Modellingof chronicpa0entphysical-ac0vity-related behaviour towards healthy lifestylesupport

Kris0naLivitckaiaESR5AristotleUniversityofThessaloniki,Greece

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 676201

HYPOTHESIS

Computa(onal models for pa(enthealthybehaviour and lifestyle support

can allow topredict chronic pa(ent’s(non-) adherence to daily health-related behaviour and choices withregard to the short- and/or long-termba sed on t he comb ina(on o f

qualita0ve and quan0ta0vedeterminants,including

REFERENCES1.  Marks, R. (2005). A Review and Synthesis of Research Evidence for Self-Efficacy-Enhancing Interven(ons for Reducing ChronicDisability: Implica(ons forHealth Educa(on

Prac(ce(PartII).HealthPromo(onPrac(ce,6(2),148–156.hTp://doi.org/10.1177/15248399042667922.  Pavel,M.,Jimison,H.B.,Korhonen,I.,Gordon,C.M.,&Saranummi,N.(2015).BehavioralInforma(csandComputa(onalModelinginSupportofProac(veHealthManagement

andCare.IEEETransac(onsonBiomedicalEngineering,62(12),2763–2775.hTp://doi.org/10.1109/TBME.2015.24842863.  WorldHealthOrganiza(on.(2005).Preven(ngChronicDiseases:aVitalInvestment.WorldHealth,202.hTp://doi.org/10.1093/ije/dyl0984.  WHO.www.who.int/chp/chronic_disease.(2005).PartTwo-Theurgentneedforac(on.Preven(ngChronicDiseases:AVitalInvestment,31–87accessedon13/4/16.Retrieved

fromhTp://www.who.int/chp/chronic_disease_report/contents/en/5.  Halpin,H.A.,Morales-Surez-Varela,M.M.,&Mar(n-Moreno,J.M.(2010).Chronicdiseasepreven(onandthenewpublichealth.PublicHealthReviews,32(1),120–154.6.  WHO|Physicalac(vity.(n.d.).RetrievedJuly6,2016,fromhTp://www.who.int/mediacentre/factsheets/fs385/en/#.V3zVyEWiT1k.mendeley

BACKGROUNDConnected health is a concept for preserving ci(zens’ health and well-being by providing technologicallyenhanced proac(ve and pa(ent-centered health services, e.g. interven(ons. In its turn, pa(ent-centered care

demands pa0ent adherence and compliance to healthy behaviour and lifestyle choices. If pa(ents areadherent to the process of disease self-management, it reduces the propor(on of those pa(entswho ignoreprescribedrecommenda(onsandlosefaithinthedesirableandpoten(allyachievableoutcomes1.

Despite a solid number of evidence-based inves(ga(ons regarding the rela(on of control and preven(on ofchronic diseases to pa(ent health behaviour adherence, there is a limited research taking into considera(onunifiedpa(entcharacteris(csfrommul(facetedbackgroundsandaspects(e.g.medicine,psychology,sociology,lifestyle, etc.). While the key towards successful Connected health interven(ons development lies under

personaliza0on and tailoring to a pa(ent by taking into account and adap(ng to addi0onal lifestylefactorsanddeterminants2,3. Inthiscontext,thereisaneedforextendedanalysisforhealthbehaviourunderstanding and predic(on, beneficial to improvement and tailoring of interven(ons through technologicalsolu(ons2.

Theaim of the research is to propose improvements for interven(ons designwith regard to qualita(ve andquan(ta(vepa(entcharacteris(cs,statesandcontextualfactorstoallowpa(entbehaviourpredic(onspaTernsforpa(entbehaviouralsupport.

RESEARCHSCOPECardiovascular diseases are themostcommon chronic illness that characterizesthe state of public health and has anotable effect onmajor global indexes ofmorbidity, disability and mortality 4.Ongoing increase in the incidence ofcardiovasculardiseasesisokenassociatedwith unhealthy lifestyle choices, includinglack of physical ac(vity, smoking, andother health-related bahaviours 3,4,5.Moreover,physical inac0vity is oneofthekeyriskfactorsfornon-communicablediseasessuchascoronaryheartdisease6.

Thescopeoftheresearchcomprisespredic(vemodellingofcardiacchronicpa(ent’sadherencetophysical-ac(vity-

relatedbehaviour.

PHASEIInves(ga(onofchronic

pa(ents’determinantsandriskfactorsaffec(ng(non-)

adherencetophysicalac(vityandexercisebehaviour

PHASEIIInves(ga(onofmethodsof

mathema(calandcomputa(onalpredic(vemodelinganddatamining

techniques

PHASEIIIValida(onofsetof

computa(onalmodelsforpa(enthealth-related

adherencelevelpredic(onatdifferent(mescales

PROCESS

Forma(on of qualita(veand quan(ta(ve poten(ala T r i b u t e s a n ddeterminants of a pa(entadherence to physicalac(vity-related behaviourf o r f u r t h e r ( n o n - )adherence predic(oninves(ga(on

PROCESS

D e v e l o p m e n t o fcomputa(onal modelsbased on inves(gateddeterminantsandpaTernsfor predic(on of pa(ent(non-) adherence level tophysical-ac(vity-relatedbehaviour

PROCESSValida(on and improvementof computa(onal modelsbased on the data setss u p p o r t e d b y t h eco l labora(on wi th theLaboratory of Compu0ngand Medical Informa0cs,and the Laboratory ofSportsMedicine,AristotleUniversityofThessaloniki,Greece

Studywithpa0entgroup

Determinantsrela0onsandaffects

Modelsvalida0on

Predic0vemodelling

Drawnconclusions§  Currently applied approaches to the segmenta(on and

predic(ve modeling of pa(ents’ physical-ac(vity-relatedbehaviour, oken take into account only clinical orpsychologicalaspects

§  Need for extended analysis for health behaviourunderstandingandpredic(on,beneficialtoimprovementofbehaviouralinterven(onstechnologies

COMPUTATIONALAPPROACHES

Exis(ngsolu(onsinves(ga(on§  Systema(c literature review for inves(ga(on of

informa(on systems and its components developed forchronicpa(ents’healthbehaviormodifica(on

§  Conducted databases: IEEE Xplore, the ACM DigitalLibraryPubMed,WIPOPatentscope,Espacenet,USPTO

§  Advancedsearchstrategy§  Main concepts: health behaviour change and

computa0onalmodelling

Classifica0onoftheresults§  Stageofthedevelopment

§  Deploymentsepngs§  Groupofpa(ents§  Interven(onsepngs§  Targethealthbehaviour§  Underlyingmul(disciplinary

domains

§  Sourcesandtypesofconsumerdata

§  Dataprocessingtechniques§  Levelofpersonaliza(on

§  Evalua(on§  Improvementgoals

PHYSICAL-ACTIVITY-RELATEDADHERENCEDETERMINANTS

Clinicallyprovenpa(ents’determinants§  Literature reviewbasedon the standardmethodological

framework§  Conducteddatabases:PubMedandCochraneLibrary§  Advancedsearchstrategy

§  Main concepts: influence factors, adherence tophysical-ac0vity-related behaviour, heartdiseases

Searchresults

GAPSIDENTIFIED§  Lackof lifestylehealth-relateddata inves(ga(onand itsaffec(ng

powertopa(entadherence§  Lackofinves(ga(onofrela(onsamongdefineddeterminants§  Need for inves(ga(on of pa(ent physical ac(vity and exercise

long-termadherence

Rela0onofdeterminantstoadherence

Defineddeterminants