Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 Jul 2024
30 Jul 2024
Historique:
received:
27
12
2023
accepted:
15
07
2024
medline:
31
7
2024
pubmed:
31
7
2024
entrez:
30
7
2024
Statut:
epublish
Résumé
Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.
Identifiants
pubmed: 39079945
doi: 10.1038/s41598-024-67767-3
pii: 10.1038/s41598-024-67767-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17545Subventions
Organisme : Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
ID : 1S56322N
Informations de copyright
© 2024. The Author(s).
Références
Heikenfeld, J. et al. Wearable sensors: Modalities, challenges, and prospects. Lab. Chip 18, 217–248 (2018).
pubmed: 29182185
pmcid: 5771841
doi: 10.1039/C7LC00914C
Baig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F. & Lindén, M. A systematic review of wearable patient monitoring systems: Current challenges and opportunities for clinical adoption. J. Med. Syst. 41, 115 (2017).
pubmed: 28631139
doi: 10.1007/s10916-017-0760-1
Taylor, M. L., Thomas, E. E., Snoswell, C. L., Smith, A. C. & Caffery, L. J. Does remote patient monitoring reduce acute care use? A systematic review. BMJ Open 11, e040232 (2021).
pubmed: 33653740
pmcid: 7929874
doi: 10.1136/bmjopen-2020-040232
Klonoff, D. C. Continuous glucose monitoring: Roadmap for 21st century diabetes therapy. Diabetes Care 28, 1231–1239 (2005).
pubmed: 15855600
doi: 10.2337/diacare.28.5.1231
Bayoumy, K. et al. Smart wearable devices in cardiovascular care: Where we are and how to move forward. Nat. Rev. Cardiol. https://doi.org/10.1038/s41569-021-00522-7 (2021).
doi: 10.1038/s41569-021-00522-7
pubmed: 33664502
pmcid: 7931503
Rodgers, M. M., Pai, V. M. & Conroy, R. S. Recent advances in wearable sensors for health monitoring. IEEE Sens. J. 15, 3119–3126 (2015).
doi: 10.1109/JSEN.2014.2357257
Chen, J., Kwong, K., Chang, D., Luk, J. & Bajcsy, R. Wearable sensors for reliable fall detection. in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 3551–3554 (IEEE, 2006).
Kim, J., Campbell, A. S., de Ávila, B.E.-F. & Wang, J. Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 37, 389–406 (2019).
pubmed: 30804534
pmcid: 8183422
doi: 10.1038/s41587-019-0045-y
De Brouwer, M. et al. mBrain: Towards the continuous follow-up and headache classification of primary headache disorder patients. BMC Med. Inform. Decis. Mak. 22, 87 (2022).
pubmed: 35361224
pmcid: 8969243
doi: 10.1186/s12911-022-01813-w
Siirtola, P., Koskimäki, H., Mönttinen, H. & Röning, J. Using sleep time data from wearable sensors for early detection of migraine attacks. Sensors 18, 1374 (2018).
pubmed: 29710791
pmcid: 5981434
doi: 10.3390/s18051374
Stubberud, A. et al. Forecasting migraine with machine learning based on mobile phone diary and wearable data. Cephalalgia 43, 033310242311692 (2023).
doi: 10.1177/03331024231169244
Böttcher, S. et al. Detecting tonic-clonic seizures in multimodal biosignal data from wearables: Methodology design and validation. JMIR Mhealth Uhealth 9, e27674 (2021).
pubmed: 34806993
pmcid: 8663471
doi: 10.2196/27674
Schmidt, P., Reiss, A., Dürichen, R. & Laerhoven, K. V. Wearable-based affect recognition: A review. Sensors 19, 4079 (2019).
pubmed: 31547220
pmcid: 6806301
doi: 10.3390/s19194079
Ranjan, Y. et al. RADAR-base: Open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. JMIR Mhealth Uhealth 7, e11734 (2019).
