Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation

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Mobile health (mHealth) interventions, which are healthcare technology utilizing mobile or other wireless technology, promise enhanced early detection by optimising lung cancer screening (LCS) implementation. However, their efficacy across various patient demographics and the underlying mechanisms that influence LCS success remain unclear and underexplored. Aim To explore the efficacy of mHealth interventions in promoting LCS uptake, focusing on patient demographics, intervention characteristics, and the underlying mechanisms and contexts influencing their effectiveness. Methods This realist review will employ an iterative literature search in databases such as PubMed, Scopus, Web of Science, and Embase. Selected studies will be assessed for relevance and rigour, extracting data on mHealth features, patient demographics, and intervention outcomes. Data will be analysed thematically to describe relationships between intervention mechanisms, contexts, and outcomes. Additionally, engagement from key stakeholders, including health experts and patients, will be sought during the synthesis phase. Conclusion This review aims to offer a comprehensive understanding of how and why mHealth interventions can influence LCS uptake and be effective across different patient demographics. These findings will provide insights into optimising mHealth interventions for LCS, potentially leading to earlier detections and improved patient outcomes. 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HRB Open Res 2025, 8 :12 ( https://doi.org/10.12688/hrbopenres.13926.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Study Protocol Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] Selena Gong 1 , Benjamin Jacob https://orcid.org/0000-0003-1119-064X 1 , Áine Harris 1 , [...] Kanishka Raval https://orcid.org/0009-0009-9564-5891 1 , Nick Clarke 2 , Frank Doyle https://orcid.org/0000-0002-3785-7433 2 , Alan Smith 3 , Seamus Cotter https://orcid.org/0009-0000-5680-2772 4 , Killian Walsh 5 , Patrick Redmond 1 Selena Gong 1 , Benjamin Jacob https://orcid.org/0000-0003-1119-064X 1 , [...] Áine Harris 1 , Kanishka Raval https://orcid.org/0009-0009-9564-5891 1 , Nick Clarke 2 , Frank Doyle https://orcid.org/0000-0002-3785-7433 2 , Alan Smith 3 , Seamus Cotter https://orcid.org/0009-0000-5680-2772 4 , Killian Walsh 5 , Patrick Redmond 1 PUBLISHED 24 Jan 2025 Author details Author details 1 Dept. of General Practice, RCSI University of Medicine and Health Sciences, Dublin, Leinster, Ireland 2 School of Population Health, RCSI University of Medicine and Health Sciences, Dublin, Leinster, Ireland 3 National Screening Service, Health Service Executive, County Dublin, County Dublin, Ireland 4 Irish Lung Cancer Community, Dublin, Ireland 5 Information Specialist, RCSI University of Medicine and Health Sciences, Dublin, Leinster, Ireland Selena Gong Roles: Writing – Original Draft Preparation, Writing – Review & Editing Benjamin Jacob Roles: Conceptualization, Writing – Review & Editing Áine Harris Roles: Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Kanishka Raval Roles: Writing – Original Draft Preparation, Writing – Review & Editing Nick Clarke Roles: Writing – Review & Editing Frank Doyle Roles: Writing – Review & Editing Alan Smith Roles: Writing – Review & Editing Seamus Cotter Roles: Writing – Review & Editing Killian Walsh Roles: Methodology Patrick Redmond Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Introduction Lung cancer, the leading cause of cancer death worldwide, is often diagnosed at advanced stages leading to a poorer prognosis. Mobile health (mHealth) interventions, which are healthcare technology utilizing mobile or other wireless technology, promise enhanced early detection by optimising lung cancer screening (LCS) implementation. However, their efficacy across various patient demographics and the underlying mechanisms that influence LCS success remain unclear and underexplored. Aim To explore the efficacy of mHealth interventions in promoting LCS uptake, focusing on patient demographics, intervention characteristics, and the underlying mechanisms and contexts influencing their effectiveness. Methods This realist review will employ an iterative literature search in databases such as PubMed, Scopus, Web of Science, and Embase. Selected studies will be assessed for relevance and rigour, extracting data on mHealth features, patient demographics, and intervention outcomes. Data will be analysed thematically to describe relationships between intervention mechanisms, contexts, and outcomes. Additionally, engagement from key stakeholders, including health experts and patients, will be sought during the synthesis phase. Conclusion This review aims to offer a comprehensive understanding of how and why mHealth interventions can influence LCS uptake and be effective across different patient demographics. These findings will provide insights into optimising mHealth interventions for LCS, potentially leading to earlier detections and improved patient outcomes. READ ALL READ LESS Keywords Mobile Applications, Lung neoplasms, Early detection of cancer, Cancer screening, Health Promotion, Realist Review Corresponding Author(s) Áine Harris ( [email protected] ) Close Corresponding author: Áine Harris Competing interests: No competing interests were disclosed. Grant information: Selena Gong’s involvement in this project was supported by a stipend from the 2024 RCSI Research Summer School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Gong S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Gong S, Jacob B, Harris Á et al. Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.12688/hrbopenres.13926.1 ) First published: 24 Jan 2025, 8 :12 ( https://doi.org/10.12688/hrbopenres.13926.1 ) Latest published: 05 Dec 2025, 8 :12 ( https://doi.org/10.12688/hrbopenres.13926.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction With 2.2 million new cases and 1.8 million deaths globally per year, lung cancer is the second most common cancer and the leading cause of cancer death worldwide ( Sung et al. , 2021 ). A significant reason for its high mortality is delayed diagnosis, with lung cancer commonly diagnosed at an advanced stage ( Walter et al. , 2015 ). This results in a poorer prognosis, with lung cancer in the UK having a 1-year survival rate of 85% for Stage I disease versus just 25% for Stage IV disease ( “NLCA annual report,” 2022 ). Later stage lung cancer diagnosis is multifaceted due to disease, patient, and provider related factors. Symptoms particular to lung cancer may be vague in their presentation and not directly reflect chest or lung symptoms (e.g., fatigue and weight loss). Asymptomatic early lung cancer disease can contribute to delayed diagnoses and more advanced symptomatic disease at presentation ( Walter et al. , 2015 ). More than two thirds of lung cancer is diagnosed at stage three or four ( McPhail et al. , 2015 ). Delayed patient presentation may occur if the patient is unaware of the potential significance of their symptoms, misinterprets them, or is fearful of the consequences of a diagnosis ( Cassim et al. , 2019 ; Weller et al. , 2019 ). Healthcare related factors include misdiagnosis or challenging diagnosis due to unclear symptoms and delays due to inefficiencies in healthcare systems ( Guirado et al. , 2022 ; Newman-Toker et al. , 2020 ). Lung cancer screening The UK National Screening Committee, the US Preventative Task Force, and the European Union position statements all currently recommend targeted lung cancer screening with low-dose computer tomography (LDCT) for those at high risk of lung cancer ( Jonas et al. , 2021 ; Mahase, 2023 ; Oudkerk et al. , 2017 ). However, the success of screening uptake is compromised by uptake at various stages of the cancer screening pathway such as participant influences, screening