The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns

Abstract

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.

Research on a machine learning-based adaptive and efficient screening model for psychological symptoms of community correctional prisoners

“…In this study, there were some missing values in the raw data we used, and most of the missing values were filled in by manually tracing the raw materials. For a small amount of other missing values such as age and other quantitative data, we use mean interpolation to fill in, as the mean can represent the central trend of the data and help maintain its distribution.For qualitative data such as crime types, we use the median to fill in, which is a better choice because it can reduce the impact of extreme values while maintaining the order and level of the data 38 .…”

Impaired 24-h activity patterns are associated with an increased risk of Alzheimer’s disease, Parkinson’s disease, and cognitive decline

“…High-frequency (100 Hz) accelerometer data were processed on Sherlock, a high-performance computing cluster provided by Stanford University, using the steps outlined in Weed et al 2022 [ 18 ]. In brief, data spanning 1 week of collection were down-sampled to 30 s epochs using the biobankAccelerometerAnalysis package in Python v3.6.1 [ 17 ].…”

Association of 24-hour activity patterns with risk of Alzheimer’s disease, Parkinson’s disease, and cognitive decline

“…High frequency (100 Hz) accelerometer data were processed on Sherlock, a high-performance computing cluster provided by Stanford University,using the steps outlined in Weed et al 2022 18 . In brief, data spanning 1 week of collection were down-sampled to 30 second epochs using the biobankAccelerometerAnalysis package in Python v3.6.1 17 .…”

“…Non-wear time was defined as stationary episodes lasting for at least 60 minutes in which all three axes had a standard deviation of less than 13.0 mg. If present, non-wear segments were automatically imputed using the median of similar time-of-day vector magnitude and intensity distribution data points with 30-second granularity on different days of the measurement 18 . Following these preprocessing steps, we derived the following six metrics.…”

“…2022 18 . In brief, data spanning 1 week of collection were down-sampled to 30 second epochs using the biobankAccelerometerAnalysis package in Python v3.6.1 17 .…”