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Van Der Donckt J, Vandenbussche N, Van Der Donckt J, Chen S, Stojchevska M, De Brouwer M, Steenwinckel B, Paemeleire K, Ongenae F, Van Hoecke S. Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches. Sci Rep 2024; 14:17545. [PMID: 39079945 PMCID: PMC11289092 DOI: 10.1038/s41598-024-67767-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
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.
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Affiliation(s)
- Jonas Van Der Donckt
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium.
| | - Nicolas Vandenbussche
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | | | - Stephanie Chen
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Marija Stojchevska
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Mathias De Brouwer
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Bram Steenwinckel
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Koen Paemeleire
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Femke Ongenae
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Sofie Van Hoecke
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
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Pilz LK, de Oliveira MAB, Steibel EG, Policarpo LM, Carissimi A, Carvalho FG, Constantino DB, Tonon AC, Xavier NB, da Rosa Righi R, Hidalgo MP. Development and testing of methods for detecting off-wrist in actimetry recordings. Sleep 2022; 45:6590428. [DOI: 10.1093/sleep/zsac118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/20/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Study Objectives
In field studies using wrist-actimetry, not identifying/handling off-wrist intervals may result in their misclassification as immobility/sleep and biased estimations of rhythmic patterns. By comparing different solutions for detecting off-wrist, our goal was to ascertain how accurately they detect nonwear in different contexts and identify variables that are useful in the process.
Methods
We developed algorithms using heuristic (HA) and machine learning (ML) approaches. Both were tested using data from a protocol followed by 10 subjects, which was devised to mimic contexts of actimeter wear/nonwear in real-life. Self-reported data on usage according to the protocol were considered the gold standard. Additionally, the performance of our algorithms was compared to that of visual inspection (by 2 experienced investigators) and Choi algorithm. Data previously collected in field studies were used for proof-of-concept analyses.
Results
All methods showed similarly good performances. Accuracy was marginally higher for one of the raters (visual inspection) than for heuristically developed algorithms (HA, Choi). Short intervals (especially < 2 h) were either not or only poorly identified. Consecutive stretches of zeros in activity were considered important indicators of off-wrist (for both HA and ML). It took hours for raters to complete the task as opposed to the seconds or few minutes taken by the automated methods.
Conclusions
Automated strategies of off-wrist detection are similarly effective to visual inspection, but have the important advantage of being faster, less costly, and independent of raters’ attention/experience. In our study, detecting short intervals was a limitation across methods.
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Affiliation(s)
- Luísa K Pilz
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
- Graduate Program in Psychiatry and Behavioral Sciences—UFRGS , Porto Alegre , Brazil
| | - Melissa A B de Oliveira
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
- Graduate Program in Psychiatry and Behavioral Sciences—UFRGS , Porto Alegre , Brazil
| | - Eduardo G Steibel
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
| | - Lucas M Policarpo
- Applied Computing Graduate Program (PPGCA)—Universidade do Vale do Rio dos Sinos (UNISINOS) , São Leopoldo , Brazil
| | - Alicia Carissimi
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
| | - Felipe G Carvalho
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
| | - Débora B Constantino
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
- Graduate Program in Psychiatry and Behavioral Sciences—UFRGS , Porto Alegre , Brazil
| | - André Comiran Tonon
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
- Graduate Program in Psychiatry and Behavioral Sciences—UFRGS , Porto Alegre , Brazil
| | - Nicóli B Xavier
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
- Graduate Program in Psychiatry and Behavioral Sciences—UFRGS , Porto Alegre , Brazil
| | - Rodrigo da Rosa Righi
- Applied Computing Graduate Program (PPGCA)—Universidade do Vale do Rio dos Sinos (UNISINOS) , São Leopoldo , Brazil
| | - Maria Paz Hidalgo
- Laboratório de Cronobiologia e Sono—Hospital de Clínicas de Porto Alegre (HCPA)/Universidade Federal do Rio Grande do Sul (UFRGS) , Porto Alegre , Brazil
- Graduate Program in Psychiatry and Behavioral Sciences—UFRGS , Porto Alegre , Brazil
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