EP PerMed presents a new “Advance and Learning in Personalised Medicine (APM)” developed at the Aragon Health Sciences Institute (IACS).
What is this APM about?
For many people living with severe hearing loss and cochlear implants, vertigo crises are among the most disabling and unpredictable aspects of their condition. Unlike hearing impairment, which is relatively stable and manageable with the implant, vertigo episodes occur without warning, forcing individuals to stop working, interrupt daily activities, and withdraw from social life. This unpredictability becomes a source of chronic anxiety and a barrier to full participation in everyday activities.
This project addresses a central question: whether identifiable patterns in movement, hormonal fluctuations, sleep, or stress precede or trigger these crises, and whether recognising such patterns can help individuals regain a degree of control over their condition.
The study follows participants with cochlear implants over a 12‑month period, combining passively collected data (daily step cadence recorded automatically via a smartphone application) with patient‑reported information on vertigo episodes, tinnitus, sleep, perceived stress, and, in women, hormonal changes related to the menstrual cycle or menopause. The objective is to determine whether specific levels or patterns of physical activity are associated with reduced frequency or severity of crises, and how hormonal and lifestyle factors modulate this relationship.
Participation requires no clinical intervention and does not involve changes to existing treatment. After providing informed consent, participants install two mobile applications (MyCap and Stepora). Data are automatically synchronised with a secure research platform (REDCap), requiring minimal effort beyond completing occasional short questionnaires. App‑based reminders support sustained engagement.
The ultimate aim is not to generate population averages but to produce individualised, longitudinal knowledge that enables each participant to understand their own trigger profile. This represents an application of precision medicine not to treatment, but to lived experience, supporting anticipation rather than passive endurance of symptoms.
This project applies personalised medicine to lived experience by combining passive activity data captured via smartphones with patient reported symptom logging to identify each individual’s vertigo and tinnitus thresholds. This approach captures self-limited vertigo episodes that are typically not recorded in healthcare information systems. The objective is to develop a predictive tool that translates individual patterns into practical, personalised activity guidance, enabling patients to anticipate crises rather than simply react to them.
Raquel Ibáñez Pérez, Principal Investigator; Betsabe Melcon Friera, Co Investigator; Raúl López Blasco, SC.
What impact does your APM will have for the citizens/patients, for the advancements of PM or for the advancements of the health care system or for innovation?
Vertigo crises in people with cochlear implants are not merely a medical inconvenience; they are life‑interrupting events that occur without warning, forcing individuals to stop working, withdraw from social activities, and live under a constant sense of uncertainty. Current clinical management is largely reactive: treatment begins after a crisis has occurred, rather than before. This project proposes a shift towards a more anticipatory approach.
For patients: from reaction to anticipation
The immediate impact of this project is the generation of individualised, longitudinal data that map each person’s symptom profile over time. Rather than focusing on how to treat a crisis, the approach shifts to identifying what precedes it.
For women, this includes hormonal fluctuations, which are rarely integrated into vestibular management despite their documented influence on inner ear function. For all participants, it includes daily physical activity patterns captured passively, without the need for wearable devices or clinical visits, using only a smartphone.
In a future implementation phase, and based on the evidence generated, these patterns will be translated into actionable, personalised guidance. This moves beyond generic recommendations (e.g. “exercise moderately”) towards individual thresholds (e.g. “symptom burden increases when activity cadence exceeds a specific level”). This represents an application of precision medicine to behaviour rather than to pharmacological intervention.
For the advancement of Personalised Medicine (PM)
This project shows that personalised medicine can be advanced without relying exclusively on genomic data or hospital‑based infrastructure. A smartphone accelerometer, a structured symptom diary, and a 12‑month longitudinal design are sufficient to generate clinically meaningful individual‑level patterns, provided that the analytical approach is idiographic rather than group‑based.
The integration of hormonal variables as effect modifiers addresses a well‑recognised gap in personalised medicine: the limited incorporation of biological sex and hormonal status as dynamic factors in chronic disease management. Demonstrating that symptom burden in vestibular disorders may follow individual hormonal rhythms provides a model that is transferable beyond cochlear implant populations.
