- -

Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Asensio Cuesta, Sabina es_ES
dc.contributor.author Sánchez-García, Ángel es_ES
dc.contributor.author Conejero, J. Alberto es_ES
dc.contributor.author Sáez Silvestre, Carlos es_ES
dc.contributor.author Rivero-Rodriguez, Alejandro es_ES
dc.contributor.author Garcia-Gomez, Juan M es_ES
dc.date.accessioned 2020-12-23T04:32:00Z
dc.date.available 2020-12-23T04:32:00Z
dc.date.issued 2019-02-01 es_ES
dc.identifier.issn 1660-4601 es_ES
dc.identifier.uri http://hdl.handle.net/10251/157773
dc.description.abstract [EN] Quality of life (QoL) indicators are now being adopted as clinical outcomes in clinical trials on cancer treatments. Technology-free daily monitoring of patients is complicated, time-consuming and expensive due to the need for vast amounts of resources and personnel. The alternative method of using the patients¿ own phones could reduce the burden of continuous monitoring of cancer patients in clinical trials. This paper proposes monitoring the patients¿ QoL by gathering data from their own phones. We considered that the continuous multiparametric acquisition of movement, location, phone calls, conversations and data use could be employed to simultaneously monitor their physical, psychological, social and environmental aspects. An open access phone app was developed (Human Dynamics Reporting Service (HDRS)) to implement this approach. We here propose a novel mapping between the standardized QoL items for these patients, the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and define HDRS monitoring indicators. A pilot study with university volunteers verified the plausibility of detecting human activity indicators directly related to QoL. es_ES
dc.description.sponsorship Funding for this study was provided by the authors' various departments, and partially by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies (727560)) and the MTS4up project (DPI2016-80054-R). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof International Journal of Environmental research and Public Health es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Oncology es_ES
dc.subject Cancer es_ES
dc.subject Activity recognition es_ES
dc.subject Mobile sensors es_ES
dc.subject Pattern recognition es_ES
dc.subject Visual analytics es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.subject.classification PROYECTOS DE INGENIERIA es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/ijerph16030461 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/727560/EU/Collective wisdom driving public health policies/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2016-80054-R/ES/BIOMARCADORES DINAMICOS BASADOS EN FIRMAS TISULARES MULTIPARAMETRICAS PARA EL SEGUIMIENTO Y EVALUACION DE LA RESPUESTA A TRATAMIENTO DE PACIENTES CON GLIOBLASTOMA Y CANCER DE/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Proyectos de Ingeniería - Departament de Projectes d'Enginyeria es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Asensio Cuesta, S.; Sánchez-García, Á.; Conejero, JA.; Sáez Silvestre, C.; Rivero-Rodriguez, A.; Garcia-Gomez, JM. (2019). Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects. International Journal of Environmental research and Public Health. 16(3):1-18. https://doi.org/10.3390/ijerph16030461 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/ijerph16030461 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 18 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 16 es_ES
dc.description.issue 3 es_ES
dc.identifier.pmid 30764535 es_ES
dc.identifier.pmcid PMC6388149 es_ES
dc.relation.pasarela S\378146 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Number of Smartphone Users Worldwide from 2014 to 2020 (in Billions)https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ es_ES
dc.description.references Mirkovic, J., Kaufman, D. R., & Ruland, C. M. (2014). Supporting Cancer Patients in Illness Management: Usability Evaluation of a Mobile App. JMIR mHealth and uHealth, 2(3), e33. doi:10.2196/mhealth.3359 es_ES
