The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns
The purpose of this study is to characterize the impact of the timing and duration of missing actigraphy data on interdaily stability (IS) and intradaily variability (IV) calculation. The performance of three missing data imputation methods (linear interpolation, mean time of day (ToD), and median T...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Clocks & Sleep |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-5175/4/4/39 |
| _version_ | 1827641224005156864 |
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| author | Lara Weed Renske Lok Dwijen Chawra Jamie Zeitzer |
| author_facet | Lara Weed Renske Lok Dwijen Chawra Jamie Zeitzer |
| author_sort | Lara Weed |
| collection | DOAJ |
| description | The purpose of this study is to characterize the impact of the timing and duration of missing actigraphy data on interdaily stability (IS) and intradaily variability (IV) calculation. The performance of three missing data imputation methods (linear interpolation, mean time of day (ToD), and median ToD imputation) for estimating IV and IS was also tested. Week-long actigraphy records with no non-wear or missing timeseries data were masked with zeros or ‘Not a Number’ (NaN) across a range of timings and durations for single and multiple missing data bouts. IV and IS were calculated for true, masked, and imputed (i.e., linear interpolation, mean ToD and, median ToD imputation) timeseries data and used to generate Bland–Alman plots for each condition. Heatmaps were used to analyze the impact of timings and durations of and between bouts. Simulated missing data produced deviations in IV and IS for longer durations, midday crossings, and during similar timing on consecutive days. Median ToD imputation produced the least deviation among the imputation methods. Median ToD imputation is recommended to recapitulate IV and IS under missing data conditions for less than 24 h. |
| first_indexed | 2024-03-09T17:10:24Z |
| format | Article |
| id | doaj.art-845504cdf1684d9eb343a891b058cd99 |
| institution | Directory Open Access Journal |
| issn | 2624-5175 |
| language | English |
| last_indexed | 2024-03-09T17:10:24Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Clocks & Sleep |
| spelling | doaj.art-845504cdf1684d9eb343a891b058cd992023-11-24T14:05:31ZengMDPI AGClocks & Sleep2624-51752022-09-014449750710.3390/clockssleep4040039The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity PatternsLara Weed0Renske Lok1Dwijen Chawra2Jamie Zeitzer3Department of Bioengineering, Stanford University, Stanford, CA 94305, USADepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USADepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USADepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USAThe purpose of this study is to characterize the impact of the timing and duration of missing actigraphy data on interdaily stability (IS) and intradaily variability (IV) calculation. The performance of three missing data imputation methods (linear interpolation, mean time of day (ToD), and median ToD imputation) for estimating IV and IS was also tested. Week-long actigraphy records with no non-wear or missing timeseries data were masked with zeros or ‘Not a Number’ (NaN) across a range of timings and durations for single and multiple missing data bouts. IV and IS were calculated for true, masked, and imputed (i.e., linear interpolation, mean ToD and, median ToD imputation) timeseries data and used to generate Bland–Alman plots for each condition. Heatmaps were used to analyze the impact of timings and durations of and between bouts. Simulated missing data produced deviations in IV and IS for longer durations, midday crossings, and during similar timing on consecutive days. Median ToD imputation produced the least deviation among the imputation methods. Median ToD imputation is recommended to recapitulate IV and IS under missing data conditions for less than 24 h.https://www.mdpi.com/2624-5175/4/4/39actigraphycircadian rhythmsinterdaily stabilityintradaily variabilityimputation |
| spellingShingle | Lara Weed Renske Lok Dwijen Chawra Jamie Zeitzer The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns Clocks & Sleep actigraphy circadian rhythms interdaily stability intradaily variability imputation |
| title | The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns |
| title_full | The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns |
| title_fullStr | The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns |
| title_full_unstemmed | The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns |
| title_short | The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns |
| title_sort | impact of missing data and imputation methods on the analysis of 24 hour activity patterns |
| topic | actigraphy circadian rhythms interdaily stability intradaily variability imputation |
| url | https://www.mdpi.com/2624-5175/4/4/39 |
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