Evidence map›Paper›PMID 36278532›Full record

ArticleClocks & sleep2022

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

Lara Weed, Renske Lok, Dwijen Chawra, Jamie Zeitzer

Abstract read
In one paragraph

Article in Clocks & sleep, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  16. Reliable measures of rest-activity rhythm fragmentation: how many days are needed?European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lara WeedDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Renske LokDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
Dwijen ChawraDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-7873-9126
Jamie ZeitzerDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0001-6174-5282

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

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.

Indexed as

actigraphycircadian rhythmsimputationinterdaily stabilityintradaily variability

Identifiers

PMID36278532
PMCPMC9590093

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.