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

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).

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:

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:

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.

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.

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:

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.

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).