dc.description.references Xing Su, Hanghang Tong, & Ping Ji. (2014). Activity recognition with smartphone sensors. Tsinghua Science and Technology, 19(3), 235-249. doi:10.1109/tst.2014.6838194 es_ES
dc.description.references Schmitz Weiss, A. (2013). Exploring News Apps and Location-Based Services on the Smartphone. Journalism & Mass Communication Quarterly, 90(3), 435-456. doi:10.1177/1077699013493788 es_ES
dc.description.references Higgins, J. P. (2016). Smartphone Applications for Patients’ Health and Fitness. The American Journal of Medicine, 129(1), 11-19. doi:10.1016/j.amjmed.2015.05.038 es_ES
dc.description.references Rivenson, Y., Ceylan Koydemir, H., Wang, H., Wei, Z., Ren, Z., Günaydın, H., … Ozcan, A. (2018). Deep Learning Enhanced Mobile-Phone Microscopy. ACS Photonics, 5(6), 2354-2364. doi:10.1021/acsphotonics.8b00146 es_ES
dc.description.references Priye, A., Ball, C. S., & Meagher, R. J. (2018). Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor. Analytical Chemistry, 90(21), 12385-12389. doi:10.1021/acs.analchem.8b03521 es_ES
dc.description.references Measuring Quality of Life for Cancer Patients: Where Are We Today and Where Are We Headed Tomorrow?http://blog.mdsol.com/measuring-quality-of-life-for-cancer-patients-where-are-we-today-and-where-are-we-headed-tomorrow/ es_ES
dc.description.references Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., … Leow, A. (2018). Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. Journal of Medical Internet Research, 20(7), e241. doi:10.2196/jmir.9775 es_ES
dc.description.references Caruso, R., GiuliaNanni, M., Riba, M. B., Sabato, S., & Grassi, L. (2017). Depressive Spectrum Disorders in Cancer: Diagnostic Issues and Intervention. A Critical Review. Current Psychiatry Reports, 19(6). doi:10.1007/s11920-017-0785-7 es_ES
dc.description.references THE WHOQOL GROUP. (1998). Development of the World Health Organization WHOQOL-BREF Quality of Life Assessment. Psychological Medicine, 28(3), 551-558. doi:10.1017/s0033291798006667 es_ES
dc.description.references Basic Issues Concerning Health-Related Quality of Life. (2017). Central European Journal of Urology, 70(2). doi:10.5173/ceju.2017.923 es_ES
dc.description.references Sloan, J. A. (2011). Metrics to Assess Quality of Life After Management of Early-Stage Lung Cancer. The Cancer Journal, 17(1), 63-67. doi:10.1097/ppo.0b013e31820e15dc es_ES
dc.description.references Bordoni, R., Ciardiello, F., von Pawel, J., Cortinovis, D., Karagiannis, T., Ballinger, M., … Rittmeyer, A. (2018). Patient-Reported Outcomes in OAK: A Phase III Study of Atezolizumab Versus Docetaxel in Advanced Non–Small-cell Lung Cancer. Clinical Lung Cancer, 19(5), 441-449.e4. doi:10.1016/j.cllc.2018.05.011 es_ES
dc.description.references Hartkopf, A. D., Graf, J., Simoes, E., Keilmann, L., Sickenberger, N., Gass, P., … Wallwiener, M. (2017). Electronic-Based Patient-Reported Outcomes: Willingness, Needs, and Barriers in Adjuvant and Metastatic Breast Cancer Patients. JMIR Cancer, 3(2), e11. doi:10.2196/cancer.6996 es_ES
dc.description.references Wallwiener, M., Matthies, L., Simoes, E., Keilmann, L., Hartkopf, A. D., Sokolov, A. N., … Brucker, S. Y. (2017). Reliability of an e-PRO Tool of EORTC QLQ-C30 for Measurement of Health-Related Quality of Life in Patients With Breast Cancer: Prospective Randomized Trial. Journal of Medical Internet Research, 19(9), e322. doi:10.2196/jmir.8210 es_ES
dc.description.references Gresham, G., Hendifar, A. E., Spiegel, B., Neeman, E., Tuli, R., Rimel, B. J., … Shinde, A. M. (2018). Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0032-6 es_ES
dc.description.references BOHANNON, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and Ageing, 26(1), 15-19. doi:10.1093/ageing/26.1.15 es_ES
