Dear Dr Hoertel,

Thank you for submitting your manuscript entitled "An Individual-Based Microsimulation Model of the Impact of Excessive Social Media Use On Adolescent Depression and Its Consequences in France" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review. Please also note that we will send a rejection letter for PMEDICINE-D-24-03799.

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Please re-submit your manuscript within two working days, i.e. by Nov 28 2024.

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Feel free to email me at atosun@plos.org or us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Alexandra Tosun, PhD

Associate Editor

PLOS Medicine

Dear Dr Hoertel,

Many thanks for submitting your manuscript "An Individual-Based Microsimulation Model of the Impact of Excessive Social Media Use On Adolescent Depression and Its Consequences in France" (PMEDICINE-D-24-03990R1) to PLOS Medicine. The paper has been reviewed by subject experts and a statistician; their comments are included below and can also be accessed here: [LINK]

As you will see, the reviewers outline several points for clarification/justification and offer several suggestions for adjusting and improving the analysis. After discussing the paper with the editorial team and an academic editor with relevant expertise, I'm pleased to invite you to revise the paper in response to the reviewers' comments. We plan to send the revised paper to some or all of the original reviewers, and we cannot provide any guarantees at this stage regarding publication.

In addition to these revisions, you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests shortly.

When you upload your revision, please include a point-by-point response that addresses all of the reviewer and editorial points, indicating the changes made in the manuscript and either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please also be sure to check the general editorial comments at the end of this letter and include these in your point-by-point response. When you resubmit your paper, please include a clean version of the paper as the main article file and a version with changes tracked as a marked-up manuscript. It may also be helpful to check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

We ask that you submit your revision by Apr 02 2025. However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Don't hesitate to contact me directly with any questions (atosun@plos.org).

Best regards,

Alexandra

Alexandra Tosun, PhD

Associate Editor

PLOS Medicine

atosun@plos.org

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Comments from the academic editor:

* Please ensure that the causality in the model is sufficiently justified and discuss the evidence on which it is based, e.g. small prospective studies/trials where the key question would be the representativeness and transferability of the estimated effect on depression/outcomes of however they changed media consumption, or observational studies documenting relationships that are either cross-sectional or prospective.

* Please ensure that you have adequately adjusted for confounding and discussed potential unaddressed confounding in detail. Have you considered internet use as a coping mechanism?

* Please discuss the generalizability/transferability of the effect size.

* Please ensure that the above points are addressed and documented in detail in your point-by-point response and in the manuscript.

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Comments from the reviewers:

Reviewer #1:

Major Comments:

1. The microsimulation model structure lacks transparency. Providing detailed justifications for key assumptions, particularly the calibration of residual baseline risk, would enhance the credibility of the model.

2. The model assumes additive effects of risk factors, potentially ignoring synergistic or multiplicative interactions (e.g., how childhood adversity combined with excessive social media use could amplify depression risk). Including interaction terms or testing these assumptions would improve realism.

3. The risk equation assumes a log-linear relationship between risk factors and depression, potentially oversimplifying interactions. Testing for non-linearities or complex interactions among key risk factors could improve accuracy.

4. Extrapolating social media usage with a gamma distribution assumes consistent patterns across platforms and demographic groups, which may not hold. Validation of these assumptions against independent datasets is recommended.

5. Validation against U.S. data adds robustness, but the assumption that other risk factors are similarly distributed between France and the U.S. needs stronger justification. Regional variations in mental health care access and cultural attitudes may limit generalisability.

6. The calibration process (Nelder-Mead simplex method) is standard but susceptible to local minima. Conducting sensitivity analyses to explore how variations in calibration parameters influence outcomes would strengthen the findings.

7. For bootstrapped samples, it is unclear whether convergence diagnostics were performed to ensure stability of estimates. This should be explicitly addressed.

8. Bootstrapping may underestimate uncertainty when parameter distributions are highly skewed or non-normal. Alternative methods, such as Monte Carlo simulations, should be considered to verify robustness.

