Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

[EN] The study presents some results of customer paths¿ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men¿s bathroom or women¿s bathroom. Since the study has a comprehensive scope, we focused on male and female customers¿ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.Dogan, O.; Bayo-Monton, JL.; Fernández Llatas, C.; Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors. 19(3):1-20. https://doi.org/10.3390/s19030557S120193Oosterlinck, D., Benoit, D. F., Baecke, P., & Van de Weghe, N. (2017). Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits. Applied Geography, 78, 55-65. doi:10.1016/j.apgeog.2016.11.005Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., & Drap, P. (2016). Tracking multiple persons under partial and global occlusions: Application to customers’ behavior analysis. Pattern Recognition Letters, 81, 11-20. doi:10.1016/j.patrec.2016.04.011Wu, Y., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). Customer’s Flow Analysis in Physical Retail Store. Procedia Manufacturing, 3, 3506-3513. doi:10.1016/j.promfg.2015.07.672Dogan, O., & Öztaysi, B. (2018). In-store behavioral analytics technology selection using fuzzy decision making. Journal of Enterprise Information Management, 31(4), 612-630. doi:10.1108/jeim-02-2018-0035Hwang, I., & Jang, Y. J. (2017). Process Mining to Discover Shoppers’ Pathways at a Fashion Retail Store Using a WiFi-Base Indoor Positioning System. IEEE Transactions on Automation Science and Engineering, 14(4), 1786-1792. doi:10.1109/tase.2017.2692961Abedi, N., Bhaskar, A., Chung, E., & Miska, M. (2015). Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses. Transportation Research Part C: Emerging Technologies, 60, 124-141. doi:10.1016/j.trc.2015.08.010Mou, S., Robb, D. J., & DeHoratius, N. (2018). Retail store operations: Literature review and research directions. 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A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems

[EN] This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The solution adds to the choreography middleware three messages passing models to be coherent and compatible with current IoT messaging protocols. It is aimed to support blind hot plugging of new machines and help with service load balance. The discovery mechanism is implemented as a broker service and supports regular expressions (Regex) in message scope to discern both publishing patterns offered by data providers and client services necessities. Results compare Control Process Unit (CPU) usage in a request¿response and datacentric configuration and analyze both regex interpreter latency times compared with a traditional message structure as well as its impact on CPU and memory consumption.The choreography engine was developed and supported by the SABIEN research group of the Universitat Politecnica de Valencia (http://www.sabien.upv.es/en/).Blanc Clavero, S.; Bayo-Monton, JL.; Palanca-Barrio, S.; Arreaga-Alvarado, NX. (2021). A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems. Sensors. 21(2):1-19. https://doi.org/10.3390/s21020672S11921

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. Nature, 412(6846), 543-545. doi:10.1038/35087589United Nations, Department of Economic and Social Affairs, World Population Prospoects 2019 https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdfAtzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122-140. doi:10.1016/j.adhoc.2016.12.004Cook, D. J., Duncan, G., Sprint, G., & Fritz, R. L. (2018). Using Smart City Technology to Make Healthcare Smarter. Proceedings of the IEEE, 106(4), 708-722. doi:10.1109/jproc.2017.2787688Cook, D. J., & Krishnan, N. (2014). Mining the home environment. Journal of Intelligent Information Systems, 43(3), 503-519. doi:10.1007/s10844-014-0341-4Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. 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Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems

[EN] Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in different fields of medicine. Google Glass satisfies the need of managing and having rapid access to real-time information in different health care scenarios. Among the most common applications are access to electronic medical records, display monitorizations, decision support and remote consultation in specialties ranging from ophthalmology to surgery and teaching. The device enables a user-friendly hands-free interaction with remote health information systems and broadcasting medical interventions and consultations from a first-person point of view. However, scientific evidence highlights important technical limitations in its use and integration, such as failure in connectivity, poor reception of images and automatic restart of the device. This article presents a technical study on the aforementioned limitations (specifically on the latency, reliability and performance) on two standard communication schemes in order to categorize and identify the sources of the problems. Results have allowed us to obtain a basis to define requirements for medical applications to prevent network, computational and processing failures associated with the use of Google Glass.Authors would like to acknowledge the Laboratory for the Analysis for Human Behavior (www.sabien.upv.es/lach) and the Operative Program FEDER 2007/2013, for providing the necessary materials to undertake the presented research. The work done by A.L. was funded by the Ministry of Economy and Competitiveness: Promoting Youth Employment Program and Implementation of the (PEJ-2014-A-06813) Youth Guarantee 2014. The subsidized activity is part of the National System of Youth Guarantee and are co-financed under the Operational Program for Youth Employment, with financial resources from the Initiative Youth Employment (IYE) and the European Social Fund (ESF) for the period 2014-2020.Martínez Millana, A.; Bayo Montón, JL.; Lizondo García, A.; Fernández Llatas, C.; Traver Salcedo, V. (2016). Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems. Sensors. 16(2142):1-12. https://doi.org/10.3390/s16122142S112162142Abrahams, E., Ginsburg, G. S., & Silver, M. (2005). The Personalized Medicine Coalition. American Journal of PharmacoGenomics, 5(6), 345-355. doi:10.2165/00129785-200505060-00002Eysenbach, G. (2001). What is e-health? 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Accuracy of Remote Electrocardiogram Interpretation With the Use of Google Glass Technology. The American Journal of Cardiology, 115(3), 374-377. doi:10.1016/j.amjcard.2014.11.008Cicero, M. X., Walsh, B., Solad, Y., Whitfill, T., Paesano, G., Kim, K., … Cone, D. C. (2015). Do You See What I See? Insights from Using Google Glass for Disaster Telemedicine Triage. Prehospital and Disaster Medicine, 30(1), 4-8. doi:10.1017/s1049023x1400140xWu, T. S., Dameff, C. J., & Tully, J. L. (2014). Ultrasound-Guided Central Venous Access Using Google Glass. The Journal of Emergency Medicine, 47(6), 668-675. doi:10.1016/j.jemermed.2014.07.045Lewis, T. L., & Vohra, R. S. (2013). Smartphones make smarter surgeons. British Journal of Surgery, 101(4), 296-297. doi:10.1002/bjs.9328Albrecht, U.-V., von Jan, U., Kuebler, J., Zoeller, C., Lacher, M., Muensterer, O. J., … Hagemeier, L. (2014). Google Glass for Documentation of Medical Findings: Evaluation in Forensic Medicine. 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Optometry and Vision Science, 91(8), 1021-1030. doi:10.1097/opx.0000000000000326Tully, J., Dameff, C., Kaib, S., & Moffitt, M. (2015). Recording Medical Students’ Encounters With Standardized Patients Using Google Glass. Academic Medicine, 90(3), 314-316. doi:10.1097/acm.0000000000000620Fox, B. I., & Felkey, B. G. (2013). Potential Uses of Google Glass in the Pharmacy. Hospital Pharmacy, 48(9), 783-784. doi:10.1310/hpj4809-783Nguyen, V., & Gruteser, M. (2015). First Experiences with GOOGLE GLASS in Mobile Research. ACM SIGMOBILE Mobile Computing and Communications Review, 18(4), 44-47. doi:10.1145/2721914.272193

Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

[EN] Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices (p << 0.01) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.Bayo-Monton, JL.; Martinez-Millana, A.; Han, W.; Fernández Llatas, C.; Sun, Y.; Traver Salcedo, V. (2018). Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care. Sensors. 18(6). https://doi.org/10.3390/s18061851S18

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

[EN] Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.The authors wish to acknowledge the consortium of the MOSAIC project (funded by the European Commission, Grant No. FP7-ICT 600914) for their commitment during concept development, which led to the development of the research reported in this manuscriptMartinez-Millana, A.; Bayo-Monton, JL.; Argente-Pla, M.; Fernández Llatas, C.; Merino-Torres, JF.; Traver Salcedo, V. (2018). Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. Sensors. 18 (1)(79):1-26. https://doi.org/10.3390/s18010079S12618 (1)79Thomas, C. C., & Philipson, L. H. (2015). Update on Diabetes Classification. Medical Clinics of North America, 99(1), 1-16. doi:10.1016/j.mcna.2014.08.015Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2006). Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444(7121), 840-846. doi:10.1038/nature05482Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Practice, 103(2), 137-149. doi:10.1016/j.diabres.2013.11.002Beagley, J., Guariguata, L., Weil, C., & Motala, A. A. (2014). Global estimates of undiagnosed diabetes in adults. Diabetes Research and Clinical Practice, 103(2), 150-160. doi:10.1016/j.diabres.2013.11.001Hippisley-Cox, J., Coupland, C., Robson, J., Sheikh, A., & Brindle, P. (2009). Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ, 338(mar17 2), b880-b880. doi:10.1136/bmj.b880Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., … Cupples, L. A. (2008). Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes. New England Journal of Medicine, 359(21), 2208-2219. doi:10.1056/nejmoa0804742Gillies, C. L., Abrams, K. R., Lambert, P. C., Cooper, N. J., Sutton, A. J., Hsu, R. T., & Khunti, K. (2007). Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ, 334(7588), 299. doi:10.1136/bmj.39063.689375.55Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: systematic review. BMJ, 343(nov28 1), d7163-d7163. doi:10.1136/bmj.d7163Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162(1), 55. doi:10.7326/m14-0697Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., … Schroter, S. (2013). Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research. PLoS Medicine, 10(2), e1001381. doi:10.1371/journal.pmed.1001381Collins, G. S., & Moons, K. G. M. (2012). Comparing risk prediction models. BMJ, 344(may24 2), e3186-e3186. doi:10.1136/bmj.e3186Riley, R. D., Ensor, J., Snell, K. I. E., Debray, T. P. A., Altman, D. G., Moons, K. G. M., & Collins, G. S. (2016). External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, i3140. doi:10.1136/bmj.i3140Reilly, B. M., & Evans, A. T. (2006). Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions. Annals of Internal Medicine, 144(3), 201. doi:10.7326/0003-4819-144-3-200602070-00009Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009). Prognosis and prognostic research: validating a prognostic model. 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J., Kumari, M., … Humphries, S. E. (2010). Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ, 340(jan14 1), b4838-b4838. doi:10.1136/bmj.b4838Sackett, D. L. (1997). Evidence-based medicine. Seminars in Perinatology, 21(1), 3-5. doi:10.1016/s0146-0005(97)80013-4Segagni, D., Ferrazzi, F., Larizza, C., Tibollo, V., Napolitano, C., Priori, S. G., & Bellazzi, R. (2011). R Engine Cell: integrating R into the i2b2 software infrastructure. Journal of the American Medical Informatics Association, 18(3), 314-317. doi:10.1136/jamia.2010.007914Semantic Webhttp://www.w3.org/2001/sw/Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). 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Users' Experiences of a Mobile Health Self-Management Approach for the Treatment of Cystic Fibrosis: Mixed Methods Study