dc.description.references Pérez-García, V. M., Fitzpatrick, S., Pérez-Romasanta, L. A., Pesic, M., Schucht, P., Arana, E., & Sánchez-Gómez, P. (2016). Applied mathematics and nonlinear sciences in the war on cancer. Applied Mathematics and Nonlinear Sciences, 1(2), 423-436. doi:10.21042/amns.2016.2.00036 es_ES
dc.description.references Shin, W., Song, S., Jung, S.-Y., Lee, E., Kim, Z., Moon, H.-G., … Lee, J. E. (2017). The association between physical activity and health-related quality of life among breast cancer survivors. Health and Quality of Life Outcomes, 15(1). doi:10.1186/s12955-017-0706-9 es_ES
dc.description.references Wearable Fitness Monitors Useful in Cancer Treatment, Study Findswww.sciencedaily.com/releases/2018/05/180501130856.htm es_ES
dc.description.references Bade, B. C., Brooks, M. C., Nietert, S. B., Ulmer, A., Thomas, D. D., Nietert, P. J., … Silvestri, G. A. (2016). Assessing the Correlation Between Physical Activity and Quality of Life in Advanced Lung Cancer. Integrative Cancer Therapies, 17(1), 73-79. doi:10.1177/1534735416684016 es_ES
dc.description.references Fortner, B. V., Stepanski, E. J., Wang, S. C., Kasprowicz, S., & Durrence, H. H. (2002). Sleep and Quality of Life in Breast Cancer Patients. Journal of Pain and Symptom Management, 24(5), 471-480. doi:10.1016/s0885-3924(02)00500-6 es_ES
dc.description.references Mishra, S. I., Scherer, R. W., Snyder, C., Geigle, P., & Gotay, C. (2014). Are Exercise Programs Effective for Improving Health-Related Quality of Life Among Cancer Survivors? A Systematic Review and Meta-Analysis. Oncology Nursing Forum, 41(6), E326-E342. doi:10.1188/14.onf.e326-e342 es_ES
dc.description.references Ratcliff, C. G., Lam, C. Y., Arun, B., Valero, V., & Cohen, L. (2014). Ecological momentary assessment of sleep, symptoms, and mood during chemotherapy for breast cancer. Psycho-Oncology, 23(11), 1220-1228. doi:10.1002/pon.3525 es_ES
dc.description.references Cox, S. M., Lane, A., & Volchenboum, S. L. (2018). Use of Wearable, Mobile, and Sensor Technology in Cancer Clinical Trials. JCO Clinical Cancer Informatics, (2), 1-11. doi:10.1200/cci.17.00147 es_ES
dc.description.references Brown, W., Yen, P.-Y., Rojas, M., & Schnall, R. (2013). Assessment of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating mobile health (mHealth) technology. Journal of Biomedical Informatics, 46(6), 1080-1087. doi:10.1016/j.jbi.2013.08.001 es_ES
dc.description.references Darlow, S., & Wen, K.-Y. (2016). Development testing of mobile health interventions for cancer patient self-management: A review. Health Informatics Journal, 22(3), 633-650. doi:10.1177/1460458215577994 es_ES
dc.description.references Martin Sanchez, F., Gray, K., Bellazzi, R., & Lopez-Campos, G. (2014). Exposome informatics: considerations for the design of future biomedical research information systems. Journal of the American Medical Informatics Association, 21(3), 386-390. doi:10.1136/amiajnl-2013-001772 es_ES
dc.description.references Kim, H. H., Lee, S. Y., Baik, S. Y., & Kim, J. H. (2015). MELLO: Medical lifelog ontology for data terms from self-tracking and lifelog devices. International Journal of Medical Informatics, 84(12), 1099-1110. doi:10.1016/j.ijmedinf.2015.08.005 es_ES
dc.description.references Kessel, K. A., Vogel, M. M., Alles, A., Dobiasch, S., Fischer, H., & Combs, S. E. (2018). Mobile App Delivery of the EORTC QLQ-C30 Questionnaire to Assess Health-Related Quality of Life in Oncological Patients: Usability Study. JMIR mHealth and uHealth, 6(2), e45. doi:10.2196/mhealth.9486 es_ES
dc.description.references Elsbernd, A., Hjerming, M., Visler, C., Hjalgrim, L. L., Niemann, C. U., Boisen, K., & Pappot, H. (2018). Cocreated Smartphone App to Improve the Quality of Life of Adolescents and Young Adults with Cancer (Kræftværket): Protocol for a Quantitative and Qualitative Evaluation. JMIR Research Protocols, 7(5), e10098. doi:10.2196/10098 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem