{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T07:14:52Z","timestamp":1760426092487,"version":"3.41.0"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>Wearables are an important source of big data, as they provide real-time high-resolution data logs of health indicators of individuals. Higher-order associations between pairs of variables is common in wearables data. Representing higher-order association curves as piecewise linear segments in a regression model makes them more interpretable. However, existing methods for identifying the change points for segmented modeling either overfit or have low external validity for wearables data containing repeated measures. Therefore, we propose a human-in-the-loop method for segmented modeling of higher-order pairwise associations between variables in wearables data. Our method uses the smooth function estimated by a generalized additive mixed model to allow the analyst to annotate change point estimates for a segmented mixed-effects model, and thereafter employs Brent's constrained optimization procedure to fine-tune the manually provided estimates. We validate our method using three real-world wearables datasets. Our method not only outperforms state-of-the-art modeling methods in terms of prediction performance but also provides more interpretable results. Our study contributes to health data science in terms of developing a new method for interpretable modeling of wearables data. Our analysis uncovers interesting insights on higher-order associations for health researchers.<\/jats:p>","DOI":"10.1145\/3564276","type":"journal-article","created":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T10:33:05Z","timestamp":1664015585000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Human-in-the-Loop Segmented Mixed-Effects Modeling Method for Analyzing Wearables Data"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-6190","authenticated-orcid":false,"given":"Karthik","family":"Srinivasan","sequence":"first","affiliation":[{"name":"School of Business, University of Kansas, Lawrence KS, U.S."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-811X","authenticated-orcid":false,"given":"Faiz","family":"Currim","sequence":"additional","affiliation":[{"name":"Eller College of Management, University of Arizona, Tucson AZ, U.S."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6053-1311","authenticated-orcid":false,"given":"Sudha","family":"Ram","sequence":"additional","affiliation":[{"name":"Eller College of Management, University of Arizona, Tucson AZ, U.S."}]}],"member":"320","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"GlobalNewsWire. 2022. Smart Wearables Market and Wearable Apps Market Global Share Insights 2021. Retrieved January 11 2022 from https:\/\/www.globenewswire.com\/news-release\/2021\/12\/15\/2352316\/0\/en\/Smart-Wearables-Market-and-Wearable-Apps-Market-Global-Share-Insights-2021-Top-Countries-Data-Future-Growth-Developments-Impact-of-Covid-19-on-Industry-Size-Production-Cost-Value-V.html."},{"key":"e_1_3_2_3_2","first-page":"185","article-title":"Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management","volume":"44","author":"Bardhan I.","year":"2022","unstructured":"I. Bardhan, H. Chen, and E. Karahanna. 2022. Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management. MIS Quarterly 44 (March 2020), 185\u2013200.","journal-title":"MIS Quarterly"},{"issue":"2","key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3471571","article-title":"A multi-attention collaborative deep learning approach for blood pressure prediction","volume":"13","author":"He L.","year":"2021","unstructured":"L. He, H. Liu, Y. Yang, and B. Wang. 2021. A multi-attention collaborative deep learning approach for blood pressure prediction. ACM Transactions on Management Information Systems 13, 2 (Oct. 2021), 1\u201320.","journal-title":"ACM Transactions on Management Information Systems"},{"key":"e_1_3_2_5_2","volume-title":"CEUR Workshop Proceedings","volume":"2429","author":"Killian J. A.","year":"2019","unstructured":"J. A. Killian, K. M. Passino, A. Nandi, D. R. Madden, and J. Clapp. 2019. Learning to detect heavy drinking episodes using smartphone accelerometer data. In CEUR Workshop Proceedings. 2429."