pubmed: 31373275
pmcid: 6694732
doi: 10.2196/11734
Canali, S., Schiaffonati, V. & Aliverti, A. Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digit. Health 1, e0000104 (2022).
pubmed: 36812619
pmcid: 9931360
doi: 10.1371/journal.pdig.0000104
Cho, S., Ensari, I., Weng, C., Kahn, M. G. & Natarajan, K. Factors affecting the quality of person-generated wearable device data and associated challenges: Rapid systematic review. JMIR Mhealth Uhealth 9, e20738 (2021).
pubmed: 33739294
pmcid: 8294465
doi: 10.2196/20738
Liao, Y., Thompson, C., Peterson, S., Mandrola, J. & Beg, M. S. The future of wearable technologies and remote monitoring in health care. in American Society of Clinical Oncology Educational Book, 115–121 (2019) https://doi.org/10.1200/EDBK_238919 .
Sriram, J. et al. Challenges in data quality assurance in pervasive health monitoring systems. In Future of Trust in Computing (eds Gawrock, D. et al.) 129–142 (Vieweg+Teubner, 2009). https://doi.org/10.1007/978-3-8348-9324-6_14 .
doi: 10.1007/978-3-8348-9324-6_14
Chung, S. et al. Real-world multimodal lifelog dataset for human behavior study. ETRI J. 44, 426–437 (2022).
doi: 10.4218/etrij.2020-0446
Schmidt, P., Reiss, A., Dürichen, R. & Van Laerhoven, K. Labelling affective states ‘in the wild’: Practical guidelines and lessons learned. in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 654–659 (ACM, 2018). https://doi.org/10.1145/3267305.3267551 .
Balbim, G. M. et al. Using fitbit as an mhealth intervention tool to promote physical activity: Potential challenges and solutions. JMIR Mhealth Uhealth 9, e25289 (2021).
pubmed: 33646135
pmcid: 7961407
doi: 10.2196/25289
Böttcher, S. et al. Data quality evaluation in wearable monitoring. Sci. Rep. 12, 21412 (2022).
pubmed: 36496546
pmcid: 9741649
doi: 10.1038/s41598-022-25949-x
Van Der Donckt, J. et al. From self-reporting to monitoring for improved migraine management. in Engineer meets Physician (EmP) (2022).
Vandenbussche, N. et al. Patients with Chronic Cluster Headache May Show Reduced Activity Energy Expenditure on Ambulatory Wrist Actigraphy Recordings during Daytime Attacks. (2023) https://doi.org/10.1101/2023.10.05.23296527 .
Empatica, S. R. L. E4 data: BVP expected signal. Empatica Support https://support.empatica.com/hc/en-us/articles/360029719792-E4-data-BVP-expected-signal .
Kaggle. Kaggle State of Machine Learning and Data Science Report 2022.Pdf. https://www.kaggle.com/c/kaggle-survey-2022/data (2022).
Perkel, J. M. Why Jupyter is data scientists’ computational notebook of choice. Nature 563, 145–146 (2018).
pubmed: 30375502
doi: 10.1038/d41586-018-07196-1
PyPoetry. Poetry: Python Dependency Management and Packaging Made Easy. https://python-poetry.org/ .
Weed, L., Lok, R., Chawra, D. & Zeitzer, J. The impact of missing data and imputation methods on the analysis of 24-hour activity patterns. Clocks Sleep 4, 497–507 (2022).
pubmed: 36278532
pmcid: 9590093
doi: 10.3390/clockssleep4040039
Heger, I. et al. Using mHealth for primary prevention of dementia: A proof-of-concept study on usage patterns, appreciation, and beliefs and attitudes regarding prevention. JAD 94, 935–948 (2023).
pubmed: 37355903
doi: 10.3233/JAD-230225
Muaremi, A., Arnrich, B. & Tröster, G. Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoSci. 3, 172–183 (2013).
doi: 10.1007/s12668-013-0089-2
Porter, S. R., Whitcomb, M. E. & Weitzer, W. H. Multiple surveys of students and survey fatigue. New Direct. Inst. Res. 2004, 63–73 (2004).