behaviour processes, and environmental influences ( Figure 1 ) ( Robb, 2021 ). Fear, fatalism, and stigma may be present in at-risk communities ( Quaife et al. , 2018 ). Primary care providers may face difficulties in identifying and inviting at-risk individuals for further testing and examinations and making informed decisions with support from multidisciplinary teams. Furthermore, ethnic and regional inequalities persist, underscoring the likely need for tailored approaches to improve LCS participation. Figure 1. Integrated screening action model I-SAM (This figure has been reproduced with permission from Robb, 2021 ). mHealth The World Health Organisation defines mHealth as medical and public health practices supported by mobile and other wireless devices ( WHO Guideline, 2019 ). With over 5.3 billion mobile phone users globally, mHealth provides unique ability to reach a great proportion of users instantaneously through voice, text (SMS), video and other multimedia services ( Marcolino et al. , 2018 ; Sereno et al. , 2023 ). MHealth interventions encompass patient education, health behaviour change communication, data collection, provider training, etc. These interventions have demonstrated positive impacts across various diseases. For cancer specifically, mHealth has been utilised for self-care, self-management, and behaviour change among cancer survivors ( Vaffis et al. , 2023 ). However, notably in Lung Cancer specific apps – most are focused on treatment/survivorship, and none are integrated with existing health records. Evidence on mHealth in cancer screening Existing reviews suggest mHealth interventions can effectively boost breast cervical, colorectal, prostate, or lung cancer screening uptake, knowledge, and awareness ( Ruco et al. , 2021 ). A variety of apps and interventions have been developed to support either cancer screening in general, such as the ePrognosis Cancer screening app ( Kotwal & Walter, 2020 ), or specific types of cancer, such as GLAm for cervical cancer screening ( Wanberg et al. , 2023 ). A recent scoping meta-review found 67 mobile interventions, of which 57 (85%) targeted breast and cervical cancer awareness and screening uptake ( Schliemann et al. , 2022 ). Overall, these mHealth interventions were found to increase cancer screening uptake, most commonly through SMS and telephone calls. Lung cancer and mHealth The evidence for mHealth to support LCS is less developed ( Table 1 ). A systematic review on mobile health in cancer screening reported only one study out of 23 examining a smartphone application for lung cancer screening ( Salmani et al. , 2020 ; Szanto et al. , 2017 ). Szanto et al. , described the development of a smartphone application that assessed lung cancer risk and directed high-risk individuals to screening centres based on their geographic location. Notably however, this did not integrate with the patient’s medical record ( Szanto et al. , 2017 ). Sereno et al. , found that the mHealth ALIBIRD platform, a remote app for recording symptoms, lifestyle and sleep patterns among patients, helped promote healthy lifestyle and patient empowerment, while supporting clinician recommendations. Ahern et al. , deployed the Lung Age app, for primary care lung function assessments ( Ahern et al. , 2016 ). This application was downloaded by a large number of users, indicating the potential reach and usefulness of this approach. Lu et al. highlight that while there are various symptom tracking apps available, there is a need for collaboration between oncologists, app developers, and patients to optimise patient-reported outcomes (PROs) assessment ( Lu et al. , 2021 ). Dhar et al. , identified factors such as guided supervision, personalised suggestions, and theoretical intervention foundations that can enhance adherence and efficacy of mHealth interventions in cancer care management ( Dhar et al. , 2023 ). Ardito highlighted the importance of multistakeholder co-design and testing of mHealth interventions, as well as addressing patient needs as a key incentive for mHealth use ( Ardito et al. , 2023 ). Table 1. Commonly used Lung Cancer Screening Risk Assessment Tools. Risk Assessment Tool Variables Considered Notable Features PLCOm2012 ( National Lung Screening Trial Research Team et al. , 2011 ) Age, race, smoking history, personal health history Widely validated; used in US-based studies LLP (Liverpool Lung Project) ( Marcus et al. , 2015 ) Age, smoking duration, personal/family history of cancer Developed for the UK population NLST Criteria ( Tammemägi et al. , 2013 ) Age, smoking pack-years, years since quitting Criteria from the National Lung Screening Trial Bach Model ( Hoggart et al. , 2012 ) Age, smoking history, exposure to asbestos, personal/family history of cancer Considers occupational exposure ELCAP (Early Lung Cancer Action Project) ( Henschke et al. , 1999 ) Age, smoking history Simplified criteria; focuses on early detection Ultimately, integration of mHealth apps with personal medical records would allow for a seamless flow of information between patients, healthcare providers, and the screening platforms. This integration could ensure that relevant health data is accurately captured and available to healthcare providers, enhancing the decision-making process and personalising the patient's care journey. Given the significance of early lung cancer detection and the rapid expansion of mHealth, an understanding of its effectiveness in promoting LCS is needed. This synthesis will enhance current knowledge on patient demographics, intervention efficacy, and underlying mechanisms. Aims & objectives The aim of this review is to explore the associations between patient demographics, characteristics of mHealth interventions, and the underlying mechanisms and contexts that influence the efficacy of eHealth interventions in promoting lung cancer screening (LCS). Specific objectives include: 1. To identify the patient demographic factors that influence the efficacy of mHealth interventions in promoting LCS. 2. To determine the specific characteristics of mHealth interventions that are effective across different demographic groups. 3. To discern the underlying mechanisms, theories, beliefs, and contexts that influence the effectiveness of mHealth interventions in promoting LCS. Methods Design and rationale Adhering to the RAMESES publication standards for realist syntheses, this study will utilise a realist synthesis approach to comprehensively examine the interplay between mHealth interventions and their efficacy in promoting lung cancer screening (LCS) ( Wong et al. , 2013 ). Synthesis of study data will focus on extracting data relevant to the underlying mechanisms, theories, contexts, and factors that determine the success of the interventions in question ( Duddy & Wong, 2023 ). We will report the findings in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( Page et al. , 2021 ). The protocol and any subsequent amendments will be prospectively registered with PROSPERO. Given the nature of mHealth interventions and the multiple factors influencing their success, a realist synthesis approach is appropriate. Scoping the literature An initial exploratory scoping of the literature will be undertaken to identify key theories, concepts, and existing evidence related to mHealth interventions in LCS. Search strategy Development of search terms. To ensure a comprehensive and systematic retrieval of relevant literature, our search strategy is designed with a combination of subject heading terms, such as Medical Subject Headings (MeSH), and relevant free-text keywords. These terms and keywords identified are based on preliminary scans of the literature, consultation with experts in the field, and alignment with our review objectives. Themes and keywords. Our search strategy will be focusing on three different terms: 1. Mhealth term including terms such as: ‘digital interventions’, ‘phone apps’, or ‘phone interventions’ 