PONE-D-21-01724

Exploring daily time-use patterns: ATUS-X data extractor and online diary visualization tool

PLOS ONE

Dear Dr. Kolpashnikova,

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PLOS ONE

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: I'm a big fan of the various IPUMS projects, so I enjoyed reading this particular manuscript. I don't think it's ready for publication yet, however. I don't think it suffers from fundamental flaws, but I do think the revisions it needs go beyond minor edits.

I think there should be more of a balance in the manuscript in terms of discussing both the data extraction tool for the ATUS data and the visualization tool. The latter gets more attention, but I think the former is of equal or even greater importance depending on the audience. Researchers who want to do more intensive analyses will find the extraction tool of much greater utility than the visualization tool, as the latter is engaging to use but is limited in terms of what you can do with it (as is often the case with data visualization tools). So, more discussion about the extraction tool is in order, I think.

As part of that discussion, the authors should spend more time discussing the contents of ATUS data in terms of the categories of activities included, the demographic/socio-economic data included, and the content of the topical modules that the manuscript mentions, e.g. on eldercare, health, well-being, etc., so that people have a better sense of how rich the contents of the data are and what opportunities they do (and don't) present. I think that more discussion of the structure of the ATUS data would also be in order in terms of how the data at the BLS are spread across multiple files, as would more discussion of the event-level nature of the data including some visual representation of the diary data. The authors note that the structure of such data is novel to many researchers and thus potentially daunting with which to work. I don't disagree with that, but I also think that the somewhat bare-bones description of the data in the manuscript effectively understates how much more accessible the data extraction tool makes the data. More explanation of how unwieldy the original ATUS data are, and thus how much easier the extraction tool makes it to manage the data, would make that point more clear.

I also have some more specific/particular points. Since the manuscript didn't have page numbers, I'll refer to the line numbers that were included in the manuscript I downloaded.

On lines 81-82, the authors state that "For illustration purposes, the visualization tool uses the subsample for caregivers identified in the ATUS from 2011 to 2019, downloaded from IPUMS." Later, on lines 223-224, they state that users can upload their own extracts for use by the visualization tool; that point should be made earlier, just to make it clear from the beginning that the visualization tool is not confined to the default subset of data.

If I understand correctly, the visualization tool doesn't break results down by time period in terms of year or month or year-month, even though the year and month of the interviews are recorded (see https://www.atusdata.org/atus-action/variables/group/tech_tech). Is my understanding correct? And, if so, what is the basis for the authors' claims on lines 321-324 that the tool will make it possible to visualize the effects of the pandemic on time use, caregiving duties, etc.? I agree that time-use data from 2020 could be useful for studies of, say, how the pandemic did or didn't exacerbate gender inequities in terms of divisions of labor within households. However, I'm not sure what insights can be gathered from a visualization tool that doesn't let you break the data down by dates to see how time allocations shifted during 2020.

Lines 170-172 refer to aggregating diary events data into an 11X11 matrix of categories and transitions between them, but those categories aren't identified until Lines 233-235. The category definitions should be provided earlier, to clarify what sorts of transitions are being visualized.

Lines 237-238 state that "Transitions plots the percent of all transitions from one activity to another shown in the highlighted path." If I understand correctly, the point is that the value for transitions between activity A and activity B is meant to be the number of transitions from A to B as a percentage of all transitions between activities. If so, the phrasing should be tweaked to make that more explicit. As it stands now, the wording is a bit unclear.

Lines 328-333 refer to integrating multinational time-use survey data into the tool, and I agree that such an effort could be very worthwhile. E.G. seeing whether differences in health-care systems and welfare states are associated with different patterns of time use, or different shifts in time use in response to macro-level shocks and crises, could be quite interesting. However, the authors don't mention the heritage time-use data at https://www.ahtusdata.org/ahtus/. Is there any intention of working these data into the visualization tool? Such an integration, in combination with additional features for breaking data down by dates/years, could provide insights with regard to how/whether gender imbalances and household divisions of labor have shifted over the decades as more women participate in the formal labor force. To the extent that there are health implications from certain types of daily activities, as the manuscript suggests, having a tool with longer time ranges of data could likewise be informative with regard to analysis longer-term health trends.

I understand that there could be considerable technical issues with such integration, and I don't expect the authors to resolve them as a condition of publication. :-) But, since they already mention future ideas for the visualization tool, I'd like to see more about extending it to cover the heritage data and provide options to filter/analyze data over time - are such ideas under consideration, would they be feasible, what data-related challenges would they involve, etc.

Reviewer #2: This article introduces the ATUS-X diary visualization tool for time use data. Time use data are certainly underutilized in research, and I suspect one reason why is their complexity. A tool that can help with translation of these data would be very useful. Overall I think there are some improvements that can be made to this manuscript that would help convince the reader that this particular tool will be helpful for visualizing, understanding, and translating time-use data.

Introduction

• Time-use data can be used in many different ways. They may be collapsed into broad categories or used in much finer detail. I think readers who are more recently introduced to the concept could use a bit more clarification on the complexity of time-use data and the various ways in which researchers in many different fields use them. For example, the intro states a few times that these data are ‘powerful’ and ‘wonderfully rich’, but I don’t feel like many examples are given to support those statements. A brief mention of how these data are generally collected may also help readers (on that note- is the visualization tool only useful for ATUS data? Or other time-use data? Only with self-reported diary data, or also accelerometry data, for example).

• It would be helpful to include references for the example studies/study topics of time use (lines 44-47).

Methods

• A reference is needed for the ATUS (BLS website or other).

• As it appears 1 minute epoch lengths are collapsed into 15 minute epoch lengths, it is worth describing the unit of data collection in the ATUS (i.e., do participants report time-use in 1 minute epoch lengths, 10 minute, every second, etc.).

o Related to this, what is the rationale for collapsing time specifically into 15 minute sequences?

Results

• The tempograms can clearly be very useful in a variety of research. I have a bit of trouble seeing the differences in the transitions visualizations and it makes me wonder if there is another way you can describe and display the value of these visualizations. Perhaps this type of figure is just less intuitive and will require a bit more in-text description, or maybe it would be more useful for an analysis with fewer time-use categories? This could be included in the discussion.

• I’m not sure if the figure captions got lost along the way, but if there are none, it seems that adding some in would improve clarity. I also expect the figures will be higher quality in the publication, as they are blurry and not very legible in the PDF.

Discussion

• Overall, I think there needs to be a bit of discussion on what makes the visualizations from this tool better than various tools that already exist. As a person who studies broader categories of time use, I don’t necessarily feel convinced that this tool will allow me to create more intuitive or more translatable figures than those I can create quickly and easily with certain R packages. Why should I make the switch to this tool?

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Reviewer #1: No

Reviewer #2: No

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Exploring daily time-use patterns: ATUS-X data extractor and online diary visualization tool

PONE-D-21-01724R1

Dear Dr. Kolpashnikova,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Solveig A. Cunningham, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments:

Please fix the figures to provide clear titles describing what is being shown, as well as details about the data source and population which the data are describing. The figures are currently not clearly labelled.