Paulsen, A., Overgaard, S. & Lauritsen, J. M. Quality of data entry using single entry, double entry and automated forms processing: An example based on a study of patient-reported outcomes. PLoS ONE 7, e35087 (2012).
pubmed: 22493733
pmcid: 3320865
doi: 10.1371/journal.pone.0035087
Healey, J., Nachman, L., Subramanian, S., Shahabdeen, J. A. & Morris, M. E. Out of the Lab and into the Fray: Towards Modeling Emotion in Everyday Life, 156–173 (2010). https://doi.org/10.1007/978-3-642-12654-3_10 .
Ottenstein, C. & Werner, L. Compliance in ambulatory assessment studies: Investigating study and sample characteristics as predictors. Assessment 29, 1765–1776 (2022).
pubmed: 34282659
doi: 10.1177/10731911211032718
Van Berkel, N., Goncalves, J., Hosio, S. & Kostakos, V. Gamification of mobile experience sampling improves data quality and quantity. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 1–21 (2017).
doi: 10.1145/3130972
Fischer, F. & Kleen, S. Possibilities, problems, and perspectives of data collection by mobile apps in longitudinal epidemiological studies: Scoping review. J. Med. Internet. Res. 23, e17691 (2021).
pubmed: 33480850
pmcid: 7864774
doi: 10.2196/17691
Gloster, A. T. et al. Daily fluctuation of emotions and memories thereof: Design and methods of an experience sampling study of major depression, social phobia, and controls. Int. J. Methods Psych. Res. 26, e1578 (2017).
doi: 10.1002/mpr.1578
Rawassizadeh, R., Momeni, E., Dobbins, C., Gharibshah, J. & Pazzani, M. Scalable daily human behavioral pattern mining from multivariate temporal data. IEEE Trans. Knowl. Data Eng. 28, 3098–3112 (2016).
doi: 10.1109/TKDE.2016.2592527
Harris, P. A. et al. The REDCap consortium: Building an international community of software platform partners. J. Biomed. Inform. 95, 103208 (2019).
pubmed: 31078660
pmcid: 7254481
doi: 10.1016/j.jbi.2019.103208
Fox-Wasylyshyn, S. M. & El-Masri, M. M. Handling missing data in self-report measures. Res. Nurs. Health 28, 488–495 (2005).
pubmed: 16287052
doi: 10.1002/nur.20100
Colls, J. et al. Patient adherence with a smartphone app for patient-reported outcomes in rheumatoid arthritis. Rheumatology 60, 108–112 (2021).
pubmed: 32572490
doi: 10.1093/rheumatology/keaa202
Baig, M. M., GholamHosseini, H. & Connolly, M. J. Mobile healthcare applications: System design review, critical issues and challenges. Australas Phys. Eng. Sci. Med. 38, 23–38 (2015).
pubmed: 25476753
doi: 10.1007/s13246-014-0315-4
Walsh, T. & Beatty, P. C. W. Human factors error and patient monitoring. Physiol. Meas. 23, R111–R132 (2002).
pubmed: 12214768
doi: 10.1088/0967-3334/23/3/201
Csikszentmihalyi, M., Csikszentmihalyi, M. & Larson, R. Validity and reliability of the experience-sampling method. in Flow and the Foundations of Positive Psychology: The Collected Works of Mihaly Csikszentmihalyi, 35–54 (2014).
Hoelzemann, A. & Van Laerhoven, K. A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors. http://arxiv.org/abs/2305.08752 (2023).
