PONE-D-24-08044A comparative analysis of 24-hour movement behaviors features using different accelerometer metrics in adults: implications for guideline compliance and associations with cardiometabolic healthPLOS ONE

Dear Dr. De Craemer,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: I Don't Know

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The results indicate that the movement behavior data varied depending on the metric used for analysis. Specifically, ENMO (Euclidean Norm Minus One) represented the most sedentary movement behavior profile, while CPM (Counts Per Minute) vector magnitude represented the most active profile.

This suggests that different accelerometer metrics capture different aspects of movement behavior, with some metrics highlighting more sedentary patterns while others emphasize more active behaviors. Understanding these differences is crucial for accurately assessing individuals' activity levels and sedentary behavior, which in turn can inform interventions aimed at promoting physical activity and reducing sedentary time to improve overall health outcomes.

Interestingly, the study found that reallocating time towards moderate-to-vigorous physical activity consistently predicted significant improvements in cardiometabolic variables, with the exception of fat percentage. This suggests that increasing time spent in moderate-to-vigorous physical activity may have positive effects on various aspects of cardiometabolic health, highlighting the importance of engaging in activities that elevate heart rate and promote greater exertion.

Overall, these findings emphasize the complexity of studying movement behaviors and their associations with health outcomes, indicating that the choice of accelerometer metric can influence both compliance rates with guidelines and the observed relationships with cardiometabolic variables.

Overall, the study suggests variations in agreement between different metrics, with some showing better consistency than others across various activity intensities. These findings underscore the importance of considering the choice of metric carefully when analyzing accelerometer data for physical activity assessment.Average acceleration showed strong associations with MVPA across all metrics, its relationship with LPA was weaker. Additionally, the intensity gradient exhibited stronger correlations with MVPA compared to LPA, with some variation across metrics.

The choice of metric significantly influences the composition of 24h MBs, particularly in terms of the distribution of time spent in sedentary behavior, light physical activity, and moderate to vigorous physical activity. ENMO tended to allocate more time to sedentary behavior, while CPM VM favored light physical activity, and MAD had a higher proportion of moderate to vigorous physical activity.

The analysis revealed significant associations between time spent on 24h movement behaviors (MB) and various cardiometabolic variables, with differences observed across different accelerometer metrics. Overall, these findings underscore the importance of considering different accelerometer metrics when examining associations between physical activity behaviors and cardiometabolic health outcomes. The direction and magnitude of associations varied across metrics, highlighting the need for tailored interventions aimed at promoting specific types of physical activity to improve cardiometabolic health.

The study has several limitations that should be considered when interpreting the results:

Selection of Cutoff Points: The use of cutoff points for each accelerometer metric is a potential limitation. While commonly used cutoff points were selected, there are alternative cutoff points available. These cutoff points are typically based on specific validation protocols conducted in particular populations. Therefore, the choice of cutoff points may impact the interpretation of physical activity data and comparisons across studies.

Limited Generalizability: The study only used hip-worn accelerometer data for the waking day. This may limit the generalizability of the findings, particularly when comparing them to studies using wrist-worn accelerometer data. Differences in acceleration patterns based on body location can influence the assessment of physical activity levels. Therefore, caution is needed when generalizing findings to populations or studies using different accelerometer placements.

Sample Characteristics: The results may be influenced by the characteristics of the sample population studied. Demographic factors such as age, gender, and physical fitness levels can affect physical activity patterns and associations with health outcomes. Therefore, the findings may not be representative of other populations with different demographic profiles.

Cross-Sectional Design: The study likely employed a cross-sectional design, which limits the ability to establish causal relationships between physical activity behaviors and cardiometabolic health outcomes. Longitudinal studies are needed to better understand the temporal relationships between these variables and to assess the effectiveness of interventions.

Measurement Error: Accelerometer measurements are subject to measurement error, which can arise from factors such as device malfunction, wear time compliance, and data processing methods. These errors could potentially affect the accuracy and reliability of the physical activity measurements and subsequent associations with health outcomes.

Acknowledging these limitations can help researchers and clinicians better interpret the study findings and guide future research efforts aimed at addressing these limitations to improve the understanding of physical activity and its impact on health.

Reviewer #2: This paper focuses on the different metrics available in the GGIR package for accelerometer processing. This is quite a technical aspect of physical activity measures, but an important one, especially in light of the findings which show that the different metrics can produce quite different summary estimates. In general, I think this is a good, well-written, useful paper. However there are areas which are unclear and I feel it currently tries to tackle too much. The discussion would also benefit from less focus on just repeating the results and more on what this means for someone about to begin a study – which metric should they use and why? I list some more detailed points below.

Major points

1) Currently, definitions and advantages and disadvantages of the different processing metrics and PA summary measures don’t appear until the methods section (eg ENMO at line 142, intensity gradient line 160+), but this makes it very hard to understand the background and why these different metrics and summaries (and hence the manuscript itself) are important. As this manuscript involves very technical aspects of accelerometer processing which not all readers will be familiar with, I suggest adding a clear overview of the full process (raw data to processed data to cut points & summaries) early on in the background. Then describe the different processing metrics and the different PA measures - definitions, where they fit into this process, how they differ and how it might be expected to affect the summaries.

