Bull C

Foley T

Hussein R

Smeddinck JD

van Gils M

English

Newcastle University E-Prints

TYPE EditorialPUBLISHED 30 January 2024| DOI 10.3389/fdgth.2024.1367567EDITED AND REVIEWED BYUwe Aickelin,The University of Melbourne, Australia*CORRESPONDENCEJan David Smeddinckjan.smeddinck@dhp.lbg.ac.atRECEIVED 09 January 2024ACCEPTED 15 January 2024PUBLISHED 30 January 2024CITATIONSmeddinck JD, Hussein R, Bull C, Foley T andvan Gils M (2024) Editorial: Supportingsustainable behavior change andempowerment in ubiquitous and learninghealth systems.Front. Digit. Health 6:1367567.doi: 10.3389/fdgth.2024.1367567COPYRIGHT© 2024 Smeddinck, Hussein, Bull, Foley andvan Gils. This is an open-access articledistributed under the terms of the CreativeCommons Attribution License (CC BY). Theuse, distribution or reproduction in otherforums is permitted, provided the originalauthor(s) and the copyright owner(s) arecredited and that the original publication inthis journal is cited, in accordance withaccepted academic practice. No use,distribution or reproduction is permittedwhich does not comply with these terms.Frontiers in Digital HealthEditorial: Supporting sustainablebehavior change andempowerment in ubiquitous andlearning health systemsJan David Smeddinck1,2,3*, Rada Hussein1, Christopher Bull3,Tom Foley4,5 and Mark van Gils61Ludwig Boltzmann Institute for Digital Health and Prevention, Ludwig Boltzmann Gesellschaft,Salzburg, Austria, 2Human-Centered Ubiquitous Media Research Group, LMU Munich, Munich,Germany, 3Open Lab, School of Computing, Newcastle University, Newcastle upon Tyne, UnitedKingdom, 4Population Health Sciences Institute for Foley, Newcastle University, Newcastle upon Tyne,United Kingdom, 5School of Medicine, University College Dublin, Dublin, Ireland, 6Faculty of Medicineand Health Technology, Tampere University, Tampere, FinlandKEYWORDSdigital health, learning health systems, behavior change, health data, health informatics,empowermentEditorial on the Research TopicSupporting sustainable behavior change and empowerment in ubiquitousand learning health systems1 IntroductionThe increasing integration of digital health technologies into everyday life marks atransformative shift in healthcare and prevention. Ubiquitous digital health systems includecontinuous monitoring and intervention capabilities, offering unprecedented opportunitiesfor preventive and rehabilitative healthcare, moving beyond a primary focus on addressinghealth problems after they symptomatically manifest. This shift is not merely characterizedby technological evolution but also by a paradigmatic change towards more personalized,data-driven, and sustainable healthcare approaches. In this editorial, we underscore thepotential of ubiquitous and learning health systems to revolutionize healthcare, includingfostering sustainable behavior change and empowering individuals in their health journeys.Digital health technologies are increasingly present in our day-to-day lives andare often used continuously, enabling the integration of an increasing variety of data.Such ubiquitous and often mobile digital health systems have considerable potential inenabling long-term—or even life-long disease management. Furthermore, accompanyinghealthcare monitoring and interventions with a focus on prevention and rehabilitationcould reduce the amount and extent of events that require traditional medicaltreatment. This can lower individual suffering, as well as the burden on public healthsystems considerably (1, 2) and has the potential to increase quality-adjusted life years (3).2 The promise of learning health systemsIn the context of ubiquitous digital health technologies, learning health systemshave the potential to reshape healthcare by providing continuous, context-aware01 frontiersin.orgSmeddinck et al. 10.3389/fdgth.2024.1367567and reflexively iterated healthcare and prevention systemsproviding benefits at individual and population levels (4).Longer-term, or even life-long health data, including patientgenerated and patient contributed data might deliver theconcept of a digital health twin (5), which promises to act asthe information driving personalized or “precision” health inan increasingly optimized manner. Learning health systems,characterized by their adaptability and iterative improvement,are pivotal