},{"issue":"2","key":"e_1_3_2_6_2","first-page":"1","article-title":"A deep learning approach for recognizing activity of daily living (ADL) for senior care: Exploiting interaction dependency and temporal patterns","volume":"45","author":"Zhu H.","year":"2021","unstructured":"H. Zhu, S. Samtani, R. A. Brown, and H. Chen. 2021. A deep learning approach for recognizing activity of daily living (ADL) for senior care: Exploiting interaction dependency and temporal patterns. MIS Quarterly 45, 2 (2021), 1\u201369.","journal-title":"MIS Quarterly"},{"issue":"10","key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1136\/oemed-2018-105077","article-title":"Effects of office workstation type on physical activity and stress","volume":"75","author":"Lindberg C. M.","year":"2018","unstructured":"C. M. Lindberg, K. Srinivasan, B. Gilligan, J. Razjouyan, H. Lee, B. Najafi, K. J. Canada, et al. 2018. Effects of office workstation type on physical activity and stress. Occupational and Environmental Medicine 75, 10 (2018), 689\u2013695.","journal-title":"Occupational and Environmental Medicine"},{"issue":"1","key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-018-0074-9","article-title":"Large-scale wearable data reveal digital phenotypes for daily-life stress detection","volume":"1","author":"Smets E.","year":"2018","unstructured":"E. Smets, E. R. Velazquez, G. Schiavone, I. Chakroun, E. D'Hondt, W. de Raedt, J. Cornelis, et al. 2018. Large-scale wearable data reveal digital phenotypes for daily-life stress detection. npj Digital Medicine 1, 1 (Dec. 2018), 1\u201310.","journal-title":"npj Digital Medicine"},{"issue":"1","key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"305","DOI":"10.25300\/MISQ\/2020\/15106","article-title":"A comprehensive analysis of triggers and risk factors for asthma based on machine learning and large heterogeneous data sources","volume":"44","author":"Zhang W.","year":"2020","unstructured":"W. Zhang and S. Ram. 2020. A comprehensive analysis of triggers and risk factors for asthma based on machine learning and large heterogeneous data sources. MIS Quarterly 44, 1 (2020), 305\u2013349.","journal-title":"MIS Quarterly"},{"issue":"4","key":"e_1_3_2_10_2","first-page":"Article 15","article-title":"Smart health and wellbeing","volume":"4","author":"Yang C. C.","year":"2013","unstructured":"C. C. Yang, G. Leroy, and S. Ananiadou. 2013. Smart health and wellbeing. ACM Transactions on Management Information Systems 4, 4 (2013), Article 15, 8 pages.","journal-title":"ACM Transactions on Management Information Systems"},{"issue":"4","key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3462441","article-title":"SymptomID: A framework for rapid symptom identification in pandemics using news reports","volume":"12","author":"Gu K.","year":"2021","unstructured":"K. Gu, S. Vosoughi, and T. Prioleau. 2021. SymptomID: A framework for rapid symptom identification in pandemics using news reports. ACM Transactions on Management Information Systems 12, 4 (Sept. 2021), 1\u201317.","journal-title":"ACM Transactions on Management Information Systems"},{"issue":"10","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"e19542","DOI":"10.2196\/19542","article-title":"Opportunities and challenges surrounding the use of data from wearable sensor devices in health care: Qualitative interview study","volume":"22","author":"Azodo I.","year":"2020","unstructured":"I. Azodo, R. Williams, A. Sheikh, and K. Cresswell. 2020. Opportunities and challenges surrounding the use of data from wearable sensor devices in health care: Qualitative interview study. Journal of Medical Internet Research 22, 10 (Oct. 2020), e19542.","journal-title":"Journal of Medical Internet Research"},{"issue":"5","key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.3390\/s20051379","article-title":"Data analytics and applications of the wearable sensors in healthcare: An overview","volume":"20","author":"Uddin M.","year":"2020","unstructured":"M. Uddin and S. Syed-Abdul. 2020. Data analytics and applications of the wearable sensors in healthcare: An overview. Sensors (Basel) 20, 5 (March 2020), 1379.","journal-title":"Sensors (Basel)"},{"issue":"5","key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1289\/ehp.1205606","article-title":"Individual daytime noise exposure during routine activities and heart rate variability in adults: A repeated measures study","volume":"121","author":"Kraus U.","year":"2013","unstructured":"U. Kraus, A. Schneider, S. Breitner, R. Hampel, R. Ruckerl, M. Pitz, U. Geruschkat, P. Belcredi, K. Radon, and A. Peters. 