PONE-D-25-31736Implementation of a marketing plan for the dissemination of the WHO SkinNTDs app in CameroonPLOS ONE

Dear Dr. Moungui, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Comments

Reviewer 1

This is a well-written and timely manuscript addressing an important issue: the dissemination of a digital health tool to improve sNTD diagnosis in a low-resource setting. The implementation of a marketing strategy tailored to frontline health workers (FHWs) in Cameroon provides a valuable contribution to the literature on mHealth interventions in LMIC contexts.

However, the paper in its current form presents several methodological limitations and reporting gaps that limit the validity and generalizability of the conclusions.

1. Short Follow-Up Period

The follow-up period post-intervention is limited to six months. This is insufficient to assess the long-term sustainability of the intervention, especially since most gains appear concentrated in the campaign phase. A longer follow-up or at least a more detailed discussion of sustainability challenges is warranted.

2. Population Selection Bias and Observer Effect

The manuscript acknowledges the possibility that some users may not be FHWs, but this issue is underexplored. Additionally, since many participants were actively engaged through WhatsApp and in-person presentations, there is a high risk of observer effect (Hawthorne effect), where participants modify behavior due to awareness of being observed, which is not discussed sufficiently.

3. Lack of a Control Group

Without a control group (e.g., a comparable region where no dissemination activities were conducted), it is difficult to attribute observed increases in app downloads or retention to the intervention rather than external factors or secular trends. A stronger justification of the quasi-experimental design, or acknowledgment of this limitation, is necessary.

4. Exclusive Reliance on Google Play Data

All quantitative app engagement metrics are derived exclusively from Google Play Console, ignoring iOS users and informal app-sharing mechanisms common in Low- and Middle-Income Countries settings (e.g., Xender). This likely results in underreporting of actual app adoption. The authors should clarify the proportion of Android vs. iOS devices in the target population and better justify the reliance on Android data alone.

5. No Measure of Clinical Impact

The study focuses entirely on process outcomes (downloads, installs, retention) without assessing any impact on clinical practice or health outcomes (e.g., improved diagnosis or reporting of sNTDs). The absence of even a basic qualitative assessment of changes in practice is a significant limitation that must be more thoroughly acknowledged.

6. No Cost-Effectiveness Analysis

There is no data on cost per download, cost per retained user, or resource expenditure relative to outcomes. Given the relevance of cost-effectiveness for decision-making in LMIC health systems, this represents a missed opportunity. At minimum, the authors should discuss this limitation more openly. While the app is freely available, the implementation of the marketing strategy incurred costs (staff salaries, transportation, in-person events, communication materials). The paper does not account for these costs relative to key outcomes (e.g., cost per additional active user, cost per retained user). Without this, policymakers in similar settings cannot assess the affordability or scalability of the approach. I recommend either a basic cost analysis or a transparent acknowledgment of this omission in the limitations.

7. Use of Basic Statistical Tools

The choice of Microsoft Excel and Jamovi for statistical analysis raises concerns about analytical robustness. While acceptable for basic ANOVA and non-parametric tests, the use of more sophisticated statistical packages (e.g., R or STATA) could enable richer analyses such as time-series modeling or interrupted time series designs, which seem more appropriate for before-after quasi-experimental data.

8. Lack of Clarity on p-Value Correction

The manuscript does not sufficiently describe whether p-value corrections for multiple testing were applied, especially given multiple outcome metrics. While the use of Dwass-Steel-Critchlow-Fligner tests is appropriate for pairwise comparisons, there is no clarity on overall correction for Type I error across multiple comparisons, which is critical in studies with numerous endpoints.

9. Inadequate Insight on WhatsApp Group Dynamics

The paper rightly identifies WhatsApp as a key engagement tool, yet offers limited analysis of group activity (e.g., participation rates, message frequency, engagement levels over time). Some quantitative or qualitative reporting on WhatsApp engagement would substantially strengthen the insights about sustained app use.

Additional Observations:

Reference [3] appears in the reference list but is not cited in the main text. This should be corrected.

Some sentences would benefit from minor grammatical editing for conciseness and clarity.

Statistical significance is sometimes highlighted without consistent emphasis on effect sizes or practical significance, which would be more informative to readers.

Reviewer 2

The description of the marketing plan outlines various dissemination channels (WhatsApp, printed posters, face‐to‐face training, emails) but does not anchor these activities in a recognized health behaviour or communication theory (e.g., Health Belief Model, Diffusion of Innovations). Explicitly identify and describe a theoretical framework that guided message development and channel selection. This will strengthen the rationale for why certain campaign components were expected to influence FHW knowledge, attitudes, or practices.