in realizing the potential of ubiquitous healthtechnologies given their rapid and continuous development.These systems can leverage data and machine learning as wellas learnings from lived and practical experience to evolve andtailor healthcare interventions at the individual and systemlevel with different levels of abstraction of learning loops (6)and with an overarching aim of enhancing the efficacy andefficiency of health services (7). The Free et al., study onpneumonia management demonstrates the potential of data-driven frameworks in supporting clinical decision support.Ultimately, LHS can be a steppingstone towards expandingquality-adjusted life years by promoting proactive healthmanagement that considers healthcare and prevention deliveryas an inherently iterative process (4) in a complex socio-digital ecosystem [cf. e.g., (8)].3 Challenges, opportunities & the needfor sustainable behavior changeThe pervasive nature of digital health technologies introducesseveral challenges, including data security, privacy, and theequitable distribution of healthcare benefits. These challengesnecessitate careful consideration and responsible implementationto ensure that these technologies empower rather thandisenfranchise individuals (9). Addressing equity in digitalhealthcare, as discussed in the context of Denmark by Eriksenet al., is crucial to ensure that these technologies benefit allsegments of society equally, integrating perspectives on personaland systemic health with a focus on ensuring equitable access,empowerment and emancipation in digital healthcare. Aparticular challenge exists at the individual level in thatsustainable behavior change is central to the vision behindapproaches to healthcare and prevention that are taking a health-journey accompanying or even life-long approach. Long-termadherence to healthy lifestyle changes, facilitated by digital tools,can significantly improve health outcomes. The EPICURE studyby Hayn et al., highlights how digital tools, such as wearables,can support cardiac rehabilitation but also emphasizes the needfor larger, randomized controlled trials to fully understandtheir impact.4 The role of personalization andadaptivity in healthcarePersonalization in healthcare, enabled by data-driven insightsand rapidly developing capabilities in machine learning andFrontiers in Digital Health 02artificial intelligence (10, 11), is becoming a core ambition ofeffective digital health systems. It allows for interventions to betailored to individual needs and preferences, increasing theirrelevance and effectiveness. E.g., the scalable employment ofconsumer wearables can support predictive analytics andpartially replace traditional manual self-reports, as indicated inthe Vuong et al. study on predicting pain ratings in sickle celldisease. Notably, while the technical and procedural buildingblocks exist for fostering sustainable behavior change inindividuals and adaptive improvements in learning healthsystems, both suffer from a lack of structured investigation.Research approaches that can enable efficient knowledgegeneration on the complex interplay of variables that determinepossible improvements (or deterioration) in individual orpopulation health are clearly needed. This encourages futurework that exceeds beyond traditional randomized controlledstudy designs, enabling the expression (12) and systematicstudy of complex system configurations and mechanisms foradaptivity. This will require capable and scalable researchinfrastructures (13), as well as improvements in researchmethods at scale, for which novel artificial intelligence methodscurrently offer an exciting avenue of exploration, particularlydue to newly evolving capabilities in readily integrating highlyvaried and sparse multi-modal data streams, as well asconverting between structured digital (i.e., “computable”) andunstructured natural language (i.e., easily and efficientlydescribable) representations.5 ConclusionThis Research Topic brings together relevant articles aroundsensor-enabled, data-driven, and increasingly ubiquitous digitalhealth. It contributes to the pathway towards individuallyempowering digital health technology, using e.g., predictivemethods that deliver increasingly more precise and preventativeapproaches, with the larger aim of preserving quality-adjusted