2013. Individual daytime noise exposure during routine activities and heart rate variability in adults: A repeated measures study. Environmental Health Perspectives 121, 5 (2013), 607\u2013612.","journal-title":"Environmental Health Perspectives"},{"issue":"4","key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1287\/isre.1120.0424","article-title":"Two worlds of trust for potential e-commerce users: Humans as cognitive misers","volume":"23","author":"Liu B. Q.","year":"2012","unstructured":"B. Q. Liu and D. L. Goodhue. 2012. Two worlds of trust for potential e-commerce users: Humans as cognitive misers. Information Systems Research 23, 4 (2012), 1246\u20131262.","journal-title":"Information Systems Research"},{"issue":"2","key":"e_1_3_2_16_2","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1287\/isre.1080.0231","article-title":"Predicting web page status","volume":"21","author":"Pant G.","year":"2010","unstructured":"G. Pant and P. Srinivasan. 2010. Predicting web page status. Information Systems Research 21, 2 (June 2010), 345\u2013364.","journal-title":"Information Systems Research"},{"key":"e_1_3_2_17_2","volume-title":"Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence","author":"Singer J. D.","year":"2009","unstructured":"J. D. Singer and J. B. Willett. 2009. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press, New York, NY."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1177\/1471082X13504721","article-title":"Segmented mixed models with random changepoints: A maximum likelihood approach with application to treatment for depression study","volume":"14","author":"Muggeo V. M.","year":"2014","unstructured":"V. M. Muggeo, D. C. Atkins, R. J. Gallop, and S. Dimidjian. 2014. Segmented mixed models with random changepoints: A maximum likelihood approach with application to treatment for depression study. Stat Modelling 14 (2014), 293\u2013313.","journal-title":"Stat Modelling"},{"issue":"3","key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"3450370","DOI":"10.1007\/BF02294361","article-title":"Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions","volume":"52","author":"Bozdogan H.","year":"1987","unstructured":"H. Bozdogan. 1987. Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika 52, 3 (1987), 3450370.","journal-title":"Psychometrika"},{"issue":"4","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.2307\/41703503","article-title":"Business intelligence and analytics: From big data to big impact","volume":"36","author":"Chen H.","year":"2012","unstructured":"H. Chen, R. H. L. Chiang, and V. C. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS Quarterly 36, 4 (2012), 1165\u20131188.","journal-title":"MIS Quarterly"},{"issue":"3","key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.artmed.2012.09.003","article-title":"Smart wearable systems: Current status and future challenges","volume":"56","author":"Chan M.","year":"2012","unstructured":"M. Chan, D. Est\u00e8ve, J.-Y. Fourniols, C. Escriba, and E. Campo. 2012. Smart wearable systems: Current status and future challenges. Artificial Intelligence in Medicine 56, 3 (2012), 137\u2013156.","journal-title":"Artificial Intelligence in Medicine"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"17472","DOI":"10.3390\/s131217472","article-title":"Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges","volume":"13","author":"Banaee H.","year":"2013","unstructured":"H. Banaee, M. U. Ahmed, and A. Loutfi. 2013. Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges. Sensors 13 (2013), 17472\u201317500.","journal-title":"Sensors"},{"issue":"1","key":"e_1_3_2_23_2","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3390\/s17010130","article-title":"Wearable sensors for remote health monitoring","volume":"17","author":"Majumder S.","year":"2017","unstructured":"S. Majumder, T. Mondal, and M. Deen. 2017. Wearable sensors for remote health monitoring. Sensors 17, 1 (2017), 130.","journal-title":"Sensors"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/VLSIT.2012.6242435","volume-title":"Proceedings of the 2012 Symposium on VLSI Technology (VLSIT\u201912)","author":"Yamada I.","year":"2012","unstructured":"I. Yamada and G. Lopez. 2012. Wearable sensing systems for healthcare monitoring. In Proceedings of the 2012 Symposium on VLSI Technology (VLSIT\u201912), 5\u201310."},{"issue":"3","key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/JSEN.2010.2091719","article-title":"A Zigbee-based wearable physiological parameters monitoring system","volume":"12","author":"Malhi K.","year":"2012","unstructured":"K. Malhi, S. C. Mukhopadhyay, J. Schnepper, M. Haefke, and H. Ewald. 