games-for-adults\/\" target=\"_blank\">CLINT Application<\/a>\n<a href=\"https:\/\/www.dynseo.com\/nos-outils\/\" target=\"_blank\">All tools<\/a>\n<a href=\"https:\/\/www.dynseo.com\/nos-formations\/\" target=\"_blank\">Training<\/a>\n<\/div>\n<\/footer>\n<\/article>\n<\/div>[\/et_pb_code][\/et_pb_column][\/et_pb_row][\/et_pb_section]","_et_gb_content_width":"","footnotes":""},"categories":[2915],"tags":[],"class_list":["post-617351","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-les-conseils-des-coachs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What real-world data can be collected during a clinical study? 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