¾ Although a “marketing plan” is mentioned, there is no clear statement of SMART (Specific, Measurable, Achievable, Relevant, Time‐bound) objectives (e.g., “Increase app downloads among FHWs in Region X by 25% within three months”).Include a table listing precise campaign objectives (e.g., target reach, frequency of message exposure, engagement metrics) and corresponding process and outcome indicators (e.g., number of posters displayed, number of WhatsApp broadcasts sent, click‐through rates).

¾ The methods lack any mention of formative assessment (e.g., focus groups, key‐informant interviews) or pretesting of messages/materials with the target audience before full rollout. Describe any formative work undertaken to tailor content (language, visuals) to the local context and document how A/B testing informed final message versions. If no formative research was done, discuss this as a limitation.

¾ There is no information on how the fidelity (i.e., whether activities were delivered as planned) or actual exposure of participants to each channel was monitored. Detail procedures for tracking campaign delivery (e.g., logs of WhatsApp broadcast times, attendance sheets for workshops, distribution records for posters) and how exposure data were linked to individual app‐usage metrics.

¾ The before–after design without a contemporaneous control group is vulnerable to secular trends (e.g., global publicity, other local NTD initiatives) that might influence app uptake.Acknowledge and discuss potential confounders (e.g., simultaneous WHO or NGO activities), and consider whether any comparison districts without targeted marketing could have been used as controls.

¾ The methods state that “videos and presentations” were used, but offer no specifics on content length, format, language, or whether materials were adapted for literacy levels. Provide sample outlines or screenshots of videos/slides (e.g., durations, main messages), and describe how materials were localized (translations, pictograms) for FHW audiences.

¾ While the use of ANOVA and non‐parametric tests is described, there is no mention of how missing data (e.g., uninstalls, silent non-users) were handled or whether any adjustments were made for multiple comparisons across three periods.Specify data-cleaning rules, handling of outliers, and correction methods (e.g., Bonferroni) if multiple pairwise contrasts were conducted. Consider presenting an a priori power calculation for detecting meaningful changes in download or retention rates.

¾ The six‐month pre-, during-, and post-campaign windows are clear, but dates in the text (e.g., “October 2024–March 2025”) should be formatted consistently (e.g., “1 October 2024 to 31 March 2025”).

¾ Provide specifics on how A/B tests were designed (randomization process, sample sizes per variant, success criteria), and indicate which version emerged as optimal.

¾ Explain how frontline health workers were identified and invited to participate in the campaign—was it purposive, convenience, or exhaustive sampling within target districts?

==============================

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Comments

Reviewer 1

This is a well-written and timely manuscript addressing an important issue: the dissemination of a digital health tool to improve sNTD diagnosis in a low-resource setting. The implementation of a marketing strategy tailored to frontline health workers (FHWs) in Cameroon provides a valuable contribution to the literature on mHealth interventions in LMIC contexts.

However, the paper in its current form presents several methodological limitations and reporting gaps that limit the validity and generalizability of the conclusions.

1. Short Follow-Up Period

The follow-up period post-intervention is limited to six months. This is insufficient to assess the long-term sustainability of the intervention, especially since most gains appear concentrated in the campaign phase. A longer follow-up or at least a more detailed discussion of sustainability challenges is warranted.

2. Population Selection Bias and Observer Effect

The manuscript acknowledges the possibility that some users may not be FHWs, but this issue is underexplored. Additionally, since many participants were actively engaged through WhatsApp and in-person presentations, there is a high risk of observer effect (Hawthorne effect), where participants modify behavior due to awareness of being observed, which is not discussed sufficiently.

3. Lack of a Control Group

Without a control group (e.g., a comparable region where no dissemination activities were conducted), it is difficult to attribute observed increases in app downloads or retention to the intervention rather than external factors or secular trends. A stronger justification of the quasi-experimental design, or acknowledgment of this limitation, is necessary.

4. Exclusive Reliance on Google Play Data

All quantitative app engagement metrics are derived exclusively from Google Play Console, ignoring iOS users and informal app-sharing mechanisms common in Low- and Middle-Income Countries settings (e.g., Xender). This likely results in underreporting of actual app adoption. The authors should clarify the proportion of Android vs. iOS devices in the target population and better justify the reliance on Android data alone.

5. No Measure of Clinical Impact

The study focuses entirely on process outcomes (downloads, installs, retention) without assessing any impact on clinical practice or health outcomes (e.g., improved diagnosis or reporting of sNTDs). The absence of even a basic qualitative assessment of changes in practice is a significant limitation that must be more thoroughly acknowledged.