2012. A Zigbee-based wearable physiological parameters monitoring system. IEEE Sensors Journal 12, 3 (2012), 423\u2013430.","journal-title":"IEEE Sensors Journal"},{"issue":"1","key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JBHI.2016.2633287","article-title":"A deep learning approach to on-node sensor data analytics for mobile or wearable devices","volume":"21","author":"Ravi D.","year":"2017","unstructured":"D. Ravi, C. Wong, B. Lo, and G.-Z. Yang. 2017. A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE Journal of Biomedical and Health Informatics 21, 1 (2017), 56\u201364.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"6","key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"210","DOI":"10.2196\/jmir.9410","article-title":"Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: Observational study","volume":"20","author":"Sano A.","year":"2018","unstructured":"A. Sano, S. Taylor, A. W. McHill, A. J. K. Phillips, L. K. Barger, E. Klerman, and R. Picard. 2018. Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: Observational study. Journal of Medical Internet Research 20, 6 (2018), 210\u2013216.","journal-title":"Journal of Medical Internet Research"},{"issue":"10","key":"e_1_3_2_28_2","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1089\/tmj.2014.0176","article-title":"Wearable sensor\/device (Fitbit One) and SMS text-messaging prompts to increase physical activity in overweight and obese adults: A randomized controlled trial","volume":"21","author":"Wang J. B.","year":"2015","unstructured":"J. B. Wang, L. A. Cadmus-Bertram, L. Natarajan, M. M. White, H. Madanat, J. F. Nichols, G. X. Ayala, and J. P. Pierce. 2015. Wearable sensor\/device (Fitbit One) and SMS text-messaging prompts to increase physical activity in overweight and obese adults: A randomized controlled trial. Telemedicine Journal and e-Health 21, 10 (Oct. 2015), 782\u2013792.","journal-title":"Telemedicine Journal and e-Health"},{"issue":"3","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1214\/10-STS330","article-title":"To explain or to predict?","volume":"25","author":"Shmueli G.","year":"2010","unstructured":"G. Shmueli. 2010. To explain or to predict? Statistical Science 25, 3 (2010), 289\u2013310.","journal-title":"Statistical Science"},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.jbi.2018.07.006","article-title":"A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care","volume":"84","author":"Zhu H.","year":"2018","unstructured":"H. Zhu, H. Chen, and R. Brown. 2018. A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care. Journal of Biomedical Informatics 84 (Aug. 2018), 148\u2013158.","journal-title":"Journal of Biomedical Informatics"},{"key":"e_1_3_2_31_2","first-page":"93","article-title":"Using reality mining to improve public health and medicine","volume":"149","author":"Pentland A.","year":"2009","unstructured":"A. Pentland, D. Lazer, D. Brewer, and T. Heibeck. 2009. Using reality mining to improve public health and medicine. Studies in Health Technology and Informatics 149 (2009), 93\u2013102.","journal-title":"Studies in Health Technology and Informatics"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/978-1-4419-7384-9_8","volume-title":"Wearable Monitoring Systems","author":"Guill\u00e9n S.","year":"2011","unstructured":"S. Guill\u00e9n, M. T. Arredondo, and E. Castellano. 2011. A survey of commercial wearable systems for sport application. In Wearable Monitoring Systems. Springer, 165\u2013178."},{"issue":"3","key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical modeling: The two cultures","volume":"16","author":"Breiman L.","year":"2001","unstructured":"L. Breiman. 2001. Statistical modeling: The two cultures. Statistical Science 16, 3 (2001), 199\u2013231.","journal-title":"Statistical Science"},{"issue":"8","key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1016\/j.neubiorev.2013.07.004","article-title":"Autonomic nervous system activity and workplace stressors\u2014A systematic review","volume":"37","author":"Jarczok M. N.","year":"2013","unstructured":"M. N. Jarczok, M. Jarczok, D. Mauss, J. Koenig, J. Li, R. M. Herr, and J. F. Thayer. 