For the health system
Although not the primary objective, the study has clear implications for system efficiency. Patients who can anticipate the conditions under which crises are more likely to occur are better positioned to take preventive action. This may lead to fewer emergency visits, fewer reactive medication adjustments, and a shift towards secondary prevention.
Describe how you collaborate with academia, private partners and/or health care providers to ensure the implementation of this APM in clinical practise or in the health care system.
The implementation of this APM is based on a collaboration model that connects research infrastructure, clinical expertise, and primary care, recognising that sustainable integration into the health system requires coordination across all three levels.
The Aragon Health Sciences Institute (IACS) provides the methodological and data governance backbone of the project. It contributes expertise in health research design, access to the secure BIGAN data platform, and REDCap infrastructure for longitudinal data management. This partnership ensures that data collected through consumer-facing applications (MyCap, Stepora) are managed within a validated research environment compliant with the General Data Protection Regulation (GDPR) and aligned from the outset with the European Health Data Space (EHDS) as a higher-level data governance framework, which is essential for any future transition from research to clinical implementation.
Planned clinical specialist partnership
Integration with Ear, Nose and Throat (ENT) specialists, particularly those involved in cochlear implant programmes and the management of severe hearing loss, is central to the clinical credibility and feasibility of recruitment for this project. ENT teams are the primary point of contact for the target population and are best positioned to identify eligible participants, contextualise symptom data within each patient’s clinical history, and translate research findings into actionable clinical guidance. Formal collaboration agreements with hospital ENT services are planned for the implementation phase, with the aim of integrating personalised activity monitoring into standard audiological follow-up.
Planned primary care partnership
For long-term implementation, general practitioners (GPs) represent the most scalable entry point into the health system. Vertigo and hearing loss are conditions predominantly managed in primary care once the acute phase has resolved. GPs can therefore act both as contributors of contextual clinical data and as the main channel through which personalised activity-based guidance would be communicated to patients. Their involvement is planned for a subsequent phase, once sufficient evidence has been generated to support the development of clinical recommendations.
Towards system integration
The collaboration architecture is designed to follow the progression of the evidence: starting with research infrastructure (IACS), moving through specialist clinical validation (ENT), and ultimately reaching the primary care level, where population-scale impact becomes feasible. This staged approach reflects a realistic implementation pathway rather than a simultaneous multi-partner rollout and ensures that each partnership is activated when the supporting evidence and tools are sufficiently mature.
Does your APM involve regulatory considerations, patient engagement, ethical/legal/social issues (ELSI), health economics, and/or health reimbursement systems?
Regulatory and ethical framework
This study will be submitted for evaluation and approval by the Ethics Committee for Clinical Research of Aragón (CEICA) and will comply with all applicable regulatory and legal requirements. All participants will provide informed consent electronically prior to enrolment. The study does not involve any clinical intervention and poses no direct health risk to participants.
All data will be pseudonymised at source and managed in accordance with EU Regulation 2016/679 (GDPR) and Spanish data protection legislation. Within the BIGAN platform, a pseudonymised cohort will be established, enabling future linkage between patient‑generated and patient‑reported data and each participant’s interactions within the healthcare system. This will facilitate the integration of additional data sources, including laboratory data, diagnostic procedures, and healthcare episodes across primary care, emergency departments, and hospital settings.
Data will be stored and processed within BIGAN, a secure health data governance infrastructure, together with REDCap and the participant‑facing applications (MyCap and Stepora), all of which comply with the required standards for health research data management.
Patient engagement
Participant engagement is embedded within the study design rather than treated as an ancillary component. Participants actively contribute symptom and lifestyle data through structured, app‑based questionnaires, thereby acting as co‑generators of evidence rather than passive subjects.
The longitudinal nature of the study (12 months) supports the development of an ongoing relationship between participants and the research process. App‑based reminders, combined with the low burden of passive data collection (automatic step cadence recording), are intended to sustain engagement without imposing demands comparable to clinical visits.
