The main purpose of RUC1 is the promotion of healthy lifestyles among healthy older people to prevent or delay the onset and worsening of controlled common chronic diseases, such as obesity or hypertension. RUC1 will be based on timely interventions provided by artificial intelligence–based digital coaches through natural language processing techniques, patient-generated longitudinal multidimensional data analyses, structured conversations, consultation, and education. Big data analytics techniques will be exploited to address risk stratification and early detection, based on lifestyle analysis, including pattern recognition for the improvement of public health surveillance [17,18] and for the early detection of mental health impairments or cognitive decline [19] and frailty [20]; data mining for inductive reasoning and exploratory data analysis [21]; and cluster analysis for the identification of high-risk groups among elder citizens [22,23].

Chronic obstructive pulmonary disease has a huge burden worldwide, accounting for 3.3 million deaths and 74.4 million disability-adjusted life-years (DALYs) in 2019 [24]. RUC2 proposes novel integrated care management for patients with chronic obstructive pulmonary disease, aiming at early detection of the appearance of exacerbations, avoidance of transitions to higher complexity stadia, and preservation of functional status [25]. At its core, machine learning methods based on Dynamic Bayesian Networks, suitable for modeling knowledge and handling time series data, could be used to implement systems that predict exacerbations and avoid hospitalizations. These systems will be built on top of advanced wearable monitoring KETs capable of generating time series data for potentially key variables, such as blood pressure, pulse oximetry, electrocardiogram, respiration rate, skin temperature, or locomotor activity.

Diabetes mellitus (DM) is the leading cause of blindness, end-stage renal failure, nontraumatic limb amputations, and cardiovascular morbidity and mortality and has an immense burden worldwide [26]. RUC3 aims to establish patterns that detect related and preventable factors of alterations in metabolic control for citizens with type 2 DM. Measures related to lifestyle and metabolic and medication control will be considered to enable tertiary prevention (decrease in comorbidity, exacerbations, and decompensations). In this sense, and considering that short-term prediction of glycemic dynamics is essential to improve DM self-management, a personalized, adaptive, real-time data-driven computational solution based on data federation in the health care space, identifying the different modes of the underlying glucose metabolism will be provided, aiming to prevent hypoglycemic events. A set of monitoring devices will collect clinical data at home such as, bio- and physiological signals (ie, blood glucose concentration data or continuous glucose monitoring data, galvanic skin response, and heart rate variability) combining them with adaptive machine learning regression models to trigger promptly alarms and signs, allowing for personalized, precise, and timely interventions. Additionally, a novel prognostic index for disease control in people with type 2 DM will be built upon data gathered both from blood samples and from smartwatch-generated data linked to lifestyles (number of steps, calorie consumption, heart rate, and sleep).

Parkinson disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system [27]. It is a common condition, with approximately 6.1 million people affected worldwide in 2016 [28]. RUC4 will target patients with PD with early motor complications, commonly motor complications related to PD treatment, such as unpredictable motor fluctuations and dyskinesia. The main objective of RUC4 will be to the improve quality and efficiency of clinical care for patients with advanced PD by using continuous monitoring of motor and nonmotor complications to identify when the disease has progressed to a point requiring a change in medical therapy. GATEKEEPER KETs, such as wearable sensors to continuously or periodically measure motor symptoms (depending on disease severity) and digital applications, which can be used to detect nonmotor symptoms could be used to record data on the patient’s electronic health records, accessible in the GATEKEEPER Health care Space, and feed machine learning models alerting clinicians when the patient’s current treatment plan is not optimal anymore and will derive suggestions on how to improve it.

Heart failure (HF) is a common disease, being the second leading cause of global DALYs loss in 2019 [29]. RUC5 proposes novel integrated care management for patients with HF, aiming at early detection of the appearance of decompensations, avoidance of transitions to higher complexity strata, and preservation of functional status. Telemonitoring services and machine learning with Dynamic Bayes Networks will be harnessed to implement an advanced model for predicting HF decompensations, considering comorbidities. Building on the experience of the Multisensor Monitoring in Congestive Heart Failure trial [30], GATEKEEPER Health care Space apps will allow to explore which other longitudinal data measured by GATEKEEPER Consumer Space “things” (ie, bio-impedance, heart rate, respiratory rate and volume, or physical activity duration and intensity) can be used for predicting decompensations.

