2016
DOI: 10.1177/0962280216657119
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Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity

Abstract: Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missin… Show more

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Cited by 24 publications
(7 citation statements)
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“…We simulated missingness evenly throughout the entire 24-hour period in relation to the observed patterns of missingness in the NoHoW trial. This is contrary to a previous study observing that missing data patterns more frequently occur at the beginning and end of the day [42]. It is of note that we utilised wrist-worn devices compared to the aforementioned study, which utilised hip worn accelerometers.…”
Section: Discussioncontrasting
confidence: 59%
“…We simulated missingness evenly throughout the entire 24-hour period in relation to the observed patterns of missingness in the NoHoW trial. This is contrary to a previous study observing that missing data patterns more frequently occur at the beginning and end of the day [42]. It is of note that we utilised wrist-worn devices compared to the aforementioned study, which utilised hip worn accelerometers.…”
Section: Discussioncontrasting
confidence: 59%
“…Despite the small bias, this is unlikely to be a feasible means of assessing free-living energy balance behaviors. Participant discomfort and sensor removal present additional biases (ie, missing data), which may require additional modeling approaches to address [48][49][50]. The threshold of practicality varies depending on the size, duration, computational resources, and specific aims of the research study.…”
Section: Xsl • Fomentioning
confidence: 99%
“…This resulted in the recoding of <7% of cases. As in previous publications [ 34 , 44 ], missing PA data from the FIT trial were accounted for using a weighted mixed model approach with variance weighting by the inverse of daily wear-time proportions to evaluate the effect of log-in duration on PA [ 45 ]. The weighted regression approach is an efficient approach for dealing with missing accelerometry data because cases with a higher proportion of missing wear time are down weighted compared with cases with less proportion of missing wear time, which has been shown to improve the precision in estimating accelerometry-estimated PA [ 45 ].…”
Section: Methodsmentioning
confidence: 99%