lifeyears at the individual and societal level.Author contributionsJS: Conceptualization, Project administration, Writing –original draft, Writing – review & editing. RH:Conceptualization, Writing – original draft, Writing – review &editing. CB: Writing – original draft, Writing – review & editing.TF: Writing – original draft, Writing – review & editing.MG: Writing – original draft, Writing – review & editing.AcknowledgmentsWe thank all submitting authors for their contributions tothe Research Topic. We also thank Professor Rik Crutzen forhis valuable inputs in shaping the topic. We further thankFrontiers in Digital Health and the Health Informatics sectionfrontiersin.orgSmeddinck et al. 10.3389/fdgth.2024.1367567for their support and editorial management of theResearch Topic.Conflict of interestThe authors declare that the research was conducted in theabsence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.Frontiers in Digital Health 03Publisher’s noteAll claims expressed in this article are solely those of theauthors and do not necessarily represent those of their affiliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, orclaim that may be made by its manufacturer, is not guaranteedor endorsed by the publisher.References1. Abernethy A, Adams L, Barrett M, Bechtel C, Brennan P, Butte A, et al. Thepromise of digital health: then, now, and the future. NAM Perspectives. (2022):1–24.doi: 10.31478/202206e2. Kvedar J, Coye MJ, Everett W. Connected health: a review of technologies andstrategies to improve patient care with telemedicine and telehealth. Health Aff(Millwood). (2014) 33(2), 194–9. doi: 10.1377/hlthaff.2013.09923. Gentili A, Failla G, Melnyk A, Puleo V, Tanna GLD, Ricciardi W, et al. The cost-effectiveness of digital health interventions: a systematic review of the literature. FrontPublic Health. (2022) 10: 787135. doi: 10.3389/fpubh.2022.7871354. Foley T, Vale L. A framework for understanding, designing, developing andevaluating learning health systems. Learn Health Sys. (2023) 7(1), e10315. doi: 10.1002/lrh2.103155. Venkatesh KP, Raza MM, Kvedar JC. Health digital twins as tools for precisionmedicine: considerations for computation, implementation, and regulation. NPJDigit Med. (2022) 5(1):149–50. doi: 10.1038/s41746-022-00694-76. Sheikh K, Abimbola S. Learning health systems: Pathways to progress (FlagshipReport of the Alliance for Health Policy and Systems Research). Geneva: WorldHealth Organization. (2021). doi: 10.31219/osf.io/z7ydr7. Friedman CP, Wong A.K, Blumenthal D. Achieving a nationwide learning healthsystem. Sci Transl Med. (2010) 2(57), 57cm29–57cm29. doi: 10.1126/scitranslmed.30014568. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al.Beyond adoption: a new framework for theorizing and evaluating nonadoption,abandonment, and challenges to the scale-up, spread, and sustainability of healthand care technologies. J Med Internet Res. 1(2017) 9(11), e367. doi: 10.2196/jmir.87759. Mittelstadt B. (2017). Ethics of the Health-Related Internet of Things: A NarrativeReview (SSRN Scholarly Paper 2932221). doi: 10.2139/ssrn.293222110. Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, et al.Foundation models for generalist medical artificial intelligence. Nature. (2023) 616(7956):259–65. doi: 10.1038/s41586-023-05881-411. Topol EJ. High-performance medicine: the convergence of human and artificialintelligence. Nat Med. (2019) 25(1):44–65. doi: 10.1038/s41591-018-0300-712. Gruber S, Neumayr B, Smeddinck JD. Towards integration-preservingcustomization of just-in-time adaptive interventions with composite clabjects in RDFand SHACL. 2022 ACM/IEEE international conference on model driven engineeringlanguages and systems companion (MODELS-C) (2022).13. Pakianathan PVS, Wurhofer D, Kumar D, Niebauer J, Smeddinck J. Multi-Stakeholder design for Complex digital health systems: development of a modularopen research platform (MORE). Stud Health Technol Inform. (2023) 301, 204–9.doi: 10.3233/SHTI230040frontiersin.org

Editorial: Supporting sustainable behavior change and empowerment in ubiquitous and learning health systems

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Editorial: Supporting sustainable behavior change and empowerment in ubiquitous and learning health systems

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