2. Lung cancer term including: ‘lung carcinoma’, or ‘pulmonary neoplasm’ 3. Screening terms such as: ‘early detection’ or ‘new diagnosis’ To see a sample search strategy, please see extended data. Database search. We will systematically search the following electronic databases: Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, MEDLINE, ProQuest Dissertation and Thesis Database, Scopus, Web of Science, and PsychInfo. Search strategy refinement. To ensure maximum yield of relevant articles, we have included an information specialist on our authorship team (KW). This specialist will help refine our search terms, ensure appropriate Boolean operators are applied, and adapt the strategy to the specific requirements of each database. Additional searches. Beyond the database searches, we will also manually search the reference lists of included studies and relevant reviews to ensure that all pertinent articles are captured. Grey literature, such as conference abstracts and reports, will also be considered to reduce publication bias. Selection and appraisal of documents Inclusion criteria: articles focusing on lung cancer screening interventions, published in English language. Exclusion criteria includes non-peer reviewed studies and interventions focusing on cancer outcome or management. Two authors will independently screen the title and abstract of all papers for relevance and rigour. Discrepancies will be discussed and resolved through consensus. Reviewers will then independently assess the full text of potentially relevant studies to determine whether they meet the inclusion criteria. In cases of uncertainty or disagreement, a third reviewer's opinion will be sought. All search results will be imported into a reference management software ( www.Rayyan.ai ) (Please also refer to https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-016-0384-4 ) to organise and screen titles and abstracts and remove duplicates ( Ouzzani et al. , 2016 ). This software will also facilitate the tracking of the selection process, which will be depicted in a PRISMA flow chart in the final review. Data extraction Data will be extracted from included studies by one reviewer and confirmed by a second reviewer using a pro forma specifically designed for the purpose. The extracted data will focus on mHealth features, patient demographics, intervention outcomes, and any context or mechanism information related to the efficacy of the intervention. To see an example form for data extraction, please see extended data. Analysis and synthesis processes Realist synthesis adopts a theory-driven approach to evidence synthesis. The process begins with the articulation of initial program theories that explain how mHealth interventions might work, for whom, and under what circumstances. These theories are then iteratively tested and refined against the evidence gathered from the included studies. 1. Initial program theories: Drawing from the literature and stakeholder consultations, we will propose tentative theories that suggest how mHealth interventions lead to their observed outcomes. These theories will elucidate the mechanisms that drive these outcomes and the contexts in which these mechanisms are triggered. 2. Testing and refining theories: As we gather evidence from the included studies, we will test the initial theories against this empirical data. This iterative process will involve: • Context-mechanism-outcome (CMO) configuration: Central to realist synthesis, we will identify and analyse any intervention contexts such as the social-economical-demographics of participants, psychological state and readiness to undertake the intervention, and the setting or modality of the intervention. Mechanisms are triggered by contexts, which lead to the outcomes of interest. Possible mechanisms include responses of participants and ease of access of interventions. Outcomes can include uptake, useability, health related outcomes, and any releated to the intervention ( De Souza, 2013 ; De Weger et al. , 2020 ). • Documenting Contradictions and Variations: Any variations in observed outcomes will be documented, and we will seek to explain these variations based on different contexts and mechanisms. 3. Synthesising Evidence: Themes and patterns will be identified that provide insights into when, why, and how mHealth interventions are effective (or ineffective) in promoting LCS across different patient demographics. This synthesis will result in refined program theories that offer a nuanced understanding of mHealth interventions. Quality appraisal and risk of bias within studies While realist synthesis values richness and relevance of data over traditional hierarchies of evidence, it is essential to appraise the quality of the included studies. 1. Relevance assessment: Does the study provide data on the contexts, mechanisms, or outcomes of interest? 2. Rigor assessment: Was the study designed and conducted in a way that provides credible insights into the CMO configurations? In addition to assessing relevance and rigour Risk of Bias will also be formally assessed: Risk of bias: Depending on the study type, we will use the ROBINS-E (“Risk Of Bias In Non-randomised Studies of Exposures”) tool combined with RoB2 for quantitative studies, or the Joanna Briggs Institue tool for qualitative studies, to informally assess the risk of bias within studies ( Higgins et al. , 2024 ; Munn et al. , 2020 ; Sterne et al. , 2019 ). Two reviewers will assess bias, with discrepancies resolved by discussion with a third reviewer. Data synthesis. Given the narrative and theory-driven nature of realist syntheses; traditional meta-analysis might not be directly applicable. However, when the data allows, we may undertake: 1. Narrative synthesis of quantitative and qualitative data: This involves summarising the findings from different studies to provide an overview of the evidence landscape. 2. Comparative analysis: If there are studies with comparable data points, comparative analyses might be conducted to identify patterns or trends across them. 3. Addressing heterogeneity: if significant heterogeneity exists in the findings, this will be explored qualitatively to understand the different contexts and mechanisms that might explain the variations between studies. Stakeholder engagement In line with realist methodology, key stakeholders, such as health experts, patients, and ICT professionals, will be engaged during various phases of the synthesis. Their insights will provide valuable contextual understanding and will be instrumental in refining the program theories. Discussion In line with our objectives, this realist synthesis seeks to explore and understand the relationship between patient demographics, beliefs, mHealth interventions, and underlying mechanisms influencing the efficacy of eHealth interventions in promoting lung cancer screening (LCS). By analysing the existing literature and synthesising key insights, we aim to provide a comprehensive overview of the factors that determine the success or failure of mHealth interventions in promoting LCS. Strengths and limitations The strength of this review lies in its realist approach, which moves beyond assessing mere efficacy to unpacking the 'why', 'when', and 'how' of mHealth interventions ( Rycroft-Malone et al. , 2012 ; Wong, 2018 ). By doing so, we hope to understand the mechanisms and contexts that shape outcomes. There are several limitations to our proposed approach. First, the dynamic nature of mHealth technology and its rapid evolution might mean that some interventions become outdated quickly. Second, cultural, geographical, and infrastructural differences may influence the generalisability of findings across different settings. Finally, the inherent complexity of realist syntheses, which draw from a wide range of sources and types of evidence, might introduce challenges in synthesising and interpreting findings. Comparison with existing literature