2013. Autonomic nervous system activity and workplace stressors\u2014A systematic review. Neuroscience and Biobehavioral Reviews 37, 8 (2013), 1810\u20131823.","journal-title":"Neuroscience and Biobehavioral Reviews"},{"issue":"1","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"e2001402","DOI":"10.1371\/journal.pbio.2001402","article-title":"Digital health: Tracking physiomes and activity using wearable biosensors reveals useful health-related information","volume":"15","author":"Li X.","year":"2017","unstructured":"X. Li, J. Dunn, D. Salins, G. Zhou, W. Zhou, S. M. Schussler-Fiorenza Rose, D. Perelman, et al. 2017. Digital health: Tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biology 15, 1 (2017), e2001402.","journal-title":"PLoS Biology"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.buildenv.2016.05.011","article-title":"Environmental perceptions and health before and after relocation to a green building","volume":"104","author":"MacNaughton P.","year":"2016","unstructured":"P. MacNaughton, J. Spengler, J. Vallarino, S. Santanam, U. Satish, and J. Allen. 2016. Environmental perceptions and health before and after relocation to a green building. Building and Environment 104 (2016), 138\u2013144.","journal-title":"Building and Environment"},{"issue":"4","key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1097\/HJR.0b013e328336923a","article-title":"Effects of the physical work environment on physiological measures of stress","volume":"17","author":"Thayer J. F.","year":"2010","unstructured":"J. F. Thayer, B. Verkuil, J. F. Brosschot, K. Kampschroer, A. West, C. Sterling, I. C. Christie, et al. 2010. Effects of the physical work environment on physiological measures of stress. European Journal of Cardiovascular Prevention and Rehabilitation 17, 4 (Aug. 2010), 431\u2013439.","journal-title":"European Journal of Cardiovascular Prevention and Rehabilitation"},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s12995-015-0081-6","article-title":"Subjective stress, objective heart rate variability-based stress, and recovery on workdays among overweight and psychologically distressed individuals: A cross-sectional study","volume":"10","author":"F\u00f6hr T.","year":"2015","unstructured":"T. F\u00f6hr, A. Tolvanen, R. Myllymaki, E. Jarvela-Reijonen, S. Rantala, R. Korpela, K. Peukhuri, et al. 2015. Subjective stress, objective heart rate variability-based stress, and recovery on workdays among overweight and psychologically distressed individuals: A cross-sectional study. Journal of Occupational Medicine and Toxicology 10 (2015), 39.","journal-title":"Journal of Occupational Medicine and Toxicology"},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.cmpb.2017.06.018","article-title":"Heart rate variability metrics for fine-grained stress level assessment","volume":"148","author":"Pereira T.","year":"2017","unstructured":"T. Pereira, P. R. Almeida, J. P. S. Cunha, and A. Aguiar. 2017. Heart rate variability metrics for fine-grained stress level assessment. Computer Methods and Programs in Biomedicine 148 (Sept. 2017), 71\u201380.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511790942","volume-title":"Data Analysis Using Regression and Multilevel\/Hierarchical Models","author":"Gelman A.","year":"2006","unstructured":"A. Gelman and J. Hill. 2006. Data Analysis Using Regression and Multilevel\/Hierarchical Models. Cambridge University Press, New York, NY."},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ijmedinf.2019.05.028","article-title":"A wearable approach for intraoperative physiological stress monitoring of multiple cooperative surgeons","volume":"129","author":"Pimentel G.","year":"2019","unstructured":"G. Pimentel, S. Rodrigues, P. A. Silva, A. Vilarinho, R. Vaz, and J. P. Silva Cunha. 2019. A wearable approach for intraoperative physiological stress monitoring of multiple cooperative surgeons. International Journal of Medical Informatics 129 (2019), 60\u201368.","journal-title":"International Journal of Medical Informatics"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3389\/fnhum.2017.00027","article-title":"The association between work-related rumination and heart rate variability: A field study","volume":"11","author":"Cropley M.","year":"2017","unstructured":"M. Cropley, D. Plans, D. Morelli, S. Sutterlin, I. Inceoglu, G. Thomas, and C. Chu. 2017. The association between work-related rumination and heart rate variability: A field study. Frontiers in Human Neuroscience 11 (Jan. 2017), 27.","journal-title":"Frontiers in Human Neuroscience"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.envint.2019.05.026","article-title":"Evaluation of wearable sensors for physiologic monitoring of individually experienced temperatures in outdoor workers in southeastern U.S","volume":"129","author":"Runkle J. D.","year":"2019","unstructured":"J. D. Runkle, C. Cui, C. Fuhrmann, S. Stevens, J. del Pinal, and M. M. Sugg. 2019. Evaluation of wearable sensors for physiologic monitoring of individually experienced temperatures in outdoor workers in southeastern U.S. Environment International 129 (2019), 229\u2013238.","journal-title":"Environment International"},{"issue":"8","key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1002\/sim.1991","article-title":"Simple fitting of subject-specific curves for longitudinal data","volume":"24","author":"Durban M.","year":"2005","unstructured":"M. Durban, J. Harezlak, M. P. Wand, and R. J. Carroll. 