Abstract
Current cardiac rehabilitation programs intending to increase physical activity of patients suffer from a lack of knowledge about effective patient’s activity profiles and their associated behavior. This leads to the fact that therapies are not completely tailored to the patient, causing non-adherence to the proposed treatment schedule. An important potential for increasing the physical activity level of patients is available in their daily travel behaviour that can be made more active. To validate this potential, we propose a Cardiac Travel Advice Support System (CTASS) digital framework for personalized travel behaviour advice to cardiac patients. The travel behaviour of the group of patients whose actual physical activity level is expected to be too low is monitored by a smartphone application that objectifies their daily activity schedules. The data from the schedules is analysed semi-automatically by the CTASS. Based on this analysis, the doctor can provide a treatment that is personalized to the specific contexts of the patient. In this way, we try to optimize their travel-related physical activity. Moreover, we predict the risk of non-adherence to the therapy taking into account the derived characteristics of the patient.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Amiribesheli M, Benmansour A, Bouchachia A (2015) A review of smart homes in healthcare. J Ambient Intell Humaniz Comput 6:495–517. https://doi.org/10.1007/s12652-015-0270-2
Berger M, Platzer M (2015) Field evaluation of the smartphone-based Travel Behaviour Data Collection App “SmartMo”. Transp Res Proc 11:263–279. https://doi.org/10.1016/j.trpro.2015.12.023
Bouchard C, Blair SN, Haskell W (2012) Physical activity and health, 2nd edn. Human Kinetics, Champaign
Calvaresi D, Cesarini D, Sernani P et al (2017) Exploring the ambient assisted living domain: a systematic review. J Ambient Intell Humaniz Comput 8:239–257. https://doi.org/10.1007/s12652-016-0374-3
Chaddha A, Jackson EA, Richardson CR, Franklin BA (2017) Technology to help promote physical activity. Am J Cardiol N Y 119:149–152. https://doi.org/10.1016/j.amjcard.2016.09.025
Cleland CL, Hunter RF, Kee F et al (2014) Validity of the Global Physical Activity Questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour. BMC Public Health Lond. https://doi.org/10.1186/1471-2458-14-1255
Danner UN, Aarts H, Vries NK (2008) Habit vs. intention in the prediction of future behaviour: the role of frequency, context stability and mental accessibility of past behaviour. Br J Soc Psychol 47:245–265. https://doi.org/10.1348/014466607X230876
de Nazelle A, Nieuwenhuijsen MJ, Anto JM et al (2011) Improving health through policies that promote active travel: a review of evidence to support integrated health impact assessment. Environ Int 37(4):766–777. https://doi.org/10.1016/j.envint.2011.02.003
De Vos C, Li X, Van Vlaenderen I et al (2013) Participating or not in a cardiac rehabilitation programme: factors influencing a patient’s decision. Eur J Prev Cardiol 20:341–348. https://doi.org/10.1177/2047487312437057
Francis J, Eccles MP, Johnston M, Walker AE, Grimshaw JM, Foy R, Kaner EFS, Smith L (2004) Constructing questionnaires based on the theory of planned behaviour: a manual for health services researchers. Centre for Health Services Research, University of Newcastle upon Tyne, Newcastle upon Tyne, UK
Gerrior S, Juan W, Basiotis P (2006) An easy approach to calculating estimated energy requirements. Prev Chronic Dis 3:A129
Geurs KT, Thomas T, Bijlsma M, Douhou S (2015) Automatic trip and mode detection with move smarter: first results from the Dutch mobile mobility panel. Transp Res Proc 11:247–262. https://doi.org/10.1016/j.trpro.2015.12.022
Hagströmer M, Oja P, Sjöström M (2006) The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr Cambr 9:755–762. https://doi.org/10.1079/PHN2005898
Ham N, Dirin A, Laine TH (2016) Machine learning and dynamic user interfaces in a context aware nurse application environment. J Ambient Intell Humaniz Comput 1–13. https://doi.org/10.1007/s12652-016-0384-1
Kroesen M (2015) Do partners influence each other’s travel patterns? A new approach to study the role of social norms. Transp Res Part A Policy Pract 78:489–505. https://doi.org/10.1016/j.tra.2015.06.015
Lee I-M, Shiroma EJ (2014) Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br J Sport Med Lond 48:197. https://doi.org/10.1136/bjsports-2013-093154
Neven A (2015) Explaining activity-related travel behaviour in persons with disabilities by means of health condition and contextual factors. PhD thesis, University of Hasselt, Belgium. http://hdl.handle.net/1942/20558
Newman P, Matan A (2012) Human mobility and human health. Curr Opin Environ Sustain 4:420–426. https://doi.org/10.1016/j.cosust.2012.08.005
Nguyen HH, Silva JN (2016) Use of smartphone technology in cardiology. Trends Cardiovasc Med Phila 26:376–386. https://doi.org/10.1016/j.tcm.2015.11.002
Santos A, McGuckin N, Nakamoto HY, Gray D, Liss S (2011) Summary of Travel Trends: 2009 National Household Travel Survey. http://nhts.ornl.gov/2009/pub/stt.pdf
Park LG, Beatty A, Stafford Z, Whooley MA (2016) Mobile phone interventions for the secondary prevention of cardiovascular disease. Prog Cardiovasc Dis 58:639–650. https://doi.org/10.1016/j.pcad.2016.03.002
Pr C, Da B, Ra F et al (1997) Measuring general levels of physical activity: preliminary evidence for the Physical Activity Questionnaire for older children. Med Sci Sports Exerc 29:1344–1349. https://doi.org/10.1097/00005768-199710000-00011
Rehabilitation AA of C& P (2004) Guidelines for cardiac rehabilitation and secondary prevention programs. Human Kinetics, Champaign
Safi H, Assemi B, Mesbah M, Ferreira L (2017) An empirical comparison of four technology-mediated travel survey methods. J Traffic Transp Eng (Engl Ed) 4:80–87. https://doi.org/10.1016/j.jtte.2015.12.003
Thompson RG, Kayak H (2011) Estimating personal physical activity from transport. Australas Transp Res Forum 1–16
UHasselt I (2012) Handleiding SPARROW app. https://www.uhasselt.be/documents/IMOB/Nieuws%20en%20Agenda/Handleiding-Sparrow.pdf
Vassilaki M, Linardakis M, Polk DM, Philalithis Α (2015) The burden of behavioral risk factors for cardiovascular disease in Europe. A significant prevention deficit. Prev Med (Baltim) 81:326–332. https://doi.org/10.1016/j.ypmed.2015.09.024
WHO (2016) WHO—cardiovascular diseases (CVDs). In: Hearts: technical package for cardiovascular disease management in primary health care. World Health Organization. ISBN 978 92 4 151137 7
Widmer RJ, Collins NM, Collins CS et al (2015) Digital Health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. Mayo Clin Proc Rochester 90:469–480
Zhao F, Ghorpade A, Pereira FC et al (2015) Stop detection in smartphone-based travel surveys. Transp Res Proc 11:218–226. https://doi.org/10.1016/j.trpro.2015.12.019
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Batool, T., Vanrompay, Y., Neven, A. et al. CTASS: an intelligent framework for personalized travel behaviour advice to cardiac patients. J Ambient Intell Human Comput 10, 4693–4705 (2019). https://doi.org/10.1007/s12652-018-0847-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-0847-7