Modelling of chronic patient physical - activity- related behaviour towards healthy lifestyle support

1. Modelling of chronic pa0ent physical-ac0vity-
related behaviour towards healthy lifestyle
support
Kris0na Livitckaia
ESR 5
Aristotle University of Thessaloniki, 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(ent
healthy behaviour and lifestyle support
can allow to predict chronic pa(ent’s
(non-) adherence to daily health-
related behaviour and choices with
regard to the short- and/or long-term
based on the combina(on of
qualita0ve and quan0ta0ve
determinants, including
REFERENCES
1. Marks, R. (2005). A Review and Synthesis of Research Evidence for Self-Efficacy-Enhancing Interven(ons for Reducing Chronic Disability: Implica(ons for Health Educa(on
Prac(ce (Part II). Health Promo(on Prac(ce, 6(2), 148–156. hTp://doi.org/10.1177/1524839904266792
2. Pavel, M., Jimison, H. B., Korhonen, I., Gordon, C. M., & Saranummi, N. (2015). Behavioral Informa(cs and Computa(onal Modeling in Support of Proac(ve Health Management
and Care. IEEE Transac(ons on Biomedical Engineering, 62(12), 2763–2775. hTp://doi.org/10.1109/TBME.2015.2484286
3. World Health Organiza(on. (2005). Preven(ng Chronic Diseases: a Vital Investment. World Health, 202.hTp://doi.org/10.1093/ije/dyl098
4. WHO.www.who.int/chp/chronic_disease. (2005). Part Two - The urgent need for ac(on. Preven(ng Chronic Diseases: A Vital Investment, 31–87 accessed on 13/4/16. Retrieved
from hTp://www.who.int/chp/chronic_disease_report/contents/en/
5. Halpin, H. A., Morales-Surez-Varela, M. M., & Mar(n-Moreno, J. M. (2010). Chronic disease preven(on and the new public health. Public Health Reviews, 32(1), 120–154.
6. WHO | Physical ac(vity. (n.d.). Retrieved July 6, 2016, from hTp://www.who.int/mediacentre/factsheets/fs385/en/#.V3zVyEWiT1k.mendeley
BACKGROUND
Connected health is a concept for preserving ci(zens’ health and well-being by providing technologically
enhanced 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 are
adherent to the process of disease self-management, it reduces the propor(on of those pa(ents who ignore
prescribed recommenda(ons and lose faith in the desirable and poten(ally achievable outcomes 1.
Despite a solid number of evidence-based inves(ga(ons regarding the rela(on of control and preven(on of
chronic diseases to pa(ent health behaviour adherence, there is a limited research taking into considera(on
unified pa(ent characteris(cs from mul(faceted backgrounds and aspects (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 lifestyle
factors and determinants 2,3. In this context, there is a need for extended analysis for health behaviour
understanding and predic(on, beneficial to improvement and tailoring of interven(ons through technological
solu(ons 2.
The aim of the research is to propose improvements for interven(ons design with regard to qualita(ve and
quan(ta(ve pa(ent characteris(cs, states and contextual factors to allow pa(ent behaviour predic(ons paTerns
for pa(ent behavioural support.
RESEARCH SCOPE
Cardiovascular diseases are the most
common chronic illness that characterizes
the state of public health and has a
notable effect on major global indexes of
morbidity, disability and mortality 4.
Ongoing increase in the incidence of
cardiovascular diseases is oken associated
with unhealthy lifestyle choices, including
lack of physical ac(vity, smoking, and
other health-related bahaviours 3,4,5.
Moreover, physical inac0vity is one of
the key risk factors for non-communicable
diseases such as coronary heart disease 6.
The scope of the research comprises predic(ve modelling
of cardiac chronic pa(ent’s adherence to physical-ac(vity-
related behaviour.
PHASE I
Inves(ga(on of chronic
pa(ents’ determinants and
risk factors affec(ng (non-)
adherence to physical ac(vity
and exercise behaviour
PHASE II
Inves(ga(on of methods of
mathema(cal and
computa(onal predic(ve
modeling and data mining
techniques
PHASE III
Valida(on of set of
computa(onal models for
pa(ent health-related
adherence level predic(on at
different (me scales
PROCESS
Forma(on of qualita(ve
and quan(ta(ve poten(al
a T r i b u t e s a n d
determinants of a pa(ent
adherence to physical
ac(vity-related behaviour
f o r f u r t h e r ( n o n - )
adherence predic(on
inves(ga(on
PROCESS
D e v e l o p m e n t o f
computa(onal models
based on inves(gated
determinants and paTerns
for predic(on of pa(ent
(non-) adherence level to
physical-ac(vity-related
behaviour
PROCESS
Valida(on and improvement
of computa(onal models
based on the data sets
s u p p o r t e d b y t h e
collabora(on with the
Laboratory of Compu0ng
and Medical Informa0cs,
and the Laboratory of
Sports Medicine, Aristotle
University of Thessaloniki,
Greece
Study with
pa0ent group
Determinants rela0ons and affects
Models
valida0on
Predic0ve
modelling
Drawn conclusions
§ Currently applied approaches to the segmenta(on and
predic(ve modeling of pa(ents’ physical-ac(vity-related
behaviour, oken take into account only clinical or
psychological aspects
§ Need for extended analysis for health behaviour
understanding and predic(on, beneficial to improvement of
behavioural interven(ons technologies
COMPUTATIONAL APPROACHES
Exis(ng solu(ons inves(ga(on
§ Systema(c literature review for inves(ga(on of
informa(on systems and its components developed for
chronic pa(ents’ health behavior modifica(on
§ Conducted databases: IEEE Xplore, the ACM Digital
Library PubMed, WIPO Patentscope, Espacenet, USPTO
§ Advanced search strategy
§ Main concepts: health behaviour change and
computa0onal modelling
Classifica0on of the results
§ Stage of the development
§ Deployment sepngs
§ Group of pa(ents
§ Interven(on sepngs
§ Target health behaviour
§ Underlying mul(disciplinary
domains
§ Sources and types of
consumer data
§ Data processing techniques
§ Level of personaliza(on
§ Evalua(on
§ Improvement goals
PHYSICAL-ACTIVITY-RELATED ADHERENCE DETERMINANTS
Clinically proven pa(ents’ determinants
§ Literature review based on the standard methodological
framework
§ Conducted databases: PubMed and Cochrane Library
§ Advanced search strategy
§ Main concepts: influence factors, adherence to
physical-ac0vity-related behaviour, heart
diseases
Search results
GAPS IDENTIFIED
§ Lack of lifestyle health-related data inves(ga(on and its affec(ng
power to pa(ent adherence
§ Lack of inves(ga(on of rela(ons among defined determinants
§ Need for inves(ga(on of pa(ent physical ac(vity and exercise
long-term adherence
Rela0on of determinants to adherence
Defined determinants