{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T13:02:50Z","timestamp":1769346170760,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether these devices can play a role in preventive care. Gaining insights from the vast amount of smartwatch data is a challenge for HCPs, thus tools are needed to support HCPs when integrating personalized health monitoring devices with EMR. This study aimed to develop and evaluate an application prototype, co-designed with HCPs and employing design science research methodology and diffusion of innovation frameworks to identify the potential for clinical integration. A machine learning algorithm was developed to detect possible health anomalies in smartwatch data, and these were presented visually to HCPs in a web-based platform. HCPs completed a usability questionnaire to evaluate the prototype, and over 60% of HCPs scored positively on usability. This preliminary study tested the proposed research to solve the practical challenges of HCP in interpreting smartwatch data before fully integrating smartwatches into the EMR. The findings provide design directions for future applications that use smartwatch data to improve clinical decision-making and reduce HCP workloads.<\/jats:p>","DOI":"10.3390\/fi15030111","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T05:36:01Z","timestamp":1679031361000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2247-7462","authenticated-orcid":false,"given":"Ruhi Kiran","family":"Bajaj","sequence":"first","affiliation":[{"name":"Department of Information Systems and Operations Management (ISOM), The University of Auckland, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8472-4940","authenticated-orcid":false,"given":"Rebecca Mary","family":"Meiring","sequence":"additional","affiliation":[{"name":"Department of Exercise Sciences, The University of Auckland, Auckland 1023, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando","family":"Beltran","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Operations Management (ISOM), The University of Auckland, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1038\/s41746-020-0236-4","article-title":"Secondary care provider attitudes towards patient generated health data from smartwatches","volume":"3","author":"Alpert","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jat, A.S., and Gr\u00f8nli, T.-M. (2022, January 27\u201330). Smart Watch for Smart Health Monitoring: A Literature Review. Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, Maspalomas, Spain.","DOI":"10.1007\/978-3-031-07704-3_21"},{"key":"ref_3","unstructured":"Philipp, R.S., Barbara, P., Gensichen, J., and Krcmar, H. (2022, January 5\u20139). Insights on Patient-Generated Health Data in Healthcare: A Literature Review. Proceedings of the Pacific Asia Conference on Information Systems (PACIS 2022), Taipei, Taiwan. Available online: https:\/\/aisel.aisnet.org\/pacis2022\/26."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e23896","DOI":"10.2196\/23896","article-title":"General Practitioners\u2019 Perceptions of the Use of Wearable Electronic Health Monitoring Devices: Qualitative Analysis of Risks and Benefits","volume":"9","author":"Volpato","year":"2021","journal-title":"JMIR mHealth uHealth"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Al-Maroof, R.S., Alhumaid, K., Alhamad, A.Q., Aburayya, A., and Salloum, S. (2021). User Acceptance of Smart Watch for Medical Purposes: An Empirical Study. Future Internet, 13.","DOI":"10.3390\/fi13050127"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bogu, G.K., and Snyder, M.P. (2021). Deep learning-based detection of COVID-19 using wearables data. MedRxiv.","DOI":"10.1101\/2021.01.08.21249474"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1038\/s41551-020-00640-6","article-title":"Pre-symptomatic detection of COVID-19 from smartwatch data","volume":"4","author":"Mishra","year":"2020","journal-title":"Nat. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1155\/2020\/6152041","article-title":"Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19","volume":"2020","author":"Zhu","year":"2020","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e9929","DOI":"10.2196\/mhealth.9929","article-title":"Health Care Provider Perceptions of Consumer-Grade Devices and Apps for Tracking Health: A Pilot Study","volume":"7","author":"Holtz","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Loos, J.R., and Davidson, E.J. (2016, January 5\u20138). Wearable Health Monitors and Physician-Patient Communication: The Physician\u2019s Perspective. Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA.","DOI":"10.1109\/HICSS.2016.422"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Frink, T.M., Gyllinsky, J.V., and Mankodiya, K. (2017, January 3\u20135). Visualization of multidimensional clinical data from wearables on the web and on apps. Proceedings of the 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA.","DOI":"10.1109\/URTC.2017.8284217"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Reddy, N.C.N., Ramesh, A., Rajasekaran, R., and Masih, J. (2020, January 20\u201321). Ritchie\u2019s Smart Watch Data Analytics and Visualization. Proceedings of the International Conference on Image Processing and Capsule Networks, Bangkok, Thailand.","DOI":"10.1007\/978-3-030-51859-2_70"},{"key":"ref_13","unstructured":"Singhal, S., Kayyali, B., Levin, R., and Greenberg, Z. (2020). The Next Wave of Health Care Innovation: The Evolution of Ecosystems, McKinsey & Company."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sunny, J.S., Patro, C.P.K., Karnani, K., Pingle, S.C., Lin, F., Anekoji, M., Jones, L.D., Kesari, S., and Ashili, S. (2022). Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects. Sensors, 22.","DOI":"10.3390\/s22030756"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e12861","DOI":"10.2196\/12861","article-title":"Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions","volume":"7","author":"Chuang","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100379","DOI":"10.1016\/j.ajpc.2022.100379","article-title":"Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology","volume":"12","author":"Javaid","year":"2022","journal-title":"Am. J. Prev. Cardiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"175","DOI":"10.17705\/1thci.00147","article-title":"Co-design in mHealth Systems Development: Insights From a Systematic Literature Review","volume":"13","author":"Noorbergen","year":"2021","journal-title":"AIS Trans. Hum.