While several studies and reviews have explored the potential of mHealth in various healthcare domains, few have specifically focused on its role in lung cancer screening ( Schliemann et al. , 2022 ). Preliminary scoping indicates that while there is growing interest in this area, comprehensive syntheses that integrate the nuances of patient demographics, intervention characteristics, and contextual factors are lacking. Implications for future research, policy, and clinical practice The findings from this synthesis have several implications. For researchers, it underlines the importance of considering the holistic context in which mHealth interventions are deployed. For policymakers and practitioners, understanding the mechanisms driving successful interventions can inform the design of future policies and national programmes. There is also potential in integrating mHealth data with other digital health platforms to provide a more comprehensive patient profile, aiding in risk assessment and early detection ( Najjar, 2024 ; Ogundaini et al. , 2021 ). Ultimately understanding the successful implementation of mHealth interventions can guide clinicians in recommending appropriate tools to patients ( Hamberger et al. , 2022 ), and supporting policymakers to design guidelines that promote effective mHealth strategies in lung cancer screening ( Barkman & Weinehall, 2017 ). Patient and Public Involvement The design, interpretation, and dissemination of our research will be done in consultation with the PRiCAN PPIE group as well as our patient co-author Mr Seamus Cotter – Chairperson of the Irish Lung Cancer Community. Ethics Research Ethics Committee approval is not required for this article. Data availability statement No data is associated with this article. Extended data OSF Repository: [Dataset] mhealth protocol for “Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation”. DOI: 10.17605/OSF.IO/4AXZB . Gong (2024) Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0) ( https://creativecommons.org/licenses/by/4.0/ )." Acknowledgements The authors would like to acknowledge Irsoon Hassan (RCSI University of Medicine and Health Sciences) for her work on a pilot search strategy and early version of the protocol. Faculty Opinions recommended References Ahern DK, Parker D, Eaton C, et al. : Patient-facing technology for identification of COPD in primary care. J Innov Health Inform. 2016; 23 (2): 824. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 24 Jan 2025 ADD YOUR COMMENT Comment Author details Author details 1 Dept. of General Practice, RCSI University of Medicine and Health Sciences, Dublin, Leinster, Ireland 2 School of Population Health, RCSI University of Medicine and Health Sciences, Dublin, Leinster, Ireland 3 National Screening Service, Health Service Executive, County Dublin, County Dublin, Ireland 4 Irish Lung Cancer Community, Dublin, Ireland 5 Information Specialist, RCSI University of Medicine and Health Sciences, Dublin, Leinster, Ireland Selena Gong Roles: Writing – Original Draft Preparation, Writing – Review & Editing Benjamin Jacob Roles: Conceptualization, Writing – Review & Editing Áine Harris Roles: Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Kanishka Raval Roles: Writing – Original Draft Preparation, Writing – Review & Editing Nick Clarke Roles: Writing – Review & Editing Frank Doyle Roles: Writing – Review & Editing Alan Smith Roles: Writing – Review & Editing Seamus Cotter Roles: Writing – Review & Editing Killian Walsh Roles: Methodology Patrick Redmond Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information Selena Gong’s involvement in this project was supported by a stipend from the 2024 RCSI Research Summer School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 05 Dec 2025, 8:12 https://doi.org/10.12688/hrbopenres.13926.2 version 1 Published: 24 Jan 2025, 8:12 https://doi.org/10.12688/hrbopenres.13926.1 Copyright © 2025 Gong S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics VIEWS $counts.viewCount downloads Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Gong S, Jacob B, Harris Á et al. Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.12688/hrbopenres.13926.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 24 Jan 2025 Views 0 Cite How to cite this report: Patkar S. Reviewer Report For: Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.21956/hrbopenres.15277.r46243 ) The direct URL for this report is: https://hrbopenresearch.org/articles/8-12/v1#referee-response-46243 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Apr 2025 Sushant Patkar , Artificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, USA Approved with Reservations VIEWS 0 https://doi.org/10.21956/hrbopenres.15277.r46243 This article charts out a plan to investigate how effective mobile health tools are at encouraging people to get screened earlier for lung cancer. Overall, the rationale is well-grounded in the need to improve early lung cancer detection, and the ... Continue reading READ ALL This article charts out a plan to investigate how effective mobile health tools are at encouraging people to get screened earlier for lung cancer. Overall, the rationale is well-grounded in the need to improve early lung cancer detection, and the objectives are clearly laid out to explore who mobile Health works for, how, and why. 1. Please describe in more depth how CMO configurations will be identified and coded. How many reviewers will be involved in data extraction and synthesis? Will inter-reviewer variability (Kappa scores) be assessed? 2. Please provide additional details along with references on tools (e.g., ROBINS-E, Joanna Briggs Institute tool), the authors plan to utilize to mitigate risks of selection bias and interpretive subjectivity in their study. Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Yes Are sufficient details of the methods provided to allow replication by others? Yes Are the datasets clearly presented in a useable and accessible format? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: genomics, precision oncology, lung cancer, artificial intelligence I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Patkar S. Reviewer Report For: Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.21956/hrbopenres.15277.r46243 ) The direct URL for this report is: https://hrbopenresearch.org/articles/8-12/v1#referee-response-46243 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 Dec 2025 Selena Gong , $usrAffiliation 05 Dec 2025 Author Response Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. While appraisal of included evidence is a core part of the realist synthesis, the ... Continue reading Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. While appraisal of included evidence is a core part of the realist synthesis, the approach differs from traditional reviews. The focus is upon the three pillars of relevance, richness, and rigour. Although the credibility of the findings within a paper are consider as part of the assessment of ‘rigour’, a strict risk of bias assessment does not form part of realist review methodology as the purpose of the evidence synthesis is to create explanatory theories rather than an unbiased quantification of effect size. (Reference 1). The methods section of the manuscript has been updated to reflect this and to explain in more detail the quality appraisal process as well as CMO configuration development. Kind regards, mHealth study team References: Dada S, Dalkin S, Gilmore B, Hunter R, Mukumbang FC. Applying and reporting relevance, richness and rigour in realist evidence appraisals: advancing key concepts in realist reviews. Research Synthesis Methods. 2023 May;14(3):504-14. Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. While appraisal of included evidence is a core part of the realist synthesis, the approach differs from traditional reviews. The focus is upon the three pillars of relevance, richness, and rigour. Although the credibility of the findings within a paper are consider as part of the assessment of ‘rigour’, a strict risk of bias assessment does not form part of realist review methodology as the purpose of the evidence synthesis is to create explanatory theories rather than an unbiased quantification of effect size. (Reference 1). The methods section of the manuscript has been updated to reflect this and to explain in more detail the quality appraisal process as well as CMO configuration development. Kind regards, mHealth study team References: Dada S, Dalkin S, Gilmore B, Hunter R, Mukumbang FC. Applying and reporting relevance, richness and rigour in realist evidence appraisals: advancing key concepts in realist reviews. Research Synthesis Methods. 2023 May;14(3):504-14. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 Dec 2025 Selena Gong , $usrAffiliation 05 Dec 2025 Author Response Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. While appraisal of included evidence is a core part of the realist synthesis, the ... Continue reading Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. While appraisal of included evidence is a core part of the realist synthesis, the approach differs from traditional reviews. The focus is upon the three pillars of relevance, richness, and rigour. Although the credibility of the findings within a paper are consider as part of the assessment of ‘rigour’, a strict risk of bias assessment does not form part of realist review methodology as the purpose of the evidence synthesis is to create explanatory theories rather than an unbiased quantification of effect size. (Reference 1). The methods section of the manuscript has been updated to reflect this and to explain in more detail the quality appraisal process as well as CMO configuration development. Kind regards, mHealth study team References: Dada S, Dalkin S, Gilmore B, Hunter R, Mukumbang FC. Applying and reporting relevance, richness and rigour in realist evidence appraisals: advancing key concepts in realist reviews. Research Synthesis Methods. 2023 May;14(3):504-14. Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. While appraisal of included evidence is a core part of the realist synthesis, the approach differs from traditional reviews. The focus is upon the three pillars of relevance, richness, and rigour. Although the credibility of the findings within a paper are consider as part of the assessment of ‘rigour’, a strict risk of bias assessment does not form part of realist review methodology as the purpose of the evidence synthesis is to create explanatory theories rather than an unbiased quantification of effect size. (Reference 1). The methods section of the manuscript has been updated to reflect this and to explain in more detail the quality appraisal process as well as CMO configuration development. Kind regards, mHealth study team References: Dada S, Dalkin S, Gilmore B, Hunter R, Mukumbang FC. Applying and reporting relevance, richness and rigour in realist evidence appraisals: advancing key concepts in realist reviews. Research Synthesis Methods. 2023 May;14(3):504-14. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Boudreau JH. Reviewer Report For: Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.21956/hrbopenres.15277.r45829 ) The direct URL for this report is: https://hrbopenresearch.org/articles/8-12/v1#referee-response-45829 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 22 Mar 2025 Jacqueline H Boudreau , Center for Healthcare Optimization & Implementation Research, VA Boston Healthcare System, VA Bedford Healthcare System, Boston, USA Approved with Reservations VIEWS 0 https://doi.org/10.21956/hrbopenres.15277.r45829 This is a very interesting, informative, and well-written protocol paper about using realist review to evaluate mHealth interventions for lung cancer screening implementation, which might lead to increased early detection and reduced mortality from lung cancer. I think this paper ... Continue reading READ ALL This is a very interesting, informative, and well-written protocol paper about using realist review to evaluate mHealth interventions for lung cancer screening implementation, which might lead to increased early detection and reduced mortality from lung cancer. I think this paper could benefit from additional and earlier rationale for the chosen method, particularly given its newness relative to more well-known review methods, which I think could be reframed as an asset to this paper if done thoroughly. Introduction - Lung cancer screening paragraph – “Furthermore, ethnic and regional inequalities persist, underscoring the likely need for tailored approaches to improve LCS participation.” Suggested citations (full disclosure- I am an author on one of them): Refer 1 and Refer 2 Lung cancer and mHealth – I am losing the thread in this section a little bit. It appears you are trying to make two main points: 1) That the evidence for mHealth to support LCS is less developed – That there are fewer studies involving lung cancer, but there is promise--based on high downloads of the lung function app--that it could be popular (if embedded into something like that) and 2) that mHealth apps need to be integrated into the medical record for increased oversight and personalized recommendations. I might split these ideas up or even take out the second part, because I don’t think it’s needed to justify the initial research question. Maybe it belongs in the discussion of a future paper based on what you find in your review. “Sereno et al. , found that the mHealth ALIBIRD platform, a remote app for recording symptoms, lifestyle and sleep patterns among patients, helped promote healthy lifestyle and patient empowerment, while supporting clinician recommendations.” ^Not sure this is needed / what purpose of including is – maybe that screening could be considered a part of this because it fits into preventive health? Is this a lung cancer app? It is under “lung cancer and mHealth”. “Ultimately, integration of mHealth apps with personal medical records would allow for a seamless flow of information between patients, healthcare providers, and the screening platforms. This integration could ensure that relevant health data is accurately captured and available to healthcare providers, enhancing the decision-making process and personalising the patient's care journey.” These statements feel a little premature in the background and maybe not needed in the initial statement of the paper’s aims – maybe save for the discussion once you’ve made more of a case through your review, if that is what you found. Or is this a justification for the method? I would leave it out or clarify. I am not very familiar with realist review. I would include a brief description and justification in your background for why this method is appropriate, including why realist review as opposed to methods your audience is more likely to be familiar with like more traditional systematic reviews. Why a review? Is this a preliminary step in developing an mHealth app for LCS? Bits from later in the manuscript that I think would have been helpful to see upfront: -What realist review is: “theory-driven approach to evidence synthesis which begins with the articulation of initial theories… explaining how mHealth interventions might work, for whom, and under what circumstances, for example… which are then iteratively tested and refined against the evidence gathered from the included studies” -“realist synthesis values richness and relevance of data over traditional hierarchies of evidence” (also an example of what’s meant by traditional hierarchies of evidence) -advantages - “moves beyond assessing mere efficacy to unpacking the 'why', 'when', and 'how' of mHealth interventions” and/or “underlines the importance of considering the holistic context in which mHealth interventions are deployed”; Allows you to “draw from a wide range of sources and types of evidence” Especially since this