[EN] Background: Despite a large number of clinical trials aiming at evaluating the digital self-management of chronic diseases, there is little discussion about users¿ experiences with digital approaches. However, a good user experience is a critical factor for technology adoption. Understanding users¿ experiences can inform the design of approaches toward increased motivation for digital self-management. Objective: This study aimed to evaluate the self-management of cystic fibrosis (CF) with a focus on gastrointestinal concerns and the care of young patients. Following a user-centered design approach, we developed a self-management app for patients and parents and a web tool for health care professionals (HCPs). To evaluate the proposed solutions, a 6-month clinical trial was conducted in 6 European CF competence centers. This paper analyzes the user acceptance of the technology and the benefits and disadvantages perceived by the trial participants. Methods: A mixed methods approach was applied. Data were collected through 41 semistructured qualitative interviews of patients, parents, and HCPs involved in the clinical trial. In addition, data were collected through questionnaires embedded in the self-management app. Results: Support for enzyme dose calculation and nutrition management was found to be particularly useful. Patients and parents rapidly strengthened their knowledge about the treatment and increased their self-efficacy. Reported benefits include reduced occurrence of symptoms and enhanced quality of life. Patients and parents had different skills, requiring follow-up by HCPs in an introductory phase. HCPs valued obtaining precise information about the patients, allowing for more personalized advice. However, the tight follow-up of several patients led to an increased workload. Over time, as patient self-efficacy increased, patient motivation for using the app decreased and the quality of the reported data was reduced. Conclusions: Self-management enfolds a collaboration between patients and HCPs. To be successful, a self-management approach should be accepted by both parties. Through understanding behaviors and experiences, this study defines recommendations for a complex case¿the demanding treatment of CF. We identify target patient groups and situations for which the app is most beneficial and suggest focusing on these rather than motivating for regular app usage over a long time. We also advise the personalized supervision of patients during the introduction of the approach. Finally, we propose to develop guidance for HCPs to facilitate changes in practice. As personalization and technology literacy are factors found to influence the acceptance of digital self-management of other chronic diseases, it is relevant to consider the proposed recommendations beyond the case of CF.The authors of this paper, on behalf of the MyCyFAPP consortium, acknowledge the European Union and the Horizon 2020 Research and Innovation Framework Programme for funding the project (ref. 643806). The authors would like to thank all project partners for their collaboration during participant recruitment and project management. Without the dedication of participants in terms of time, effort, and valuable input, this publication would not have been possible. The authors would like to thank all the participants who contributed to this work.Floch, J.; Vilarinho, T.; Zettl, A.; Ibáñez Sánchez, G.; Calvo-Lerma, J.; Stav, E.; Halland Haro, P.... (2020). Users' Experiences of a Mobile Health Self-Management Approach for the Treatment of Cystic Fibrosis: Mixed Methods Study. JMIR mHealth and uHealth. 