Categorizing digital data collection and intervention tools in health and wellbeing living lab settings: A modified Delphi study

BACKGROUND

Health and Wellbeing Living Labs are a valuable research infrastructure for exploring innovative solutions to tackle complex healthcare challenges and promote overall wellbeing. A knowledge gap exists in categorizing and understanding the types of ICT tools and technical devices employed by Living Labs.

AIM

Define a comprehensive taxonomy that effectively categorizes and organizes the digital data collection and intervention tools employed in Health and Wellbeing Living Lab research studies.

METHODS

A modified consensus-seeking Delphi study was conducted, starting with a pre-study involving a survey and semistructured interviews (N=30) to gather information on existing equipment. The follow-up three Delphi rounds with a panel of living lab experts (R1 N=18, R2 - 3 N=15) from 10 different countries focused on achieving consensus on the category definitions, ease of reading, and included subitems for each category. Due to the controversial results in the 2nd round of qualitative feedback, an online workshop was organized to clarify the contradictory issues.

RESULTS

The resulting taxonomy included 52 subitems, which were divided into three levels as follows: The first level consists of 'devices for data monitoring and collection' and 'technologies for intervention.' At the second level, the 'data monitoring and collection' category is further divided into 'environmental' and 'human' monitoring. The latter includes the following third-level categories: 'biometrics,' 'activity and behavioral monitoring,' 'cognitive ability and mental processes,' 'electrical biosignals and physiological monitoring measures,' '(primary) vital signs,' and 'body size and composition.' At the second level, 'technologies for intervention' consists of 'assistive technology,' 'extended reality - XR (VR & AR),' and 'serious games' categories.

CONCLUSION

A common language and standardized terminology are established to enable effective communication with living labs and their customers. The taxonomy opens a roadmap for further studies to map related devices based on their functionality, features, target populations, and intended outcomes, fostering collaboration and enhancing data capture and exploitation.

Differences in healthcare service utilization in patients with polypharmacy according to their risk level by adjusted morbidity groups: a population-based cross-sectional study

BACKGROUND

Patients with polypharmacy suffer from complex medical conditions involving a large healthcare burden. This study aimed to describe the characteristics and utilization of primary care (PC) and hospital care (HC) and factors associated in chronic patients with polypharmacy, stratifying by adjusted morbidity groups (AMG) risk level, sex and age, and comparing with non-polypharmacy.

METHODS

Cross-sectional study conducted in a Spanish basic healthcare area. Studied patients were those over 18 years with chronic diseases identified by the AMG tool from Madrid electronic clinical record, which was the data source. Sociodemographic, sociofunctional, clinical and healthcare utilization variables were described and compared by risk level, sex, age and having or not polypharmacy. Factors associated with healthcare utilization in polypharmacy patients were determined by a negative binomial regression model.

RESULTS

In the area studied, 61.3% patients had chronic diseases, of which 16.9% had polypharmacy vs. 83.1% without polypharmacy. Patients with polypharmacy (vs. non-polypharmacy) mean age was 82.7 (vs. 52.7), 68.9% (vs. 60.7%) were women, and 22.0% (vs. 1.2%) high risk. Their average number of chronic diseases was 4.8 (vs. 2.2), and 95.6% (vs. 56.9%) had multimorbidity. Their mean number of annual healthcare contacts was 30.3 (vs. 10.5), 25.9 (vs. 8.8) with PC and 4.4 (vs. 1.7) with HC. Factors associated with a greater PC utilization in patients with polypharmacy were elevated complexity, high risk level and dysrhythmia. Variables associated with a higher HC utilization were also increased complexity and high risk, in addition to male sex, being in palliative care, having a primary caregiver, suffering from neoplasia (specifically lymphoma or leukaemia) and arthritis, whereas older age and immobilization were negatively associated.

CONCLUSIONS

Polypharmacy population compared to non-polypharmacy was characterized by a more advanced age, predominance of women, high-risk, complexity, numerous comorbidities, dependency and remarkable healthcare utilization. These findings could help healthcare policy makers to optimize the distribution of resources and professionals within PC and HC systems, aiming for the improvement of polypharmacy management and rational use of medicines while reducing costs attributed to healthcare utilization by these patients.

Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm.