is a protocol, you could also point out that part of the value of this paper is that realist review is a new approach, and this manuscript serves as a protocol of this method in a subject area that it has not been previously applied to (assuming this is true) Methods: Overall, the methods section is very clearly written and describes the process followed in great detail that helps me as someone unfamiliar with this method understand and draw comparisons to more familiar methods. “Given the nature of mHealth interventions and the multiple factors influencing their success, a realist synthesis approach is appropriate.” Briefly, what about their nature makes this approach appropriate? Again, I think a little more explanation of when you would want to use a review approach and why this particular review method vs. others is warranted. Also, as someone who knows little detail about these methodologies, I’m wondering (as others might): Since there are so few studies of LCS mHealth interventions, as you state in the background, is there enough data for this approach to draw the conclusions you need? “These terms and keywords identified are based on preliminary scans of the literature, consultation with experts in the field, and alignment with our review objectives.” Consider briefly describing who the experts are, e.g., pulmonologists, lung cancer screening coordinators… and in what setting, e.g. large hospitals in x country Stakeholder engagement – Suggest spelling out ICT professionals acronym Discussion - Strengths and limitations: “By doing so, we hope to understand the mechanisms and contexts that shape outcomes.” It may seem obvious, but why do you need to know “the why”? Consider spelling out just a bit more about the implications of knowing the reasons the intervention works (creating tailored lung cancer screening interventions that address x y z factors shown to impact intervention efficacy) Implications for future research, policy, and clinical practice- Might it be appropriate to reiterate that not only will the findings of the synthesis be important, but this protocol serves as a blueprint for researchers to evaluate complex interventions? Is the rationale for, and objectives of, the study clearly described? Partly Is the study design appropriate for the research question? Partly Are sufficient details of the methods provided to allow replication by others? Yes Are the datasets clearly presented in a useable and accessible format? Not applicable References 1. Boudreau JH, Miller DR, Qian S, Nunez ER, et al.: Access to Lung Cancer Screening in the Veterans Health Administration: Does Geographic Distribution Match Need in the Population?. Chest . 2021; 160 (1): 358-367 PubMed Abstract | Publisher Full Text 2. Carter-Bawa L: Shifting the Lens on Lung Cancer Screening Inequities. JAMA Netw Open . 2024; 7 (5): e2412782 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: lung cancer screening, mixed methods research, systematic reviews I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Boudreau JH. Reviewer Report For: Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.21956/hrbopenres.15277.r45829 ) The direct URL for this report is: https://hrbopenresearch.org/articles/8-12/v1#referee-response-45829 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 Dec 2025 Selena Gong , $usrAffiliation 05 Dec 2025 Author Response Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. The authors acknowledge the recommendations and have updated the manuscript to reflect the following: ... Continue reading Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. The authors acknowledge the recommendations and have updated the manuscript to reflect the following: Introduction references updated to include those relevant to inequalities in Lung Cancer Screening participation. The paragraph regarding mHealth and Lung Cancer Screening has been refined, with a more targeted argument for this work. An earlier stated rationale behind choice of review methodology in addition to relevant references. Further information has been provided outlining the nature and expertise of our stakeholders and collaborators. The strengths and limitations section has been updated to outline implications. To the question about whether enough evidence exists in this area to justify a realist review, the flexible approach of this methodology allows it to adapt to the available evidence. With a focus on theory development, even a small number of papers can suffice if they meaningfully contribute to the research question and the theory itself. As such, we do not believe this to be a significant barrier to conducting our review, although it may indeed highlight gaps in the literature. While realist methodology has not previously been applied to lung cancer screening participation, it has been utilised previously in cancer screening. It may be under-utilised in screening contexts, but we cannot claim to be the first to apply it in this way (reference 1). Kind regards, mHealth study team References: Myers L, Goodwin B, Ralph N, Castro O, March S. Implementation Strategies for Interventions Aiming to Increase Participation in Mail-Out Bowel Cancer Screening Programs: A Realist Review. Front Oncol. 2020 Sep 29;10:543732. doi: 10.3389/fonc.2020.543732. PMID: 33117681; PMCID: PMC7550731. Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. The authors acknowledge the recommendations and have updated the manuscript to reflect the following: Introduction references updated to include those relevant to inequalities in Lung Cancer Screening participation. The paragraph regarding mHealth and Lung Cancer Screening has been refined, with a more targeted argument for this work. An earlier stated rationale behind choice of review methodology in addition to relevant references. Further information has been provided outlining the nature and expertise of our stakeholders and collaborators. The strengths and limitations section has been updated to outline implications. To the question about whether enough evidence exists in this area to justify a realist review, the flexible approach of this methodology allows it to adapt to the available evidence. With a focus on theory development, even a small number of papers can suffice if they meaningfully contribute to the research question and the theory itself. As such, we do not believe this to be a significant barrier to conducting our review, although it may indeed highlight gaps in the literature. While realist methodology has not previously been applied to lung cancer screening participation, it has been utilised previously in cancer screening. It may be under-utilised in screening contexts, but we cannot claim to be the first to apply it in this way (reference 1). Kind regards, mHealth study team References: Myers L, Goodwin B, Ralph N, Castro O, March S. Implementation Strategies for Interventions Aiming to Increase Participation in Mail-Out Bowel Cancer Screening Programs: A Realist Review. Front Oncol. 2020 Sep 29;10:543732. doi: 10.3389/fonc.2020.543732. PMID: 33117681; PMCID: PMC7550731. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 Dec 2025 Selena Gong , $usrAffiliation 05 Dec 2025 Author Response Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. The authors acknowledge the recommendations and have updated the manuscript to reflect the following: ... Continue reading Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. The authors acknowledge the recommendations and have updated the manuscript to reflect the following: Introduction references updated to include those relevant to inequalities in Lung Cancer Screening participation. The paragraph regarding mHealth and Lung Cancer Screening has been refined, with a more targeted argument for this work. An earlier stated rationale behind choice of review methodology in addition to relevant references. Further information has been provided outlining the nature and expertise of our stakeholders and collaborators. The strengths and limitations section has been updated to outline implications. To the question about whether enough evidence exists in this area to justify a realist review, the flexible approach of this methodology allows it to adapt to the available evidence. With a focus on theory development, even a small number of papers can suffice if they meaningfully contribute to the research question and the theory itself. As such, we do not believe this to be a significant barrier to conducting our review, although it may indeed highlight gaps in the literature. While realist methodology has not previously been applied to lung cancer screening participation, it has been utilised previously in cancer screening. It may be under-utilised in screening contexts, but we cannot claim to be the first to apply it in this way (reference 1). Kind regards, mHealth study team References: Myers L, Goodwin B, Ralph N, Castro O, March S. Implementation Strategies for Interventions Aiming to Increase Participation in Mail-Out Bowel Cancer Screening Programs: A Realist Review. Front Oncol. 2020 Sep 29;10:543732. doi: 10.3389/fonc.2020.543732. PMID: 33117681; PMCID: PMC7550731. Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. The authors acknowledge the recommendations and have updated the manuscript to reflect the following: Introduction references updated to include those relevant to inequalities in Lung Cancer Screening participation. The paragraph regarding mHealth and Lung Cancer Screening has been refined, with a more targeted argument for this work. An earlier stated rationale behind choice of review methodology in addition to relevant references. Further information has been provided outlining the nature and expertise of our stakeholders and collaborators. The strengths and limitations section has been updated to outline implications. To the question about whether enough evidence exists in this area to justify a realist review, the flexible approach of this methodology allows it to adapt to the available evidence. With a focus on theory development, even a small number of papers can suffice if they meaningfully contribute to the research question and the theory itself. As such, we do not believe this to be a significant barrier to conducting our review, although it may indeed highlight gaps in the literature. While realist methodology has not previously been applied to lung cancer screening participation, it has been utilised previously in cancer screening. It may be under-utilised in screening contexts, but we cannot claim to be the first to apply it in this way (reference 1). Kind regards, mHealth study team References: Myers L, Goodwin B, Ralph N, Castro O, March S. Implementation Strategies for Interventions Aiming to Increase Participation in Mail-Out Bowel Cancer Screening Programs: A Realist Review. Front Oncol. 2020 Sep 29;10:543732. doi: 10.3389/fonc.2020.543732. PMID: 33117681; PMCID: PMC7550731. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 24 Jan 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 05 Dec 25 Version 1 24 Jan 25 read read Jacqueline H Boudreau , Center for Healthcare Optimization & Implementation Research, VA Boston Healthcare System, VA Bedford Healthcare System, Boston, USA Sushant Patkar , Artificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Patkar S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. 10 Apr 2025 | for Version 1 Sushant Patkar , Artificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, USA 0 Views copyright © 2025 Patkar S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article charts out a plan to investigate how effective mobile health tools are at encouraging people to get screened earlier for lung cancer. Overall, the rationale is well-grounded in the need to improve early lung cancer detection, and the objectives are clearly laid out to explore who mobile Health works for, how, and why. 1. Please describe in more depth how CMO configurations will be identified and coded. How many reviewers will be involved in data extraction and synthesis? Will inter-reviewer variability (Kappa scores) be assessed? 2. Please provide additional details along with references on tools (e.g., ROBINS-E, Joanna Briggs Institute tool), the authors plan to utilize to mitigate risks of selection bias and interpretive subjectivity in their study. Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Yes Are sufficient details of the methods provided to allow replication by others? Yes Are the datasets clearly presented in a useable and accessible format? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise genomics, precision oncology, lung cancer, artificial intelligence I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 05 Dec 2025 Selena Gong, Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. While appraisal of included evidence is a core part of the realist synthesis, the approach differs from traditional reviews. The focus is upon the three pillars of relevance, richness, and rigour. Although the credibility of the findings within a paper are consider as part of the assessment of ‘rigour’, a strict risk of bias assessment does not form part of realist review methodology as the purpose of the evidence synthesis is to create explanatory theories rather than an unbiased quantification of effect size. (Reference 1). The methods section of the manuscript has been updated to reflect this and to explain in more detail the quality appraisal process as well as CMO configuration development. Kind regards, mHealth study team References: Dada S, Dalkin S, Gilmore B, Hunter R, Mukumbang FC. Applying and reporting relevance, richness and rigour in realist evidence appraisals: advancing key concepts in realist reviews. Research Synthesis Methods. 2023 May;14(3):504-14. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Patkar S. Peer Review Report For: Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.21956/hrbopenres.15277.r46243) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://hrbopenresearch.org/articles/8-12/v1#referee-response-46243 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Boudreau J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 Mar 2025 | for Version 1 Jacqueline H Boudreau , Center for Healthcare Optimization & Implementation Research, VA Boston Healthcare System, VA Bedford Healthcare System, Boston, USA 0 Views copyright © 2025 Boudreau J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is a very interesting, informative, and well-written protocol paper about using realist review to evaluate mHealth interventions for lung cancer screening implementation, which might lead to increased early detection and reduced mortality from lung cancer. I think this paper could benefit from additional and earlier rationale for the chosen method, particularly given its newness relative to more well-known review methods, which I think could be reframed as an asset to this paper if done thoroughly. Introduction - Lung cancer screening paragraph – “Furthermore, ethnic and regional inequalities persist, underscoring the likely need for tailored approaches to improve LCS participation.” Suggested citations (full disclosure- I am an author on one of them): Refer 1 and Refer 2 Lung cancer and mHealth – I am losing the thread in this section a little bit. It appears you are trying to make two main points: 1) That the evidence for mHealth to support LCS is less developed – That there are fewer studies involving lung cancer, but there is promise--based on high downloads of the lung function app--that it could be popular (if embedded into something like that) and 2) that mHealth apps need to be integrated into the medical record for increased oversight and personalized recommendations. I might split these ideas up or even take out the second part, because I don’t think it’s needed to justify the initial research question. Maybe it belongs in the discussion of a future paper based