8(7):1-19. https://doi.org/10.2196/15896S11987Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy. Journal of Medical Internet Research, 12(1), e4. doi:10.2196/jmir.1376Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., … Haines, A. (2013). The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review. PLoS Medicine, 10(1), e1001362. doi:10.1371/journal.pmed.1001362Marcolino, M. S., Oliveira, J. A. Q., D’Agostino, M., Ribeiro, A. L., Alkmim, M. B. M., & Novillo-Ortiz, D. (2018). The Impact of mHealth Interventions: Systematic Review of Systematic Reviews. JMIR mHealth and uHealth, 6(1), e23. doi:10.2196/mhealth.8873Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. doi:10.2307/30036540Cystic Fibrosis Europe2020-05-27https://www.cf-europe.eu/Conway, S., Balfour-Lynn, I. M., De Rijcke, K., Drevinek, P., Foweraker, J., Havermans, T., … Peckham, D. (2014). European Cystic Fibrosis Society Standards of Care: Framework for the Cystic Fibrosis Centre. Journal of Cystic Fibrosis, 13, S3-S22. doi:10.1016/j.jcf.2014.03.009Floch, J., Zettl, A., Fricke, L., Weisser, T., Grut, L., Vilarinho, T., … Schauber, C. (2018). User Needs in the Development of a Health App Ecosystem for Self-Management of Cystic Fibrosis: User-Centered Development Approach. JMIR mHealth and uHealth, 6(5), e113. doi:10.2196/mhealth.8236Calvo-Lerma, J., Martinez-Jimenez, C. P., Lázaro-Ramos, J.-P., Andrés, A., Crespo-Escobar, P., Stav, E., … Ribes-Koninckx, C. (2017). Innovative approach for self-management and social welfare of children with cystic fibrosis in Europe: development, validation and implementation of an mHealth tool (MyCyFAPP). BMJ Open, 7(3), e014931. doi:10.1136/bmjopen-2016-014931Borowitz, D., Gelfond, D., Maguiness, K., Heubi, J. E., & Ramsey, B. (2013). Maximal daily dose of pancreatic enzyme replacement therapy in infants with cystic fibrosis: A reconsideration. Journal of Cystic Fibrosis, 12(6), 784-785. doi:10.1016/j.jcf.2013.05.011Calvo-Lerma, J., Fornés-Ferrer, V., Peinado, I., Heredia, A., Ribes-Koninckx, C., & Andrés, A. (2019). A first approach for an evidence-based in vitro digestion method to adjust pancreatic enzyme replacement therapy in cystic fibrosis. PLOS ONE, 14(2), e0212459. doi:10.1371/journal.pone.0212459Calvo-Lerma, J., Hulst, J., Boon, M., Martins, T., Ruperto, M., Colombo, C., … Ribes-Koninckx, C. (2019). The Relative Contribution of Food Groups to Macronutrient Intake in Children with Cystic Fibrosis: A European Multicenter Assessment. Journal of the Academy of Nutrition and Dietetics, 119(8), 1305-1319. doi:10.1016/j.jand.2019.01.003Turck, D., Braegger, C. P., Colombo, C., Declercq, D., Morton, A., Pancheva, R., … Wilschanski, M. (2016). ESPEN-ESPGHAN-ECFS guidelines on nutrition care for infants, children, and adults with cystic fibrosis. Clinical Nutrition, 35(3), 557-577. doi:10.1016/j.clnu.2016.03.004Vo, V., Auroy, L., & Sarradon-Eck, A. (2019). Patients’ Perceptions of mHealth Apps: Meta-Ethnographic Review of Qualitative Studies. JMIR mHealth and uHealth, 7(7), e13817. doi:10.2196/13817Anderson, K., Burford, O., & Emmerton, L. (2016). Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences. PLOS ONE, 11(5), e0156164. doi:10.1371/journal.pone.0156164Boon, M., Calvo-Lerma, J., Claes, I., Havermans, T., Asseiceira, I., Bulfamante, A., … Ribes-Koninckx, C. (2020). Use of a mobile application for self-management of pancreatic enzyme replacement therapy is associated with improved gastro-intestinal related quality of life in children with Cystic Fibrosis. Journal of Cystic Fibrosis, 19(4), 562-568. doi:10.1016/j.jcf.2020.04.001Hevner, March, Park, & Ram. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75. doi:10.2307/25148625-ISO 9241-210:2010 Ergonomics of Human-System Interaction — Part 210: Human-Centred Design for Interactive SystemsInternational Organization for Standardization20102020-06-05https://www.iso.org/standard/52075.htmlVilarinho, T., Floch, J., & Stav, E. (2017). Co-designing a mHealth Application for Self-management of Cystic Fibrosis. Lecture Notes in Computer Science, 3-22. doi:10.1007/978-3-319-67687-6_1Kristensen, G. 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