Bracke, V. et al. Design and evaluation of a scalable Internet of Things backend for smart ports. Softw. Pract. Exp. 51, 1557–1579 (2021).
doi: 10.1002/spe.2973
Sun, S. et al. The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions. Comput. Methods Prog. Biomed. 227, 107204 (2022).
doi: 10.1016/j.cmpb.2022.107204
Mombers, C., Legako, K. & Gilchrist, A. Identifying medical wearables and sensor technologies that deliver data on clinical endpoints: Editorial. Br. J. Clin. Pharmacol. 81, 196–198 (2016).
pubmed: 26542184
doi: 10.1111/bcp.12818
Vaisman, A., Bannerman, G., Matelski, J., Tinckam, K. & Hota, S. S. Out of sight, out of mind: A prospective observational study to estimate the duration of the Hawthorne effect on hand hygiene events. BMJ Qual. Saf. 29, 932–938 (2020).
pubmed: 32152090
doi: 10.1136/bmjqs-2019-010310
Berger, A. M. et al. Methodological challenges when using actigraphy in research. J. Pain Sympt. Manag. 36, 191–199 (2008).
doi: 10.1016/j.jpainsymman.2007.10.008
Choi, J., Ahmed, B. & Gutierrez-Osuna, R. Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Trans. Inf. Technol. Biomed. 16, 279–286 (2012).
pubmed: 21965215
doi: 10.1109/TITB.2011.2169804
Ahmadi, M. N., Nathan, N., Sutherland, R., Wolfenden, L. & Trost, S. G. Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data. J. Sports Sci. 38, 399–404 (2020).
pubmed: 31826746
doi: 10.1080/02640414.2019.1703301
Vert, A. et al. Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature. BMC Med. Res. Methodol. 22, 147 (2022).
pubmed: 35596151
pmcid: 9123693
doi: 10.1186/s12874-022-01633-6
Pagnamenta, S., Grønvik, K. B., Aminian, K., Vereijken, B. & Paraschiv-Ionescu, A. Putting temperature into the equation: Development and validation of algorithms to distinguish non-wearing from inactivity and sleep in wearable sensors. Sensors 22, 1117 (2022).
pubmed: 35161862
pmcid: 8838557
doi: 10.3390/s22031117
Stuyck, H., Dalla Costa, L., Cleeremans, A. & Van Den Bussche, E. Validity of the Empatica E4 wristband to estimate resting-state heart rate variability in a lab-based context. Int. J. Psychophysiol. 182, 105–118 (2022).
pubmed: 36252721
doi: 10.1016/j.ijpsycho.2022.10.003
Posada-Quintero, H. F. & Chon, K. H. Innovations in electrodermal activity data collection and signal processing: A systematic review. Sensors 20, 479 (2020).
pubmed: 31952141
pmcid: 7014446
doi: 10.3390/s20020479
Uchida, Y. & Izumizaki, M. The use of wearable devices for predicting biphasic basal body temperature to estimate the date of ovulation in women. J. Therm. Biol. 108, 103290 (2022).
pubmed: 36031211
doi: 10.1016/j.jtherbio.2022.103290
Reiss, A., Indlekofer, I., Schmidt, P. & Van Laerhoven, K. Deep PPG: Large-scale heart rate estimation with convolutional neural networks. Sensors 19, 3079 (2019).
pubmed: 31336894
pmcid: 6679242
doi: 10.3390/s19143079
Moser, B. A. Estimating the signal reconstruction error from threshold-based sampling without knowing the original signal. in 2017 3rd International Conference on Event-Based Control, Communication and Signal Processing (EBCCSP), 1–4 (IEEE, 2017). https://doi.org/10.1109/EBCCSP.2017.8022834 .
Van Der Donckt, J., Van Der Donckt, J., Deprost, E. & Van Hoecke, S. tsflex: Flexible time series processing & feature extraction. SoftwareX 17, 100971 (2022).
doi: 10.1016/j.softx.2021.100971
Van Der Donckt, J., Van Der Donckt, J., Deprost, E. & Van Hoecke, S. Plotly-resampler: Effective visual analytics for large time series. in 2022 IEEE Visualization and Visual Analytics (VIS) 21–25 (IEEE, 2022). https://doi.org/10.1109/VIS54862.2022.00013 .