6. No Cost-Effectiveness Analysis

There is no data on cost per download, cost per retained user, or resource expenditure relative to outcomes. Given the relevance of cost-effectiveness for decision-making in LMIC health systems, this represents a missed opportunity. At minimum, the authors should discuss this limitation more openly. While the app is freely available, the implementation of the marketing strategy incurred costs (staff salaries, transportation, in-person events, communication materials). The paper does not account for these costs relative to key outcomes (e.g., cost per additional active user, cost per retained user). Without this, policymakers in similar settings cannot assess the affordability or scalability of the approach. I recommend either a basic cost analysis or a transparent acknowledgment of this omission in the limitations.

7. Use of Basic Statistical Tools

The choice of Microsoft Excel and Jamovi for statistical analysis raises concerns about analytical robustness. While acceptable for basic ANOVA and non-parametric tests, the use of more sophisticated statistical packages (e.g., R or STATA) could enable richer analyses such as time-series modeling or interrupted time series designs, which seem more appropriate for before-after quasi-experimental data.

8. Lack of Clarity on p-Value Correction

The manuscript does not sufficiently describe whether p-value corrections for multiple testing were applied, especially given multiple outcome metrics. While the use of Dwass-Steel-Critchlow-Fligner tests is appropriate for pairwise comparisons, there is no clarity on overall correction for Type I error across multiple comparisons, which is critical in studies with numerous endpoints.

9. Inadequate Insight on WhatsApp Group Dynamics

The paper rightly identifies WhatsApp as a key engagement tool, yet offers limited analysis of group activity (e.g., participation rates, message frequency, engagement levels over time). Some quantitative or qualitative reporting on WhatsApp engagement would substantially strengthen the insights about sustained app use.

Additional Observations:

Reference [3] appears in the reference list but is not cited in the main text. This should be corrected.

Some sentences would benefit from minor grammatical editing for conciseness and clarity.

Statistical significance is sometimes highlighted without consistent emphasis on effect sizes or practical significance, which would be more informative to readers.

Reviewer 2

The description of the marketing plan outlines various dissemination channels (WhatsApp, printed posters, face‐to‐face training, emails) but does not anchor these activities in a recognized health behaviour or communication theory (e.g., Health Belief Model, Diffusion of Innovations). Explicitly identify and describe a theoretical framework that guided message development and channel selection. This will strengthen the rationale for why certain campaign components were expected to influence FHW knowledge, attitudes, or practices.

� Although a “marketing plan” is mentioned, there is no clear statement of SMART (Specific, Measurable, Achievable, Relevant, Time‐bound) objectives (e.g., “Increase app downloads among FHWs in Region X by 25% within three months”).Include a table listing precise campaign objectives (e.g., target reach, frequency of message exposure, engagement metrics) and corresponding process and outcome indicators (e.g., number of posters displayed, number of WhatsApp broadcasts sent, click‐through rates).

� The methods lack any mention of formative assessment (e.g., focus groups, key‐informant interviews) or pretesting of messages/materials with the target audience before full rollout. Describe any formative work undertaken to tailor content (language, visuals) to the local context and document how A/B testing informed final message versions. If no formative research was done, discuss this as a limitation.

� There is no information on how the fidelity (i.e., whether activities were delivered as planned) or actual exposure of participants to each channel was monitored. Detail procedures for tracking campaign delivery (e.g., logs of WhatsApp broadcast times, attendance sheets for workshops, distribution records for posters) and how exposure data were linked to individual app‐usage metrics.

� The before–after design without a contemporaneous control group is vulnerable to secular trends (e.g., global publicity, other local NTD initiatives) that might influence app uptake.Acknowledge and discuss potential confounders (e.g., simultaneous WHO or NGO activities), and consider whether any comparison districts without targeted marketing could have been used as controls.

� The methods state that “videos and presentations” were used, but offer no specifics on content length, format, language, or whether materials were adapted for literacy levels. Provide sample outlines or screenshots of videos/slides (e.g., durations, main messages), and describe how materials were localized (translations, pictograms) for FHW audiences.

� While the use of ANOVA and non‐parametric tests is described, there is no mention of how missing data (e.g., uninstalls, silent non-users) were handled or whether any adjustments were made for multiple comparisons across three periods.Specify data-cleaning rules, handling of outliers, and correction methods (e.g., Bonferroni) if multiple pairwise contrasts were conducted. Consider presenting an a priori power calculation for detecting meaningful changes in download or retention rates.

� The six‐month pre-, during-, and post-campaign windows are clear, but dates in the text (e.g., “October 2024–March 2025”) should be formatted consistently (e.g., “1 October 2024 to 31 March 2025”).