2005. Simple fitting of subject-specific curves for longitudinal data. Statistics in Medicine 24, 8 (2005), 1153\u20131167.","journal-title":"Statistics in Medicine"},{"issue":"4","key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"821","DOI":"10.2307\/2951764","article-title":"Tests for parameter instability and structural change with unknown change point","volume":"61","author":"Andrews D. W. K.","year":"1993","unstructured":"D. W. K. Andrews. 1993. Tests for parameter instability and structural change with unknown change point. Econometrica 61, 4 (1993), 821\u2013856.","journal-title":"Econometrica"},{"issue":"2","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1177\/1536867X1501500208","article-title":"Conducting interrupted time-series analysis for single- and multiple-group comparisons","volume":"15","author":"Linden A.","year":"2015","unstructured":"A. Linden. 2015. Conducting interrupted time-series analysis for single- and multiple-group comparisons. Stata Journal 15, 2 (2015), 480\u2013500.","journal-title":"Stata Journal"},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","DOI":"10.2737\/RMRS-GTR-189","author":"Ryan S. E.","year":"2007","unstructured":"S. E. Ryan and L. S. Porth. 2007. A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data. General Technical Report RMRS-GTR, no. 189. USDA Forest Service.","journal-title":"A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data"},{"issue":"19","key":"e_1_3_2_48_2","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1002\/sim.1545","article-title":"Estimating regression models with unknown break-points","volume":"22","author":"Muggeo V. M. R.","year":"2003","unstructured":"V. M. R. Muggeo. 2003. Estimating regression models with unknown break-points. Statistics in Medicine 22, 19 (2003), 3055\u20133071.","journal-title":"Statistics in Medicine"},{"issue":"2","key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1198\/1085711031580","article-title":"Using segmented regression models to fit soil nutrient and soybean grain yield changes due to liming","volume":"8","author":"Shuai X.","year":"2003","unstructured":"X. Shuai, Z. Zhou, and R. S. Yost. 2003. Using segmented regression models to fit soil nutrient and soybean grain yield changes due to liming. Journal of Agricultural, Biological, and Environmental Statistics 8, 2 (2003), 240\u2013252.","journal-title":"Journal of Agricultural, Biological, and Environmental Statistics"},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the Workshop on Human-in-the-Loop Data Analytics (HILDA\u201918)","author":"Doan A.","year":"2018","unstructured":"A. Doan. 2018. Human-in-the-loop data analysis: A personal perspective. In Proceedings of the Workshop on Human-in-the-Loop Data Analytics (HILDA\u201918)."},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.future.2022.05.014","article-title":"A survey of human-in-the-loop for machine learning","volume":"135","author":"Wu X.","year":"2022","unstructured":"X. Wu, L. Xiao, Y. Sun, J. Zhang, T. Ma, and L. He. 2022. A survey of human-in-the-loop for machine learning. Future Generation Computer Systems 135 (Oct. 2022), 364\u2013381.","journal-title":"Future Generation Computer Systems"},{"key":"e_1_3_2_52_2","first-page":"614","volume-title":"Proceedings of the IUI International Conference on Intelligent User Interfaces (IUI\u201919)","author":"Gil Y.","year":"2019","unstructured":"Y. Gil, J. Honaker, S. Gupta, Y. Ma, V. D'Orazio, D. Garijo, S. Gadewar, O. Yang, and N. Jahanshad. 2019. Towards human-guided machine learning. In Proceedings of the IUI International Conference on Intelligent User Interfaces (IUI\u201919), 614\u2013624."},{"key":"e_1_3_2_53_2","article-title":"Accelerating human-in-the-loop machine learning: challenges and opportunities.","author":"Xin D. D.","year":"2018","unstructured":"D. D. Xin, L. L. Ma, J. J. Liu, S. S. Macke, S. S. Song, and A. A. Parameswaran. 2018. Accelerating human-in-the-loop machine learning: challenges and opportunities. In Proceedings of the 2nd Workshop on Data Management for End-to-End Machine Learning (DEEM\u201918).","journal-title":"Proceedings of the 2nd Workshop on Data Management for End-to-End Machine Learning (DEEM\u201918)"},{"issue":"2","key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s40708-016-0042-6","article-title":"Interactive machine learning for health informatics: When do we need the human-in-the-loop?","volume":"3","author":"Holzinger A.","year":"2016","unstructured":"A. Holzinger. 2016. Interactive machine learning for health informatics: When do we need the human-in-the-loop? Brain Informatics 3, 2 (2016), 119\u2013131.","journal-title":"Brain Informatics"},{"key":"e_1_3_2_55_2","first-page":"2156","article-title":"PACE: Learning effective task decomposition for human-in-the-loop healthcare delivery","author":"Zheng K.","year":"2021","unstructured":"K. Zheng, G. Chen, M. Herschel, K. Y. Ngiam, B. C. Ooi, and J. Gao. 2021. PACE: Learning effective task decomposition for human-in-the-loop healthcare delivery. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 2156\u20132168.","journal-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","first-page":"114476","DOI":"10.1016\/j.eswa.2020.114476","article-title":"Optimal sepsis patient treatment using human-in-the-loop artificial intelligence","volume":"169","author":"Gupta A.","year":"2021","unstructured":"A. Gupta, M. T. Lash, and S. K. Nachimuthu. 2021. Optimal sepsis patient treatment using human-in-the-loop artificial intelligence. Expert Systems with Applications 169 (May 2021), 114476.","journal-title":"Expert Systems with Applications"},{"issue":"467","key":"e_1_3_2_57_2","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1198\/016214504000000980","article-title":"Stable and efficient multiple smoothing parameter estimation for generalized additive models","volume":"99","author":"Wood S. N.","year":"2004","unstructured":"S. N. Wood. 2004. Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association 99, 467 (2004), 673\u2013686.","journal-title":"Journal of the American Statistical Association"},{"key":"e_1_3_2_58_2","volume-title":"Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.","author":"Faraway J. J.","year":"2016","unstructured":"J. J. Faraway. 2016. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC Press, Boca Raton, FL."},{"key":"e_1_3_2_59_2","volume-title":"Algorithms for Minimization without Derivatives","author":"Brent R. P.","year":"2013","unstructured":"R. P. Brent. 2013. Algorithms for Minimization without Derivatives (2nd ed.). Dover Publications, Eaglewood Cliffs, NJ.","edition":"2"},{"key":"e_1_3_2_60_2","volume-title":"Practical Methods of Optimization","author":"Fletcher R.","year":"2013","unstructured":"R. Fletcher. 2013. Practical Methods of Optimization (4th ed.), vol. 53. John Wiley & Sons, West Sussex, England.","edition":"4"},{"key":"e_1_3_2_61_2","unstructured":"U.S. General Services Administration. 2022. Wellbuilt for Wellbeing. Retrieved January 12 2022 from https:\/\/www.gsa.gov\/governmentwide-initiatives\/federal-highperformance-green-buildings\/resource-library\/health\/wellbuilt-for-wellbeing."},{"issue":"258","key":"e_1_3_2_62_2","first-page":"1","article-title":"An overview of heart rate variability metrics and norms","volume":"5","author":"Shaffer F.","year":"2017","unstructured":"F. Shaffer and J. P. Ginsberg. 2017. An overview of heart rate variability metrics and norms. Frontiers in Public Health 5, 258 (Sept. 2017), 1\u201317.","journal-title":"Frontiers in Public Health"},{"issue":"21","key":"e_1_3_2_63_2","doi-asserted-by":"crossref","first-page":"2100","DOI":"10.1161\/CIRCULATIONAHA.113.005361","article-title":"Physical activity and heart rate variability in older adults","volume":"129","author":"Soares-Miranda L.","year":"2014","unstructured":"L. Soares-Miranda, J. Sattelmair, P. Chaves, Glen Duncan, D. S. Siscovick, P. K. Stein, and D. Mozaffarian. 2014. Physical activity and heart rate variability in older adults. Circulation 129, 21 (May 2014), 2100\u20132110.","journal-title":"Circulation"},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3079452.3079503","volume-title":"Proceedings of the 7th International Conference on Digital Health (DH\u201917)","author":"Srinivasan K.","year":"2017","unstructured":"K. Srinivasan, F. Currim, S. Ram, M. R. Mehl, C. Lindberg, E. Sternberg, P. Skeath, et al. 2017. A regularization approach for identifying cumulative lagged effects in smart health applications. In Proceedings of the 7th International Conference on Digital Health (DH\u201917), 99\u2013103."