9. The reliance on bootstrapping lacks evaluation of potential biases from non-independence in samples. Addressing this limitation or testing alternative resampling methods would strengthen the findings.

10. Beyond R² and Pearson's R, metrics such as mean absolute error (MAE) or root mean square error (RMSE) could provide a more nuanced assessment of model fit.

11. The model simulates annual risk assessments but does not account for intra-annual variations (e.g., seasonal effects on social media use or depression rates). Exploring these could offer more granular insights.

12. Some parameters (e.g., the 20% standard deviation for social media usage extrapolations) appear arbitrary. Providing sensitivity analyses or citing empirical evidence for these choices would improve confidence in the model.

13. The paper mentions uncertainty intervals but lacks detail on how parameter uncertainty propagates through the model. Bayesian approaches or probabilistic sensitivity analyses might better capture full uncertainty.

14. For statistical significance reporting effect sizes, confidence intervals, and advanced model fit metrics instead of p<0.05 would align better with current statistical practices.

15. While R² and Pearson's R validate the model, overfitting or predictive accuracy is not explicitly assessed. Including cross-validation or out-of-sample testing would enhance validation robustness.

16. Parameters like the 10% correlation coefficient between social media platforms seem arbitrary. Testing this assumption in sensitivity analyses would clarify its impact on results.

17. The rationale for choosing specific parameter distributions (e.g., normal or uniform) is missing. Providing this information would clarify why these distributions were used for model inputs.

Minor Comments:

18. Simulating 1,000 individuals per cohort may introduce sampling variability, especially for rare outcomes like suicide. Justification for this choice and its implications on uncertainty should be discussed.

19. While parameter values are referenced in Supplementary Table 1, a concise summary of the most critical parameters and their uncertainty ranges should be included in the main text for clarity.

20. The integration of multiple data sources (e.g., INSEE, national surveys) is commendable, but handling of inconsistencies or missing data should be explicitly detailed.

21. The "COVID-19 confinement" factor long-term effects on depression trends remain unclear. Discussing how this factor was validated or cross-checked would add robustness.

22. Scenarios like replacing 30 minutes of social media use with physical activity are idealistic. Addressing adherence and real-world feasibility would make these analyses more practical.

23. The manuscript estimates societal costs but does not address the cost-effectiveness of proposed interventions. A dedicated analysis comparing intervention costs to benefits is recommended.

24. The limitations section should explicitly address how unmeasured confounders, such as genetic predisposition, were mitigated or might bias results.

25. Hypothetical interventions are assessed but not aligned with real-world pilot studies or trials. Incorporating data from similar initiatives would validate assumptions.

26. Potential biases in data sources (e.g., underreporting of mental health issues or inaccuracies in self-reported social media usage) are not discussed and should be addressed.

27. The model assumes universal adherence to interventions, which is often unrealistic. Incorporating adherence rates from comparable interventions would make projections more realistic.

28. Figures comparing observed vs. predicted prevalence rates lack detailed annotations (e.g., reasons for discrepancies in specific years). Enhanced visual clarity would aid interpretation.

29. Figures such as Supplementary Figures 1 and 2 (adoption curves) lack explanation for assumptions about adoption trends. Clarifying whether these trends are universal or France-specific would be helpful.

30. Supplementary Table 1 could group parameters by module (e.g., demographics, behaviours, outcomes) for easier interpretation. Including metadata, such as each parameter's role (e.g., input vs. calibrated), would improve usability.

31. Supplementary Table 2 details time spent on platforms but lacks longitudinal trends. Including these trends would clarify how usage patterns influence depression risks.

32. Sensitivity results for key parameters (e.g., baseline risk, COVID-19 impact) are not detailed. Including tornado diagrams or comprehensive outputs would improve transparency.