-Comput. Interact."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/15710880701875068","article-title":"Co-creation and the new landscapes of design","volume":"4","author":"Sanders","year":"2008","journal-title":"Codesign"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"S70","DOI":"10.1016\/j.amepre.2012.09.038","article-title":"Designing for Diffusion of a Biomedical Intervention","volume":"44","author":"Dearing","year":"2013","journal-title":"Am. J. Prev. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102292","DOI":"10.1016\/j.ijinfomgt.2020.102292","article-title":"Continued use of wearable fitness technology: A value co-creation perspective","volume":"57","author":"Windasari","year":"2020","journal-title":"Int. J. Inf. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41746-020-00320-4","article-title":"Multi-task deep learning for cardiac rhythm detection in wearable devices","volume":"3","author":"Ashley","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1001\/jamacardio.2018.0136","article-title":"Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch","volume":"3","author":"Tison","year":"2018","journal-title":"JAMA Cardiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e260","DOI":"10.2196\/jmir.5094","article-title":"Bringing Health and Fitness Data Together for Connected Health Care: Mobile Apps as Enablers of Interoperability","volume":"17","author":"Gay","year":"2015","journal-title":"J. Med. Internet Res."},{"key":"ref_25","first-page":"112","article-title":"The Integration of Patient Health Record Generated from Wearable and Internet of Things Devices into Health Information Exchanges","volume":"15","author":"Hill","year":"2021","journal-title":"Int. J. Health Med. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jbi.2018.11.003","article-title":"A smartwatch-based framework for real-time and online assessment and mobility monitoring","volume":"89","author":"Kheirkhahan","year":"2018","journal-title":"J. Biomed. Informatics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/07421222.1990.11517898","article-title":"Systems Development in Information Systems Research","volume":"7","author":"Nunamaker","year":"1990","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_28","unstructured":"Rogers, E.M. (2003). Diffusion of Innovations, Simon and Schuster. [5th ed.]."},{"key":"ref_29","first-page":"S55","article-title":"Diffusion of innovation theory for clinical change","volume":"180","year":"2004","journal-title":"Med. J. Aust."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"75","DOI":"10.2307\/25148625","article-title":"Design Science in Information Systems Research","volume":"28","author":"Hevner","year":"2004","journal-title":"Manag. Inf. Syst. Q."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nilmini Wickramasinghe, J.L.S. (2018). Theories to Inform Superior Health Informatics Research and Practice, Springer.","DOI":"10.1007\/978-3-319-72287-0"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2753\/MIS0742-1222240302","article-title":"A Design Science Research Methodology for Information Systems Research","volume":"24","author":"Peffers","year":"2007","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1177\/1049731509335569","article-title":"Applying Diffusion of Innovation Theory to Intervention Development","volume":"19","author":"Dearing","year":"2009","journal-title":"Res. Soc. Work. Pract."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1177\/0164025994016001002","article-title":"Portraying the New: Communication Between University Innovators and Potential Users","volume":"16","author":"Dearing","year":"1994","journal-title":"Sci. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1377\/hlthaff.2017.1104","article-title":"Diffusion of Innovations Theory, Principles, and Practice","volume":"37","author":"Dearing","year":"2018","journal-title":"Health Aff."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/15710882.2014.888183","article-title":"Probes, toolkits and prototypes: Three approaches to making in codesigning","volume":"10","author":"Sanders","year":"2014","journal-title":"Codesign"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Thambawita, V., Hicks, S.A., and Borgli, H. (2020, January 8\u201311). PMData: A sports logging dataset. Proceedings of the 11th ACM Multimedia Systems Conference, Istanbul, Turkey.","DOI":"10.1145\/3339825.3394926"},{"key":"ref_38","unstructured":"(2023, February 19). Dataset PMData: A Sports Logging Dataset. Available online: https:\/\/datasets.simula.no\/pmdata\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e11500","DOI":"10.2196\/11500","article-title":"The mHealth App Usability Questionnaire (MAUQ): Development and Validation Study","volume":"7","author":"Zhou","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Goldstein, M., and Uchida, S. (2016). A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152173"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ahj.2018.09.002","article-title":"Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study","volume":"207","author":"Turakhia","year":"2018","journal-title":"Am. Heart J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e34483","DOI":"10.2196\/34483","article-title":"Reimagining Connected Care in the Era of Digital Medicine","volume":"10","author":"Mann","year":"2022","journal-title":"JMIR mHealth uHealth"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/TNSRE.2022.3226860","article-title":"Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG","volume":"31","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8925","DOI":"10.1016\/j.jfranklin.2020.04.033","article-title":"Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines","volume":"357","author":"Zhang","year":"2020","journal-title":"J. Frankl. Inst."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/3\/111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:57:33Z","timestamp":1760122653000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/3\/111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["fi15030111"],"URL":"https:\/\/doi.org\/10.3390\/fi15030111","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,17]]}}}