Bernard, J., Ruppert, T., Goroll, O., May, T. & Kohlhammer, J. Visual-interactive preprocessing of time series data. in Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29–30, 39–48 (Citeseer, 2012).
Di, J. et al. Considerations to address missing data when deriving clinical trial endpoints from digital health technologies. Contemp. Clin. Trials 113, 106661 (2022).
pubmed: 34954098
doi: 10.1016/j.cct.2021.106661
Bulling, A., Blanke, U. & Schiele, B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46, 1–33 (2014).
doi: 10.1145/2499621
Rawassizadeh, R., Keshavarz, H. & Pazzani, M. Ghost imputation: Accurately reconstructing missing data of the off period. IEEE Trans. Knowl. Data Eng. 32, 2185–2197 (2020).
doi: 10.1109/TKDE.2019.2914653
Proceedings of the 2020 SIAM International Conference on Data Mining. (Society for Industrial and Applied Mathematics, 2020). https://doi.org/10.1137/1.9781611976236 .
Wu, X., Mattingly, S., Mirjafari, S., Huang, C. & Chawla, N. V. Personalized imputation on wearable-sensory time series via knowledge transfer. in Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 1625–1634 (ACM, 2020). https://doi.org/10.1145/3340531.3411879 .
Berkowitz, J. & Kilian, L. Recent developments in bootstrapping time series. Econ. Rev. 19, 1–48 (2000).
doi: 10.1080/07474930008800457
Efron, B. Missing data, imputation, and the bootstrap. J. Am. Stat. Assoc. 89, 463–475 (1994).
doi: 10.1080/01621459.1994.10476768
Bai, J. et al. An activity index for raw accelerometry data and its comparison with other activity metrics. PLoS ONE 11, e0160644 (2016).
pubmed: 27513333
pmcid: 4981309
doi: 10.1371/journal.pone.0160644
Cornelissen, G. Cosinor-based rhythmometry. Theor. Biol. Med. Model 11, 16 (2014).
pubmed: 24725531
pmcid: 3991883
doi: 10.1186/1742-4682-11-16
Moškon, M. CosinorPy: A python package for cosinor-based rhythmometry. BMC Bioinform. 21, 485 (2020).
doi: 10.1186/s12859-020-03830-w
Chalofsky, N. & Krishna, V. Meaningfulness, commitment, and engagement: The intersection of a deeper level of intrinsic motivation. Adv. Dev. Hum. Resourc. 11, 189–203 (2009).
doi: 10.1177/1523422309333147
Wolling, F., van Laerhoven, K., Siirtola, P. & Roning, J. PulSync: The heart rate variability as a unique fingerprint for the alignment of sensor data across multiple wearable devices. in 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 188–193 (IEEE, 2021). https://doi.org/10.1109/PerComWorkshops51409.2021.9431015 .
Van Remoortel, H. et al. Validity of six activity monitors in chronic obstructive pulmonary disease: A comparison with indirect calorimetry. PLoS ONE 7, e39198 (2012).
pubmed: 22745715
pmcid: 3380044
doi: 10.1371/journal.pone.0039198
Milstein, N. & Gordon, I. Validating measures of electrodermal activity and heart rate variability derived from the Empatica E4 utilized in research settings that involve interactive dyadic states. Front. Behav. Neurosci. 14, 148 (2020).
pubmed: 33013337
pmcid: 7461886
doi: 10.3389/fnbeh.2020.00148
Asahina, M., Poudel, A. & Hirano, S. Sweating on the palm and sole: Physiological and clinical relevance. Clin. Auton. Res. 25, 153–159 (2015).
pubmed: 25894655
doi: 10.1007/s10286-015-0282-1