Stroke is a common disease, being the third leading cause of global DALYs loss in 2019 [29]. RUC6 will target patients who had a cerebrovascular accident, and their carers, with the aim of delivering empowerment programs focused on the early detection of symptoms, improving lifestyle, and adherence to pharmacological and nonpharmacological treatments. Early detection will be tackled from 2 different fronts. On one hand, patients and carers will be trained in the early detection of stroke symptoms (using coaching apps or simulation of real cases with web-based and augmented reality), and thus be able to react faster during an episode of reinfarction. In contrast, a set of monitoring tools providing measures, such as physical activity and adherence to treatment will try to establish the risks associated with a new stroke. These monitoring tools will be wrapped up in a patient’s app that can also screen for cognitive, behavioral, and mood status, and provide web-based coaching features fostering the promotion of self-management (ie, the WeRISE App [31]).

RUC7 targets chronic older patients with variable complexity according to GATEKEEPER’s risk stratification pyramid. The EIP on AHA blueprint “personas” can provide good examples of potential participant profiles [32]. RUC7 proposes the implementation of novel chronic care models (CCM) for multimorbid subjects, or the enhancement of already existing CCM, using the possibilities of the different GATEKEEPER Spaces. Several sensing technologies, available in the GATEKEEPER Things Catalogue, can be leveraged and integrated into an unobtrusive mobile data collection platform (ie, based on smartphones and smart trackers), able to monitor the multiple parameters required in CCM for multimorbid subjects. Through the GATEKEEPER Health care Space, data could be shared with clinical professionals in charge of managing the CCMs, to adjust individual care plans accordingly. Through the GATEKEEPER Ecosystem Transaction Space, robotics KETs (from very simple pill dispensers to more complex social robots [33]) could be integrated with digital coaching systems to assist polymedicated patients (ie, in particular for patients who are concurrently affected by cognitive impairments).

Hypertension or high blood pressure (HBP), is a serious medical condition with an estimated worldwide prevalence of 31% of adults (1.4 billion) that significantly increases the risk of heart, brain, kidney, and other diseases [34]. RUC8 proposes novel integrated care management for patients with HBP, aiming for the monitoring of blood pressure and early detection of health complications (ie, heart problems and stroke). Telemonitoring services of different intensities will be harnessed to implement periodic or continuous monitoring. The comprehensive analysis of the generated data flow, channeled through the GATEKEEPER systems, will ultimately provide predictions anticipating the appearance of decompensations and triggering alerts and alarms. The intensity of telemonitoring will range from high-intensity monitoring devices in the GATEKEEPER Consumer Space “things” such as wrist monitors, including measurements of other vitals that are important for the close follow-up of patients with HBP, to low-intensity health promotion apps using Optical Character Recognition technology to capture blood pressure data with family-based management features.

As of January 2023, more than 650 million COVID-19 cases and 6.7 million deaths have been confirmed globally [35]. The onset of the COVID-19 pandemic has triggered a need for integrated management tools capable of working on a remote basis, thus limiting physical contact among different actors. In this scenario, the different GATEKEEPER Spaces can provide the technological basis to cover the necessities of patients with COVID-19, citizens in quarantine or lockdown, and health and social organizations dealing with the pandemic. RUC9 will tackle this challenge at 2 different levels. At the health care level, several sensing technologies, available in the GATEKEEPER Things Catalogue, can be leveraged and integrated into an unobtrusive mobile data collection platform (ie, based on smartphones, smart trackers, etc), able to monitor key parameters in COVID-19 (ie, heart rate, respiratory rate, and blood oxygen saturation). At the social care level, GATEKEEPER can provide technological solutions dealing with the complexities of social isolation, thus supporting the study population’s self-monitoring at individual and community scale, in synergy with the existing community or social area services.