ArticleJMIR mHealth and uHealth2020
Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps.BMC gastroenterology · 2022Pooled it
- Applications of Model-Based Meta-Analysis in Drug Development.Pharmaceutical research · 2022Pooled it
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- Reliability of Actigraphy for the Assessment of Sleep and Circadian Rhythms in Rett and Related Syndromes.Journal of intellectual disability research : JIDR · 2026Article
- Preventive circadian medicine: improving health with sleep checkups.Npj biological timing and sleep · 2025Review
- Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation.Proceedings of machine learning research · 2024Article
- A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach.Journal of medical Internet research · 2023Article
- The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns.Clocks & sleep · 2022Article
- Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia.Frontiers in neuroinformatics · 2022Article
- The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities.Journal of clinical medicine · 2021Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks.
objectiveThe aim of this study was to impute missing values in data using a deep learning approach.
methodsTo develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning-based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning-based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression).
resultsThe zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts.
conclusionsOur deep learning-based imputation model performed better than the other methods when imputing missing values in actigraphy data.
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