Innovation through the Quintuple Helix in living labs: lessons learned for a transformation from lab to ecosystem

Introduction

In the process of growing societies, and especially in the digital era we live in, there is a need for a strong push for innovation that puts citizens at the center of the process from the beginning to build more resilient, cooperative and flexible communities. Different collaborative design approaches have emerged in recent decades, one of the most interesting being Living Labs, which involves user-centered design and co-creative innovation that bring together different actors and roles. However, although these new methodologies are harnessing creativity, some aspects of this new, more ecosystemic and complex vision are not clearly understood: possible barriers, how to facilitate local and operational solutions, overcoming institutional blockage, integrating new roles, etc.

Methods

The incorporation of the Quintuple Helix as a driver to ensure greater coordinated participation of local actors has proven its usefulness and impact during the re-adaptation of LifeSpace (previously named Smart House Living Lab), managed by the Polytechnic University of Madrid (Spain), a transformation based on the experiences and lessons learned during the large-scale ACTIVAGE pilot funded by the European Commission, more specifically at the Madrid Deployment Site. It involved more than 350 older adult people and other stakeholders from different areas, including family members, formal and informal caregivers, hospital service managers, third-age associations, and public service providers, forming a sense of community, which was called MAHA.

Results

The living lab infrastructure evolved from a single multi-purpose environment to incorporate three harmoniously competing environments: (1) THE LAB: Headquarters for planning, demonstration, initial design phases and entry point for newcomers to the process, (2) THE CLUB: Controlled interaction environment where returning users validate solutions, focusing mainly on AHA services (MAHA CLUB), such as exergames, social interaction applications, brain training activities, etc. (3) THE NEIGHBOURHOOD: Real-life environments for free and open interaction between actors and implementation of previously validated and tested solutions.

Conclusion

The Quintuple Helix model applied in LifeSpace's new vision allows a coordinated involvement of a more diverse set of actors, beyond the end-users and especially those who are not traditionally part of research and innovation processes.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

Evaluating IoT-Based Services to Support Patient Empowerment in Digital Home Hospitalization Services

Hospitals need to optimize patient care, as, among other factors, life expectancy has increased due to improvements in sanitation, nutrition, and medicines. Hospitalization-at-home (HaH) could increase admission efficiency, moderate costs, and reduce the demand for beds. This study aimed to provide data on the feasibility, acceptability, and effectiveness of the integration of IoT-based technology to support the remote monitoring and follow-up of patients admitted to HaH units, as well as the acceptability of IoT-based solutions in healthcare processes. The need for a reduction in the number of admission days, the percentage of admissions after discharge, and the actions of the emergency services during admission were the most relevant findings of this study. Furthermore, in terms of patient safety and trust perception, 98% of patients preferred this type of digitally-supported hospitalization model and up to 95% were very satisfied. On the professional side, the results showed a reduction in work overload and an increase in trust when the system was adopted.

Digital Biomarkers for Supporting Transitional Care Decisions: Protocol for a Transnational Feasibility Study

Background

Virtual Health and Wellbeing Living Lab Infrastructure is a Horizon 2020 project that aims to harmonize Living Lab procedures and facilitate access to European health and well-being research infrastructures. In this context, this study presents a joint research activity that will be conducted within Virtual Health and Wellbeing Living Lab Infrastructure in the transitional care domain to test and validate the harmonized Living Lab procedures and infrastructures. The collection of data from various sources (information and communications technology and clinical and patient-reported outcome measures) demonstrated the capacity to assess risk and support decisions during care transitions, but there is no harmonized way of combining this information.

Objective

This study primarily aims to evaluate the feasibility and benefit of collecting multichannel data across Living Labs on the topic of transitional care and to harmonize data processes and collection. In addition, the authors aim to investigate the collection and use of digital biomarkers and explore initial patterns in the data that demonstrate the potential to predict transition outcomes, such as readmissions and adverse events.

Methods

The current research protocol presents a multicenter, prospective, observational cohort study that will consist of three phases, running consecutively in multiple sites: a cocreation phase, a testing and simulation phase, and a transnational pilot phase. The cocreation phase aims to build a common understanding among different sites, investigate the differences in hospitalization discharge management among countries, and the willingness of different stakeholders to use technological solutions in the transitional care process. The testing and simulation phase aims to explore ways of integrating observation of a patient’s clinical condition, patient involvement, and discharge education in transitional care. The objective of the simulation phase is to evaluate the feasibility and the barriers faced by health care professionals in assessing transition readiness.