Ethical, legal and social considerations
The generation of individualised, longitudinal health‑behaviour data in a population living with disability raises considerations that extend beyond standard data protection. In particular, data linking physical activity thresholds to symptom burden could, if inadequately governed, have implications in insurance or occupational contexts.
Pseudonymisation procedures and restricted access to data by the research team constitute the primary safeguards. In a future implementation phase, any translation of individual patterns into personalised guidance will require explicit re‑engagement with participants regarding data ownership, access, and use.
Health economics
Formal health economic analysis is not within the scope of this study. However, the project is designed as an evidence‑generation phase preceding any cost‑effectiveness evaluation. Specifically, it aims to determine whether individualised, activity‑based guidance reduces the frequency of crises before assessing its potential impact on emergency care utilisation and productivity loss.
How are questions of equity, diversity or gender considered in your APM?
The exclusion criteria applied in this study are not based on discriminatory factors but on the functional and methodological requirements inherent to active participation. The study relies on continuous data contribution and on the collection of information related to daily physical activity, which requires a minimum level of interaction, comprehension, and functional autonomy.
In this context, the exclusion of individuals with neurological conditions or severe intellectual disability is justified to ensure valid informed consent and active, informed participation in the research process. Similarly, the exclusion of individuals requiring permanent technical assistance for ambulation reflects a methodological requirement: that basic walking function is preserved, given that ambulatory activity (e.g. step cadence) is the core variable analysed in relation to vestibular symptom burden.
Therefore, the exclusion criteria derive from functional and methodological considerations specific to the study design and do not constitute a general limitation on the participation of persons with disabilities, provided that such participation is compatible with active involvement and with the scientific objectives of the study.
Is your APM part of a national/international initiative? How does the international aspect/dimension contribute to your APM?
This APM is a regional initiative developed in the Autonomous Community of Aragón within the Spanish National Health System. Its scope has been deliberately defined at a regional level to ensure methodological rigour and high standards of data governance during the initial phase. This does not limit its ambition, as the study has been designed from the outset to be replicable at both national and international levels.
The technical infrastructure underpinning the study is designed for deployment across different institutional and national contexts, without dependence on a specific healthcare system. REDCap is a widely established research platform, and the Aragon Health Sciences Institute (IACS) may provide access to its REDCap infrastructure to other European partners, facilitating protocol adoption by EP PerMed institutions lacking equivalent resources and reducing technical barriers to implementation.
Stepora is a mobile application developed within the Biocomputation Unit, specifically designed for the structured collection of activity and symptom data in clinical research settings. Its current technology readiness level (TRL 3) is appropriate for validation in research environments. Both Stepora and MyCap operate on standard consumer smartphones and do not require specialised equipment, supporting the feasibility of broader adoption.
Within the BIGAN platform, the creation of a pseudonymised participant cohort represents a reusable asset in the medium to long term. This cohort could support future federated research initiatives with other regions, enabling retrospective comparative analyses as a preparatory step for prospective multicentre studies. This approach can strengthen hypothesis generation and improve methodological efficiency in future interregional or European projects.
The main element likely to require local adaptation during replication relates to hormonal and lifestyle variables, particularly differences across countries in the diagnosis and management of hormonal conditions. This represents a methodological consideration rather than a barrier, and addressing it within multicentre studies could generate valuable comparative evidence on the interaction between biological factors and organisational characteristics across different health systems.
Do you believe your APM has the potential to be scalable and/or transferable to another field or healthcare system?
The scalability of this APM lies not in its clinical focus but in its methodological framework. The combination of passive sensor data, longitudinal symptom reporting, and idiographic pattern analysis is applicable to any chronic episodic condition in which trigger profiles vary substantially between individuals and where behavioural or hormonal factors are likely to play a role.
Conditions such as migraine, fibromyalgia, epilepsy, and autoimmune disorders characterised by fluctuating symptom burden are clear candidates for this approach. Across these conditions, the central question remains consistent: what factors precede an individual’s episodes, and can identifying these patterns reduce their frequency or impact?