� Provide specifics on how A/B tests were designed (randomization process, sample sizes per variant, success criteria), and indicate which version emerged as optimal.

� Explain how frontline health workers were identified and invited to participate in the campaign—was it purposive, convenience, or exhaustive sampling within target districts?

[Note: HTML markup is below. Please do not edit.]

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: Partly

Reviewer #2: Partly

**********

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

Reviewer #1: No

Reviewer #2: I Don't Know

**********

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

**********

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

**********

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: This is a well-written and timely manuscript addressing an important issue: the dissemination of a digital health tool to improve sNTD diagnosis in a low-resource setting. The implementation of a marketing strategy tailored to frontline health workers (FHWs) in Cameroon provides a valuable contribution to the literature on mHealth interventions in LMIC contexts.

However, the paper in its current form presents several methodological limitations and reporting gaps that limit the validity and generalizability of the conclusions.

1. Short Follow-Up Period

The follow-up period post-intervention is limited to six months. This is insufficient to assess the long-term sustainability of the intervention, especially since most gains appear concentrated in the campaign phase. A longer follow-up or at least a more detailed discussion of sustainability challenges is warranted.

2. Population Selection Bias and Observer Effect

The manuscript acknowledges the possibility that some users may not be FHWs, but this issue is underexplored. Additionally, since many participants were actively engaged through WhatsApp and in-person presentations, there is a high risk of observer effect (Hawthorne effect), where participants modify behavior due to awareness of being observed, which is not discussed sufficiently.

3. Lack of a Control Group

Without a control group (e.g., a comparable region where no dissemination activities were conducted), it is difficult to attribute observed increases in app downloads or retention to the intervention rather than external factors or secular trends. A stronger justification of the quasi-experimental design, or acknowledgment of this limitation, is necessary.

4. Exclusive Reliance on Google Play Data

All quantitative app engagement metrics are derived exclusively from Google Play Console, ignoring iOS users and informal app-sharing mechanisms common in Low- and Middle-Income Countries settings (e.g., Xender). This likely results in underreporting of actual app adoption. The authors should clarify the proportion of Android vs. iOS devices in the target population and better justify the reliance on Android data alone.

5. No Measure of Clinical Impact

The study focuses entirely on process outcomes (downloads, installs, retention) without assessing any impact on clinical practice or health outcomes (e.g., improved diagnosis or reporting of sNTDs). The absence of even a basic qualitative assessment of changes in practice is a significant limitation that must be more thoroughly acknowledged.

6. No Cost-Effectiveness Analysis

There is no data on cost per download, cost per retained user, or resource expenditure relative to outcomes. Given the relevance of cost-effectiveness for decision-making in LMIC health systems, this represents a missed opportunity. At minimum, the authors should discuss this limitation more openly. While the app is freely available, the implementation of the marketing strategy incurred costs (staff salaries, transportation, in-person events, communication materials). The paper does not account for these costs relative to key outcomes (e.g., cost per additional active user, cost per retained user). Without this, policymakers in similar settings cannot assess the affordability or scalability of the approach. I recommend either a basic cost analysis or a transparent acknowledgment of this omission in the limitations.

7. Use of Basic Statistical Tools

The choice of Microsoft Excel and Jamovi for statistical analysis raises concerns about analytical robustness. While acceptable for basic ANOVA and non-parametric tests, the use of more sophisticated statistical packages (e.g., R or STATA) could enable richer analyses such as time-series modeling or interrupted time series designs, which seem more appropriate for before-after quasi-experimental data.

8. Lack of Clarity on p-Value Correction

The manuscript does not sufficiently describe whether p-value corrections for multiple testing were applied, especially given multiple outcome metrics. While the use of Dwass-Steel-Critchlow-Fligner tests is appropriate for pairwise comparisons, there is no clarity on overall correction for Type I error across multiple comparisons, which is critical in studies with numerous endpoints.

9. Inadequate Insight on WhatsApp Group Dynamics

The paper rightly identifies WhatsApp as a key engagement tool, yet offers limited analysis of group activity (e.g., participation rates, message frequency, engagement levels over time). Some quantitative or qualitative reporting on WhatsApp engagement would substantially strengthen the insights about sustained app use.

Additional Observations:

Reference [3] appears in the reference list but is not cited in the main text. This should be corrected.

Some sentences would benefit from minor grammatical editing for conciseness and clarity.

Statistical significance is sometimes highlighted without consistent emphasis on effect sizes or practical significance, which would be more informative to readers.