Results

The cocreation phase will be completed by April 2022. The testing and simulation phase will begin in September 2022 and will partially overlap with the deployment of the transnational pilot phase that will start in the same month. The data collection of the transnational pilots will be finalized by the end of June 2023. Data processing is expected to be completed by March 2024. The results will consist of guidelines and implementation pathways for large-scale studies and an analysis for identifying initial patterns in the acquired data.

Conclusions

The knowledge acquired through this research will lead to harmonized procedures and data collection for Living Labs that support transitions in care.

International Registered Report Identifier (IRRID)

PRR1-10.2196/34573

Co-designing a remote rehabilitation tool for Parkinson's disease: exploratory values and challenges

BACKGROUND

Impaired motor function is one of the early symptoms shown in patients with Parkinson Disease (PD). For this reason, rehabilitative interventions have been used for many years to improve motor and non-motor symptoms. Among them, the use of music therapy has shown benefits in helping to overcome some of the most common motor dysfunction. Addressing the challenge of providing access to this type of therapy, this document presents the collaborative design process to develop a remote training support tool for PD based on music therapy.

METHODS

A qualitative study with creative co-design methods was used in which different groups of healthcare professionals, patients, and relatives participated in six iterative sessions. Workshops were designed and structured to incrementally discover requirements and needs and validate the proposed prototype ideas.

RESULTS

The study provided key aspects that were used for the development and validation of the proposed prototypes for the remote music-based training support tool for PD. Up to 20 factors that had a positive and/or negative influence on patient access to training were detected. These factors were classified into three common themes: daily activities and independence, participation in treatment and barriers to daily treatment, and self-management and personalization of information and telecommunication technologies (ICT).

CONCLUSIONS

This paper shows the results of a collaborative design process aimed at identifying the different factors, relevant to patients with PD, to improve their access to remote ICT-based training therapy and their expectations regarding alternative therapies, such as music. The participatory design methods and the iterative model used helped overcome many of the traditionally barriers that this type of technological support solutions usually have, facilitating the future participation.

Developing modular training components to support home hospital digital solutions: Results of a Delphi panel

BACKGROUND

Home hospitalization (HH) has demonstrated to be a cost-effective alternative with respect ti traditional hospitalization. Digital technologies, such as remote monitoring, have the potential to contribute to its expansion. Tailored educational content is a need to ensure patient safety during the whole admission.

PURPOSE

The objective of this study was to systematically obtain consensus on patients with HH using training in the digital monitoring system. The goal of this work was to develop an adaptable modular and personalized training program for patients to support quality and safety care for HH.

METHODS

The methodological approach for developing the proposed training content followed a modified Delphi technique with a multidisciplinary group of experts with significant knowledge of health informatics and HH protocols in Spain. The study comprised two rounds of training material description and gathering were completed. In Round 1, the experts received 58 predefined items obtained from the literature review and protocol selection. 20 items were rejected for different reasons and 25 new items were proposed. In Round 2, the experts selected the final items to build on the training content for every type of user and illness.

RESULTS

A total of 21 experts completed rounds 1 and 2. The consensus was reached at the end of Round 2 with the inclusion of 53 items to build the training material. This included 17 treatment procedures, 4 diagnosis procedures, 22 additional support content, and 10 content features that describe how to build and deliver customized training content.

CONCLUSIONS

Participants agreed on the type of content, its structure, and delivery methods to build modular training materials that support patients when they are hospitalized at home with the help of digital monitoring tools. This information can be used to create HH training programs that support new HH protocols and provide a standard for evaluating the quality of existing educational materials and programs.

Definition and development of a digital system for the empowerment and activation of type 1 diabetic patient

Health education is essential for type 1 diabetic patients to actively participate in the decision-making process about their disease. Under the framework of the INCAP project, a mobile application has been designed and developed with an easy-to-use interface for type 1 diabetic patients to improve their empowerment, activation and thus their self-control and improvement of their treatment adherence.

Definition of a framework for the creation of a Living Labs network: the case of the European Living Labs and Test Beds Network focused on health care domain

This paper describes the framework for the creation of a Living Labs network based on the experience of the setting up, growth and further consolidation of the European Living Labs and Test Beds Network focused on Health. The manuscript presents how to create an open innovation ecosystem through a network of Living Labs and Test Beds, introducing its value proposition and current status.