on what you find in your review. “Sereno et al. , found that the mHealth ALIBIRD platform, a remote app for recording symptoms, lifestyle and sleep patterns among patients, helped promote healthy lifestyle and patient empowerment, while supporting clinician recommendations.” ^Not sure this is needed / what purpose of including is – maybe that screening could be considered a part of this because it fits into preventive health? Is this a lung cancer app? It is under “lung cancer and mHealth”. “Ultimately, integration of mHealth apps with personal medical records would allow for a seamless flow of information between patients, healthcare providers, and the screening platforms. This integration could ensure that relevant health data is accurately captured and available to healthcare providers, enhancing the decision-making process and personalising the patient's care journey.” These statements feel a little premature in the background and maybe not needed in the initial statement of the paper’s aims – maybe save for the discussion once you’ve made more of a case through your review, if that is what you found. Or is this a justification for the method? I would leave it out or clarify. I am not very familiar with realist review. I would include a brief description and justification in your background for why this method is appropriate, including why realist review as opposed to methods your audience is more likely to be familiar with like more traditional systematic reviews. Why a review? Is this a preliminary step in developing an mHealth app for LCS? Bits from later in the manuscript that I think would have been helpful to see upfront: -What realist review is: “theory-driven approach to evidence synthesis which begins with the articulation of initial theories… explaining how mHealth interventions might work, for whom, and under what circumstances, for example… which are then iteratively tested and refined against the evidence gathered from the included studies” -“realist synthesis values richness and relevance of data over traditional hierarchies of evidence” (also an example of what’s meant by traditional hierarchies of evidence) -advantages - “moves beyond assessing mere efficacy to unpacking the 'why', 'when', and 'how' of mHealth interventions” and/or “underlines the importance of considering the holistic context in which mHealth interventions are deployed”; Allows you to “draw from a wide range of sources and types of evidence” Especially since this is a protocol, you could also point out that part of the value of this paper is that realist review is a new approach, and this manuscript serves as a protocol of this method in a subject area that it has not been previously applied to (assuming this is true) Methods: Overall, the methods section is very clearly written and describes the process followed in great detail that helps me as someone unfamiliar with this method understand and draw comparisons to more familiar methods. “Given the nature of mHealth interventions and the multiple factors influencing their success, a realist synthesis approach is appropriate.” Briefly, what about their nature makes this approach appropriate? Again, I think a little more explanation of when you would want to use a review approach and why this particular review method vs. others is warranted. Also, as someone who knows little detail about these methodologies, I’m wondering (as others might): Since there are so few studies of LCS mHealth interventions, as you state in the background, is there enough data for this approach to draw the conclusions you need? “These terms and keywords identified are based on preliminary scans of the literature, consultation with experts in the field, and alignment with our review objectives.” Consider briefly describing who the experts are, e.g., pulmonologists, lung cancer screening coordinators… and in what setting, e.g. large hospitals in x country Stakeholder engagement – Suggest spelling out ICT professionals acronym Discussion - Strengths and limitations: “By doing so, we hope to understand the mechanisms and contexts that shape outcomes.” It may seem obvious, but why do you need to know “the why”? Consider spelling out just a bit more about the implications of knowing the reasons the intervention works (creating tailored lung cancer screening interventions that address x y z factors shown to impact intervention efficacy) Implications for future research, policy, and clinical practice- Might it be appropriate to reiterate that not only will the findings of the synthesis be important, but this protocol serves as a blueprint for researchers to evaluate complex interventions? Is the rationale for, and objectives of, the study clearly described? Partly Is the study design appropriate for the research question? Partly Are sufficient details of the methods provided to allow replication by others? Yes Are the datasets clearly presented in a useable and accessible format? Not applicable References 1. Boudreau JH, Miller DR, Qian S, Nunez ER, et al.: Access to Lung Cancer Screening in the Veterans Health Administration: Does Geographic Distribution Match Need in the Population?. Chest . 2021; 160 (1): 358-367 PubMed Abstract | Publisher Full Text 2. Carter-Bawa L: Shifting the Lens on Lung Cancer Screening Inequities. JAMA Netw Open . 2024; 7 (5): e2412782 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise lung cancer screening, mixed methods research, systematic reviews I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 05 Dec 2025 Selena Gong, Thank you for reviewing this article and providing your valuable feedback. It is greatly appreciated. The authors acknowledge the recommendations and have updated the manuscript to reflect the following: Introduction references updated to include those relevant to inequalities in Lung Cancer Screening participation. The paragraph regarding mHealth and Lung Cancer Screening has been refined, with a more targeted argument for this work. An earlier stated rationale behind choice of review methodology in addition to relevant references. Further information has been provided outlining the nature and expertise of our stakeholders and collaborators. The strengths and limitations section has been updated to outline implications. To the question about whether enough evidence exists in this area to justify a realist review, the flexible approach of this methodology allows it to adapt to the available evidence. With a focus on theory development, even a small number of papers can suffice if they meaningfully contribute to the research question and the theory itself. As such, we do not believe this to be a significant barrier to conducting our review, although it may indeed highlight gaps in the literature. While realist methodology has not previously been applied to lung cancer screening participation, it has been utilised previously in cancer screening. It may be under-utilised in screening contexts, but we cannot claim to be the first to apply it in this way (reference 1). Kind regards, mHealth study team References: Myers L, Goodwin B, Ralph N, Castro O, March S. Implementation Strategies for Interventions Aiming to Increase Participation in Mail-Out Bowel Cancer Screening Programs: A Realist Review. Front Oncol. 2020 Sep 29;10:543732. doi: 10.3389/fonc.2020.543732. PMID: 33117681; PMCID: PMC7550731. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Boudreau JH. Peer Review Report For: Protocol for a Realist Review of mHealth in Lung Cancer Screening: Understanding Mechanisms, Contexts, and Intervention Characteristics for Enhanced Participation [version 1; peer review: 2 approved with reservations] . HRB Open Res 2025, 8 :12 ( https://doi.org/10.21956/hrbopenres.15277.r45829) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://hrbopenresearch.org/articles/8-12/v1#referee-response-45829 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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