Reviewer #2: � The description of the marketing plan outlines various dissemination channels (WhatsApp, printed posters, face‐to‐face training, emails) but does not anchor these activities in a recognized health behaviour or communication theory (e.g., Health Belief Model, Diffusion of Innovations). Explicitly identify and describe a theoretical framework that guided message development and channel selection. This will strengthen the rationale for why certain campaign components were expected to influence FHW knowledge, attitudes, or practices.

� Although a “marketing plan” is mentioned, there is no clear statement of SMART (Specific, Measurable, Achievable, Relevant, Time‐bound) objectives (e.g., “Increase app downloads among FHWs in Region X by 25% within three months”).Include a table listing precise campaign objectives (e.g., target reach, frequency of message exposure, engagement metrics) and corresponding process and outcome indicators (e.g., number of posters displayed, number of WhatsApp broadcasts sent, click‐through rates).

� The methods lack any mention of formative assessment (e.g., focus groups, key‐informant interviews) or pretesting of messages/materials with the target audience before full rollout. Describe any formative work undertaken to tailor content (language, visuals) to the local context and document how A/B testing informed final message versions. If no formative research was done, discuss this as a limitation.

� There is no information on how the fidelity (i.e., whether activities were delivered as planned) or actual exposure of participants to each channel was monitored. Detail procedures for tracking campaign delivery (e.g., logs of WhatsApp broadcast times, attendance sheets for workshops, distribution records for posters) and how exposure data were linked to individual app‐usage metrics.

� The before–after design without a contemporaneous control group is vulnerable to secular trends (e.g., global publicity, other local NTD initiatives) that might influence app uptake.Acknowledge and discuss potential confounders (e.g., simultaneous WHO or NGO activities), and consider whether any comparison districts without targeted marketing could have been used as controls.

� The methods state that “videos and presentations” were used, but offer no specifics on content length, format, language, or whether materials were adapted for literacy levels. Provide sample outlines or screenshots of videos/slides (e.g., durations, main messages), and describe how materials were localized (translations, pictograms) for FHW audiences.

� While the use of ANOVA and non‐parametric tests is described, there is no mention of how missing data (e.g., uninstalls, silent non-users) were handled or whether any adjustments were made for multiple comparisons across three periods.Specify data-cleaning rules, handling of outliers, and correction methods (e.g., Bonferroni) if multiple pairwise contrasts were conducted. Consider presenting an a priori power calculation for detecting meaningful changes in download or retention rates.

� The six‐month pre-, during-, and post-campaign windows are clear, but dates in the text (e.g., “October 2024–March 2025”) should be formatted consistently (e.g., “1 October 2024 to 31 March 2025”).

� Provide specifics on how A/B tests were designed (randomization process, sample sizes per variant, success criteria), and indicate which version emerged as optimal.

� Explain how frontline health workers were identified and invited to participate in the campaign—was it purposive, convenience, or exhaustive sampling within target districts?

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PONE-D-25-31736R1Implementation of a marketing plan for the dissemination of the WHO SkinNTDs app in CameroonPLOS ONE

Dear Dr.  Moungui,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Oct 16 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
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• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

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We look forward to receiving your revised manuscript.

Kind regards,

Fatemeh Zarei, PhD

Academic Editor

PLOS ONE

Journal Requirements:

1.If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. 

2. 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. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear Henri Claude Moungui Thanks for your excellent efforts. Please consider the remain comment(s) .  

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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 #1: (No Response)

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 #1: Yes

Reviewer #2: Yes

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

Reviewer #1: No

Reviewer #2: I Don't Know

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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 #1: Yes

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 #1: Yes

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 #1: Thanks for the thorough revision, you’ve addressed the prior concerns and appropriately stated the limitations. One point remains unclear: You report using Tukey’s HSD (after ANOVA) and DSCF (after Kruskal–Wallis), which controls Type I error within each outcome’s pairwise comparisons. Please clarify whether you also applied multiplicity control across outcomes (e.g., growth, churn, retention, loss, device conversion). Add one explicit sentence in Methods (and mirror it in Limitations, if applicable) stating whether across-outcome correction was or was not performed. (Optional: a brief sensitivity note, e.g., Holm or FDR across omnibus p-values, would be welcome but is not required.). With this addition, the manuscript will be acceptable for publication.

Reviewer #2: Thank you, authors, for your efforts to enhance the manuscript according to the comments. It seems that all comments addressed

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Reviewer #1: Yes: Andrea Gulli

Reviewer #2: Yes: Fatemeh Zarei

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Implementation of a marketing plan for the dissemination of the WHO SkinNTDs app in Cameroon

PONE-D-25-31736R2

Dear Dr.  Henri Claude Moungui

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Fatemeh Zarei, PhD

Academic Editor

PLOS ONE