{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:46:54Z","timestamp":1761767214375,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031345852"},{"type":"electronic","value":"9783031345869"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34586-9_29","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T03:27:04Z","timestamp":1686367624000},"page":"439-449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Linking Data Collected from\u00a0Mobile Phones with\u00a0Symptoms Level in\u00a0Parkinson\u2019s Disease: Data Exploration of\u00a0the\u00a0mPower Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7102-083X","authenticated-orcid":false,"given":"Gent","family":"Ymeri","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9203-1124","authenticated-orcid":false,"given":"Dario","family":"Salvi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4261-281X","authenticated-orcid":false,"given":"Carl Magnus","family":"Olsson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1111\/jnc.13691","volume":"139","author":"S Sveinbjornsdottir","year":"2016","unstructured":"Sveinbjornsdottir, S.: The clinical symptoms of Parkinson\u2019s disease. J. Neurochem. 139, 318\u2013324 (2016)","journal-title":"J. Neurochem."},{"issue":"11","key":"29_CR2","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/S1474-4422(18)30295-3","volume":"17","author":"ER Dorsey","year":"2018","unstructured":"Dorsey, E.R., et al.: Global, regional, and national burden of Parkinson\u2019s disease, 1990\u20132016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 17(11), 939\u2013953 (2018)","journal-title":"Lancet Neurol."},{"issue":"15","key":"29_CR3","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1002\/mds.22340","volume":"23","author":"CG Goetz","year":"2008","unstructured":"Goetz, C.G., et al.: Movement disorder society-sponsored revision of the unified Parkinson\u2019s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. Official J. Mov. Disord. Soc. 23(15), 2129\u20132170 (2008)","journal-title":"Mov. Disord. Official J. Mov. Disord. Soc."},{"issue":"10","key":"29_CR4","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1002\/mds.27790","volume":"34","author":"LJ Evers","year":"2019","unstructured":"Evers, L.J., Krijthe, J.H., Meinders, M.J., Bloem, B.R., Heskes, T.M.: Measuring Parkinson\u2019s disease over time: the real-world within-subject reliability of the MDS-UPDRS. Mov. Disord. 34(10), 1480\u20131487 (2019)","journal-title":"Mov. Disord."},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Linares-Del Rey, M., Vela-Desojo, L., Cano-de La Cuerda, R.: Mobile phone applications in Parkinson\u2019s disease: a systematic review. Neurolog\u00eda (English Edition) 34(1), 38\u201354 (2019)","DOI":"10.1016\/j.nrleng.2018.12.002"},{"issue":"1","key":"29_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.11","volume":"3","author":"BM Bot","year":"2016","unstructured":"Bot, B.M., et al.: The mpower study, parkinson disease mobile data collected using researchkit. Sci. Data 3(1), 1\u20139 (2016)","journal-title":"Sci. Data"},{"issue":"2","key":"29_CR7","doi-asserted-by":"publisher","DOI":"10.2196\/25451","volume":"9","author":"D Su","year":"2021","unstructured":"Su, D., et al.: Simple smartphone-based assessment of gait characteristics in Parkinson disease: validation study. JMIR Mhealth Uhealth 9(2), e25451 (2021)","journal-title":"JMIR Mhealth Uhealth"},{"issue":"11","key":"29_CR8","doi-asserted-by":"publisher","DOI":"10.2196\/21543","volume":"8","author":"E Kuosmanen","year":"2020","unstructured":"Kuosmanen, E., et al.: Smartphone-based monitoring of Parkinson disease: quasi-experimental study to quantify hand tremor severity and medication effectiveness. JMIR Mhealth Uhealth 8(11), e21543 (2020)","journal-title":"JMIR Mhealth Uhealth"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Chaibub Neto, E.L.I.A.S., et al.: Personalized hypothesis tests for detecting medication response in Parkinson disease patients using iPhone sensor data. In: Biocomputing 2016: Proceedings of the Pacific Symposium. World Scientific, 2016, pp. 273\u2013284 (2016)","DOI":"10.1142\/9789814749411_0026"},{"issue":"11","key":"29_CR10","doi-asserted-by":"publisher","first-page":"3236","DOI":"10.3390\/s20113236","volume":"20","author":"A Lauraitis","year":"2020","unstructured":"Lauraitis, A., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., Krilavi\u010dius, T.: A mobile application for smart computer-aided self-administered testing of cognition, speech, and motor impairment. Sensors 20(11), 3236 (2020)","journal-title":"Sensors"},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.imu.2017.05.005","volume":"9","author":"S Aghanavesi","year":"2017","unstructured":"Aghanavesi, S., Nyholm, D., Senek, M., Bergquist, F., Memedi, M.: A smartphone-based system to quantify dexterity in Parkinson\u2019s disease patients. Inform. Med. Unlocked 9, 11\u201317 (2017)","journal-title":"Inform. Med. Unlocked"},{"issue":"1","key":"29_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-021-00414-7","volume":"4","author":"SK Sieberts","year":"2021","unstructured":"Sieberts, S.K., et al.: Crowdsourcing digital health measures to predict Parkinson\u2019s disease severity: the Parkinson\u2019s disease digital biomarker dream challenge. NPJ Digit. Med. 4(1), 1\u201312 (2021)","journal-title":"NPJ Digit. Med."},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"L. Omberg, E., et al.: Remote smartphone monitoring of Parkinson\u2019s disease and individual response to therapy. Nat. Biotechnol. 40(4), 480\u2013487 (2022)","DOI":"10.1038\/s41587-021-00974-9"},{"issue":"4","key":"29_CR14","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1093\/biomet\/87.4.954","volume":"87","author":"I-K Yeo","year":"2000","unstructured":"Yeo, I.-K., Johnson, R.A.: A new family of power transformations to improve normality or symmetry. Biometrika 87(4), 954\u2013959 (2000)","journal-title":"Biometrika"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with python. In: 9th Python in Science Conference (2010)","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Arik, S.\u00d6., Pfister, T.: Tabnet: attentive interpretable tabular learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, pp. 6679\u20136687 (2021)","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"29_CR17","doi-asserted-by":"publisher","unstructured":"Dubitzky, W., Granzow, M., Berrar, D.P.: Fundamentals of Data Mining in Genomics and Proteomics. Springer Science & Business Media, New York (2007). https:\/\/doi.org\/10.1007\/978-0-387-47509-7","DOI":"10.1007\/978-0-387-47509-7"},{"issue":"3","key":"29_CR18","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1136\/eb-2015-102129","volume":"18","author":"R Heale","year":"2015","unstructured":"Heale, R., Twycross, A.: Validity and reliability in quantitative studies. Evid. Based Nurs. 18(3), 66\u201367 (2015)","journal-title":"Evid. Based Nurs."},{"issue":"5","key":"29_CR19","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1002\/mds.26960","volume":"32","author":"K Horv\u00e1th","year":"2017","unstructured":"Horv\u00e1th, K., et al.: Minimal clinically important differences for the experiences of daily living parts of movement disorder society-sponsored unified Parkinson\u2019s disease rating scale. Mov. Disord. 32(5), 789\u2013793 (2017)","journal-title":"Mov. Disord."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Pervasive Computing Technologies for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34586-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T03:35:42Z","timestamp":1686368142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34586-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031345852","9783031345869"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34586-9_29","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pervasive Computing Technologies for Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ph2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pervasivehealth.eai-conferences.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy Plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}