33. While calibration is summarised (e.g., Supplementary Figure 5), the absence of residual plots or advanced goodness-of-fit metrics leaves gaps in assessing quality.

34. Figures use dotted lines for uncertainty ranges but could include shaded areas for better visual distinction.

35. Some parameter estimates (e.g., for obesity and substance use) differ slightly between tables and figures. Consistency or explanations for discrepancies would avoid confusion.

Reviewer #2:

This paper developed an individual-based microsimulation to model the impact of excessive social media use on depression among adolescents in France. The model was calibrated to reproduce observed rates of depression in France in 2011. The calibrated model was further tested to see whether it reproduced empirical trends in social media use prevalence and observed rates of depression in France after 2011. Model validation was assessed to determine the model's ability to predict the prevalence of adolescent depressive symptoms in the United States using 2009-2019 data. Finally, the validated model was used to explore four scenarios and their impacts on total costs and mortality attributed to depression and suicide:

1. The natural course of adolescent depression (if social media had not been available over the period 2000-2022);

2. A scenario where social media use would have been limited to a maximum of 1 hour per day;

3. Starting in 2016, a scenario where social media was reduced by 30 minutes per day for those with daily use ≥ 1 hour per day and that time replaced with 30 minutes of physical activity per day;

4. Social media limited to a maximum of 1 hour per day for 8.5% of adolescents at highest risk for depression.

The authors should be commended for their work in developing such a novel model and their rigorous approach to model calibration and validation to ensure the model produced meaningful results on a timely and important issue.

Below are a few comments and suggestions intended to further strengthen key aspects of the paper.

Introduction

- The authors provide a well-reasoned argument for the focus on social media and the challenges in disentangling its influence on rising adolescent depression rates. What is missing however is a clear explication of the gap this model is trying to fill. Why was individual-based microsimulation modelling used over other approaches e.g., agent-based modelling? Have studies previously used this approach to understand social media use (to my knowledge, they have not which highlights the novelty of this model)?

- It is important also to provide some context for the choice of intervention scenarios being modelled. For example, one of the scenarios relates to time substitution from social media use to physical activity. Why was physical activity chosen? Is there any evidence to suggest that adolescents who engage in excessive social media use are less physically active? Is it not equally possible that time might be substituted from other activities e.g., sleeping which is also a risk factor for depressive symptoms? To be clear, the choice to look at physical activity is not an issue, I just think the paper would be strengthened by providing a better context around time substitution and evidence supporting the choice to look at physical activity over other activities.

Methods

- Could authors clarify if DALE - adjusted life expectancy loss - refers to Disability-adjusted life years (DALYs)? Or if authors are referring to Health-adjusted life expectancy (HALE)? DALE may confuse some readers as its not a term I've come across previously.

- The chosen interventions are interesting however, their effects are difficult to compare as they are all implemented over different time periods. Within the figures, it would be helpful to provide a line that shows the 'business-as-usual' or calibrated trajectory which shows the trends observed in the data, enabling readers to make a visual comparison of the potential impact of each scenario relative to the baseline case.

- What was the timeframe for scenario 2 where social media use is limited to a maximum of 1 hour per day and scenario 4 where these limits were only imposed on high-risk adolescents? Were these over the same time period 2000-2022?

- Why did authors decide to limit the timeframe of scenario 3 to 2016 and onwards? It makes it hard to compare which scenario results in the greatest reduction in the cumulative prevalence of depression when the timeframes are so different. This scenario seems almost as effective as scenario 2 where social media is limited to 1 hour max for all and makes me wonder whether it would have been more effective if it were implemented from 2000-2022 and not 2016-2022.

- A recent umbrella review by Sala focusing on the mental health impacts of social media use among adolescents found that > 2 hours of social media use per day was associated with negative mental health outcomes. It would also be helpful if authors could provide some justification or insight into why a 1 hour cut off for social media us was tested and others weren't considered.

Sala, A., L. Porcaro, and E. Gómez, Social Media Use and adolescents' mental health and well-being: An umbrella review. Computers in Human Behavior Reports, 2024. 14: p. 100404.

Results

- When authors mention 'the current situation' are they referring to the observed social media and depression trends or the scena

- On page 12, at the bottom of the results section authors state that "limiting social media use to 1 hour per day for all adolescents was associated [with] the greatest benefits in terms of medical and social costs…". This makes sense in relation to Table 1 which states that interventions all spanned 2000-2022. But per my earlier point, this seems to conflict with the methods section (p. 8) which states that "Starting in 2016 when average exposure to SM was greater than 90 min, SM use would have been reduced by 30 min per day …". Can authors please clarify the timeframes in both the text and table?

- If one intervention was only implemented from 2016 onwards, and the other from 2000, then I think it would be misleading to say that one is more effective than the other without explicitly qualifying this statement and the extent to which these different timeframes likely impacted the findings.

- Table 1, could authors clarify that scenario 2 where 30 minutes of social media use is replaced by 30 min of physical activity, that this replacement only occurs 'for those with daily use ≥ 1 hour per day.

Discussion

- Could authors make a statement about the generalisability of the model's findings and the adaptability of the model to other contexts?

Figure S2

- Could authors explain in more detail how the curves in Appendix figure S2 were derived and the assumptions underpinning these?

Minor comments

- On page 9, under section 2.7, "…general population exposed to SN" and page 10, "…adolescents exposed to SN between…" - please change to SM. More generally please define social media as SM and use the acronym consistently in all subsequent mentions.

- On page 12, at the bottom of the results section authors state that "limiting social media use to 1 hour per day for all adolescents was associated [with] the greatest benefits in terms of medical and social costs…". I think there is a 'with' missing.

Reviewer #3: Thank you for submitting this very informative and well written manuscript titled An Individual-Based Microsimulation Model of the Impact of Excessive Social Media Use On Adolescent Depression and Its Consequences in France.

Overall, this is an excellent piece and an important topic to consider.

I believe the background literature and rationale are mostly sufficient. However, I believe there are some more recent studies that could be used in some sections to enhance the robustness of the assumptions. There are some very recent papers outlining links (or lack of evidence of links) between SM use and psychological distress. The recent literature suggests that it is not as simple as more time on SM = increases in mental health problems. The relationship is complex and affected by several factors such as engagement with SM (i.e. active versus passive engagement), experiences with cyberbullying, who and how young people are interacting with (i.e. friend or foe) and their own engagement in terms of posting. This needs to be accounted for or considered in more detail at least in the intro and discussion, and perhaps in the model if at all possible.

In section 2 (Methods) I would like to see a few sentences to summarize the method rather than just referring to previous studies. Currently it says "Following previously described methods [27-31]".

The model description and justification are clear and detailed, and the purpose and scope are evident and justified.

It appears that the data used in the model are appropriate to the context and population under study, and have been drawn from recent statistics or (mostly) recent studies - based on my earlier comment there may perhaps be some that could be more updated.

Parameters seem to be appropriately selected.

Validation and calibration are well reported.

Can the authors expand on any sensitivity analysis carried out in terms of if any assumptions or parameters were changed did this affect the model outcomes?

Results and interpretation are good. Perhaps with the addition of further literature as suggested above, this may need changing though.

Model documentation is clear and appears reproducible.

Any attachments provided with reviews can be seen via the following link: [LINK]

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Dear Dr. Hoertel,

Thank you very much for re-submitting your manuscript "An Individual-Based Microsimulation Model of the Impact of Excessive Social Media Use On Adolescent Depression and Its Consequences in France" (PMEDICINE-D-24-03990R2) for review by PLOS Medicine.

Thank you for your detailed response to the reviewers' and editors’ comments. I have discussed the paper with my colleagues, and it has also been seen again by all of the original reviewers. The changes made to the paper were satisfactory to the reviewers. As such, we intend to accept the paper for publication, pending your attention to the editors' comments below in a further revision. When submitting your revised paper, please once again include a detailed point-by-point response to the editorial comments.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript: ********

In revising the manuscript for further consideration here, please ensure you address the specific points made by the editors. In your rebuttal letter you should indicate your response to the editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

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Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

We ask that you submit your revision within 1 week (Aug 13 2025). However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Please do not hesitate to contact me directly with any questions (atosun@plos.org).

We look forward to receiving the revised manuscript.

Sincerely,

Alexandra Tosun, PhD

Senior Editor 

PLOS Medicine

plosmedicine.org

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

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Comments from Reviewers:

Reviewer #1: Thank you for thoroughly addressing all my comments and providing detailed clarifications in your revised manuscript. I appreciate the effort you have put into enhancing the rigor and transparency of your analyses.

The manuscript now reflects a robust and valuable contribution to subject, I am happy to recommend this work for publication and look forward to seeing its positive impact in the field.

Reviewer #2: The authors have done a great job revising the manuscript and have thoroughly addressed all the points from my review. This is a strong piece of work that will make a valuable contribution to the field. Congratulations to the authors.

Reviewer #3: Thank you to the authors for thoroughly attending to all the reviewer comments in such detail. This has further improved the quality and transparency of the study. Well done.

Any attachments provided with reviews can be seen via the following link:

********

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* Please ensure that the Introduction ends with a clear description of the study question or hypothesis.

METHODS AND RESULTS

* Please add the following statement, or similar, to the Methods: "This study is reported as per the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD-AI) statement (S1 Checklist)." When completing the checklist, please use section and paragraph numbers, rather than page numbers.

* When reporting age, please add a unit, such as years.

* l.292: Please note that this information (Data availability and Code availability) should be included in the Data Availability Statement in the online submission form. You may remove the links from the main text and simply describe the data sources and how they were used.

* Figure 1: Please define the meaning of the orange triangles in the figure legend. Please note that the figure description appears to repeat certain information. Please revise.

* l.356ff, “Limiting social media use to 1 hour per day for all adolescents was associated with the greatest benefits in terms of medical and social costs…” – we suggest describing the cost results in more detail here.

* Please specify the variables controlled for in all relevant Tables.

* We find the findings in Figures S10 and S11 very interesting and useful. They might be worth moving from the supplementary information to the main text. However, we will leave this decision to you.

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General Editorial Requests

1) We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

2) Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

3) Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Dear Dr Hoertel, 

On behalf of my colleagues and the Academic Editor, Jeremy D Goldhaber-Fiebert, I am pleased to inform you that we have agreed to publish your manuscript "Impact of Excessive Social Media Use On Adolescent Depression and Its Consequences in France: An Individual-Based Microsimulation Model" (PMEDICINE-D-24-03990R3) in PLOS Medicine.

I appreciate your thorough responses to the reviewers' and editors' comments throughout the editorial process. We look forward to publishing your manuscript. Editorially, there are a few remaining points that should be addressed prior to publication. We will carefully check whether the changes have been made. If you have any questions or concerns regarding these final requests, please feel free to contact me at atosun@plos.org.

Please see below the minor points that we request you respond to:

1) Data availability: In the Data Availability Statement of the online submission form, please include a statement indicating that all underlying data are publicly available and described in the manuscript with relevant references.

2) Data availability: Please confirm that the data on prevalence rates of adolescent depression in France and in the United States are also publicly available. If so, please clearly state so.

3) Code availability: Because Github depositions can be readily changed or deleted, we encourage you to make a permanent DOI'd copy (e.g. in Zenodo) and provide the URL.

4) Figure 5: Please add an x-axis label.

5) Please note that the headings (and subheadings) are still numbered. Please remove.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email (including the editorial requests above). Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process. 

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. 

Sincerely, 

Alexandra Tosun, PhD 

Senior Editor 

PLOS Medicine