},{"issue":"3","key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1213\/ANE.0b013e318241f7c0","article-title":"University of Queensland vital signs dataset: Development of an accessible repository of anesthesia patient monitoring data for research","volume":"114","author":"Liu D.","year":"2012","unstructured":"D. Liu, M. G\u00f6rges, and S. A. Jenkins. 2012. University of Queensland vital signs dataset: Development of an accessible repository of anesthesia patient monitoring data for research. Anesthesia and Analgesia 114, 3 (2012), 584\u2013589.","journal-title":"Anesthesia and Analgesia"},{"issue":"1","key":"e_1_3_2_66_2","first-page":"1","article-title":"ST-segment elevation myocardial infarction","volume":"5","author":"Vogel B.","year":"2019","unstructured":"B. Vogel, B. E. Claessen, S. V. Arnold, D. Chan, D. J. Cohen, E. Giannitsis, C. Michael Gibson, et al. 2019. ST-segment elevation myocardial infarction. Nature Reviews: Disease Primers 5, 1 (June 2019), 1\u201320.","journal-title":"Nature Reviews: Disease Primers"},{"issue":"4","key":"e_1_3_2_67_2","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.resuscitation.2012.12.016","article-title":"The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death","volume":"84","author":"Smith G. B.","year":"2013","unstructured":"G. B. Smith, D. R. Prytherch, P. Meredith, P. E. Schmidt, and P. I. Featherstone. 2013. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 84, 4 (April 2013), 465\u2013470.","journal-title":"Resuscitation"},{"key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"108304","DOI":"10.1016\/j.drugalcdep.2020.108304","article-title":"Validation of transdermal alcohol concentration data collected using wearable alcohol monitors: A systematic review and meta-analysis","volume":"216","author":"Kianersi S.","year":"2020","unstructured":"S. Kianersi, M. Luetke, J. Agley, R. Gassman, C. Ludema, and M. Rosenberg. 2020. Validation of transdermal alcohol concentration data collected using wearable alcohol monitors: A systematic review and meta-analysis. Drug and Alcohol Dependence 216 (Nov. 2020), 108304.","journal-title":"Drug and Alcohol Dependence"},{"issue":"1","key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12877-020-01572-1","article-title":"Sensor-based characterization of daily walking: A new paradigm in pre-frailty\/frailty assessment","volume":"20","author":"Pradeep Kumar D.","year":"2020","unstructured":"D. Pradeep Kumar, N. Toosizadeh, J. Mohler, H. Ehsani, C. Mannier, and K. Laksari. 2020. Sensor-based characterization of daily walking: A new paradigm in pre-frailty\/frailty assessment. BMC Geriatrics 20, 1 (May 2020), 1\u201311.","journal-title":"BMC Geriatrics"},{"key":"e_1_3_2_70_2","first-page":"20","article-title":"Segmented: An R package to fit regression models with broken-line relationships","volume":"8","author":"Muggeo V. M. R.","year":"2008","unstructured":"V. M. R. Muggeo. 2008. Segmented: An R package to fit regression models with broken-line relationships. R News 8 (May 2008), 20\u201325.","journal-title":"R News"},{"key":"e_1_3_2_71_2","first-page":"iii","article-title":"Editor's Comments: Diversity of design science research","volume":"41","author":"Rai A.","year":"2017","unstructured":"A. Rai. 2017. Editor's Comments: Diversity of design science research. MIS Quarterly 41 (2017), iii\u2013xviii.","journal-title":"MIS Quarterly"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564276","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3564276","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:10Z","timestamp":1750183750000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564276"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,25]]},"references-count":70,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6,30]]}},"alternative-id":["10.1145\/3564276"],"URL":"https:\/\/doi.org\/10.1145\/3564276","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"type":"print","value":"2158-656X"},{"type":"electronic","value":"2158-6578"}],"subject":[],"published":{"date-parts":[[2023,1,25]]},"assertion":[{"value":"2022-01-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-09-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}