2) This manuscript is trying to do a lot of things, which makes it difficult to follow. In particular, associations between PA measures and cardiovascular outcomes seem out of place – they’re not affected directly by the processing metrics, the analysis is not in-depth enough to be a full association study and its not clear what the implications are for different metrics. I think the paper would be stronger and clearer if this section were dropped entirely. If the authors do decide to keep this, then there needs to be more linking to the metrics and crucially some guidance on the appropriate metrics to use in different circumstances.

3) Much of the model description in the methods is not clear, so I am unable to comment on the suitability of the modelling. For example, at line 192 – what’s the outcome in this model? What fixed effect terms are included? What are the random terms? Line 192 refers to a single model, but line 194 to multiple models –how many models and what are they? Also, line 193 refers to random slopes, but line 194 talks about random intercept models (ie random intercept only) – which is it? Finally, I think there might be repeated measures on the same individual in here – is there a random intercept for participant included? A model equation would be useful, eg in the Supporting Information.

Minor points

Abstract

• Line 5 : The abbreviations ENMO and MAD need definitions

• Line 9: in what country?

Background

• Lines 35-41: The ‘combined’ guidelines mentioned here are new and I’m not sure if they extend beyond Canada - most countries and the WHO only have guidelines for physical activity. I think it’s worth being clear that these are not internationally-agreed guidelines, and in fact the WHO 2020 guidance explicitly says that ‘evidence was insufficient to quantify a sedentary behaviour threshold’.

• Lines 61-62: I’m not sure what point is being made here. Regardless of metrics, if the accelerometer is removed, determining non-wear time will be a problem. If not, then determining sleep time is an issue.

Methods

• Line 89-90: what does ‘active on the job market’ mean? Is this employed, or unemployed and looking for work? (to me, the ‘job market’ refers to recruitment for new posts)

• Line 97: more details required. How many days? Including weekends? Was there a minimum wear time to be included (either per day, or number of days?)

• Line 124-5: can you give more detail of the educational system for the international reader eg ages, qualifications. I don’t understand the distinction between ‘higher education degree’ and ‘university degree’.

• Line 136-8: add reference or justification

• Lines 142+ this information on metrics belongs in the background section

• Line 149-155 : mixing of reference styles

• Line 178: add reference

• Line 183-185: It’s not clear, but either these are the same MB measures for different metrics, in which case they are measuring the same thing so no surprise if they’re correlated, or they’re the same metric and different MBs, in which case they must be correlated because they sum to 24h. Either way, please justify why correlations are useful/relevant.

• Lines 214-7 for log-transformed outcomes, exp(coef) should be reported as % change – it’s not clear whether this is what is proposed

• Lines 218, 222: Please avoid the term ‘statistically significant’ and report all estimates with p-values, regardless of significance level.

Results

• Fig1: graphs are different sizes and dimensions

• Fig2: I can’t read some of these numbers- rethink colour scheme?

• Table S5: this table has very little information of use, and none of the % mentioned in the text are reported here.

• Table S6: what does the column ‘no guidance’ show?

Discussion

• Lines 356-269:.Can you expand on why they’re different eg what aspects of PA different metrics are capturing, when one might be more or less suitable than another?

• Line 375-378: a related point is that much of the evidence on which guidelines are based is still from self-report rather than accelerometer PA, and even in the latter case much of it is on older count-based approaches. Does this suggest we should try to use metrics that replicate this evidence even if they are not accurate? Or are the newer metrics presented here ‘better’ in any way? Can you say anything about when estimates of MBs might be over or underestimated?

• Line 388-391: what valuable insights?

• Line 391-397: what are the implications of this? Are you arguing that some metrics are better than others? Suitable for different purposes?

• Line 398-418: again, this is just reporting results – can you explain what this means for choosing different metrics?

Conclusion

• Line 435-437: can you offer a bit more guidance than just awareness? Surely reporting analyses using multiple metrics will just be confusing and encourage cherry-picking of results – what we need is guidance on which metric to use, and possibly when.

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Reviewer #1: No

Reviewer #2: No

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A comparative analysis of 24-hour movement behaviors features using different accelerometer metrics in adults: implications for guideline compliance and associations with cardiometabolic health

PONE-D-24-08044R1

Dear Dr. Craemer,

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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Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

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

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: I would like to congratulate the authors on a very thoughtful and comprehensive response to my comments. The extra explanation in the discussion and the moving of the association study to the supplementation file make the paper much clearer to read. I am satisfied with these and all the minor changes. While it’s a shame that they are not able to make any recommendations about the use of metrics in different situations, I understand the limitations here . One concern was that others might use this study to justify cherry-picking the metric that gives the most desirable answers, but I think that the caveats in the discussion should mostly discourage this.

This is really useful paper and I have no further comments.

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Reviewer #2: No

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