Data requirements of an informative, directive, and preventive mobile health application for gastric cancer.

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Learn more: PMC Disclaimer | PMC Copyright Notice Digit Health . 2026 May 29;12:20552076261425336. doi: 10.1177/20552076261425336 Data requirements of an informative, directive, and preventive mobile health application for gastric cancer Vahideh Zolfaghari 1 , Hamid Moghaddasi 1, ✉ , Reza Rabiei 1, ✉ , Mitra Ahadi 2 Author information Article notes Copyright and License information 1 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2 Department of Gastroenterology and Hepatology, Mashhad University of Medical Sciences, Mashhad, Iran ✉ Hamid Moghaddasi, Professor of Health Information Management & Medical Informatics, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.Email: moghaddasi@sbmu.ac.ir ✉ Reza Rabiei, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.Email: r.rabiei@sbmu.ac.ir Received 2025 Aug 21; Accepted 2026 Feb 2; Collection date 2026 Jan-Dec. © The Author(s) 2026 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). PMC Copyright notice PMCID: PMC13221601  PMID: 42221515 Abstract Introduction Gastric cancer is one of the leading causes of cancer-related mortality worldwide, making early detection and prevention essential. Existing mobile health applications in this field are predominantly treatment-oriented and focused on post-diagnostic stages, leaving a significant gap in primary prevention coverage and identification of precursor conditions. The aim of this study is the systematic identification of data requirements related to precursor conditions of gastric cancer through review of clinical guidelines and evidence-based studies, in order to provide a conceptual framework for designing an informative, directive, and preventive mobile health application based on a rule-based (non-AI) approach. Method This study is a systematic review conducted in accordance with the PRISMA 2020 protocol. A comprehensive search was performed across PubMed, Google Scholar, Scopus, Web of Science, and UpToDate databases from January 2005 to March 2024. Inclusion criteria encompassed authoritative clinical guidelines, consensus studies, and research articles related to diagnosis, screening, or precursor conditions of gastric cancer. Source quality was assessed using the Mixed Method Appraisal Tool. Finally, 24 sources (19 primary sources including 14 clinical guidelines, 1 consensus study, and 4 research articles; plus 5 UpToDate sources as supplementary evidence) were included in the analysis. Findings From the systematic analysis of 19 primary sources, three main categories of precursor conditions for gastric cancer were identified: (a) baseline risk factors (family history, hereditary syndromes, previous gastric surgery), (b) diagnosed medical conditions ( Helicobacter pylori infection, gastritis, gastric ulcer, iron-deficiency anemia, Epstein-Barr virus), and (c) functional symptoms of the upper gastrointestinal tract (dyspepsia, gastroesophageal reflux, and related symptoms). This classification forms the basis of a four-level risk assessment framework that translates precursor conditions into understandable questions for general users. Conclusion This study presents the first comprehensive evidence-based framework for data requirements of a preventive mobile health application for gastric cancer. The proposed framework, with its focus on primary prevention and deterministic decision-making engine, offers the potential to create a conceptual infrastructure for early interventions and public health literacy enhancement. Major limitations include the conceptual nature of the design and lack of field validation. Future studies should focus on practical implementation, clinical effectiveness evaluation, user acceptance, and integration with health systems to realize the potential for reducing the burden of gastric cancer, particularly in high-prevalence regions. Keywords: Gastric cancer, mobile health applications, screening, early detection Introduction Gastric cancer (GC) is a significant global public health concern, with over one million new cases and approximately 769,000 deaths reported in 2020 alone. 1 The majority of patients are diagnosed at advanced stages of the disease, often due to delayed clinical presentation, rendering them ineligible for curative treatment. 2 Consequently, the 5-year survival rate for these patients drops below 30%, whereas early diagnosis can increase this rate to as high as 90%. 3 , 4 Treatment of this disease in advanced stages, in addition to high costs, places considerable pressure on healthcare systems. 5 – 7 Therefore, prevention and early detection are recognized as the most effective strategies for controlling gastric cancer. 8 The gastric carcinogenesis process is multifactorial, influenced by the interaction between host factors, Helicobacter pylori infection, and environmental contributors such as diet. It typically follows a stepwise progression including gastritis, atrophy, intestinal metaplasia, and dysplasia. 9 The World Health Organization (WHO) categorizes precursor conditions of gastric cancer into two main groups: precancerous diseases and precancerous lesions. 10 Among these, stage 0 gastric cancer (Tis, N0, M0), or carcinoma in situ, refers to a condition in which abnormal cells are confined to the superficial mucosal layer of the stomach without having spread to deeper layers or metastasized to other parts of the body. 11 Diagnosis and treatment of precancerous gastric conditions (PLGC) provide an effective intervention for gastric cancer prevention. 12 Although stage 0 gastric cancer is typically asymptomatic and can only be diagnosed through histopathological examination, the stages preceding it are often accompanied by clinical manifestations, thus providing a critical window of opportunity for preventive interventions. In this study, the term “precursor conditions of gastric cancer” is used to describe potentially preventable factors in gastric carcinogenesis, including demographic and medical conditions (such as H. pylori infection, family history of gastric cancer, chronic gastritis, gastric ulcer, tobacco use, etc.) and non-specific warning symptoms (such as persistent indigestion, epigastric pain, heartburn, bloating, acid reflux, etc.). Mobile health (mHealth) applications and medical chatbot are among the emerging technologies that can play a significant role in early disease detection, disease prevention, healthcare cost reduction, and overall health promotion. 13 , 14 Chatbots are generally categorized into two types: AI-based and non-AI (rule-based). AI-based chatbots possess learning capabilities, allowing them to improve their performance over time and provide increasingly better responses. In contrast, rule-based chatbots operate based on a set of predefined rules and patterns, delivering responses according to pre-programmed scenarios without adaptive learning. The design of such applications for gastric cancer prevention requires a well-defined set of data requirements comprising three main domains. The first domain includes educational data requirements for improving health literacy related to gastric cancer, encompassing educational information about the stomach, gastric anatomy, gastric cancer, and gastric cancer screening methods. The second domain comprises diagnostic data requirements related to precursor conditions of gastric cancer. These data must be based on medical guidelines and up-to-date studies to provide a scientific basis for identifying users at high risk of gastric cancer. The third domain focuses on interactive data requirements for designing user–application conversations. This component, as the primary interface between the user and the system, must be capable of generating responses tailored to user needs and concerns that are validated by medical specialists. This study focuses exclusively on the second domain of these data requirements, namely, the identification, examination, and integration of data related to precursor conditions of gastric cancer from authoritative sources. The proposed application is designed based on a rule-based chatbot, because currently, confidence in rule-based medical chatbots grounded in medical guidelines is greater than in AI-based chatbots. In the sensitive clinical domain where patient safety is of critical importance, rule-based applications provide complete transparency and explainability, precise control over all outputs, deterministic behavior without the risk of hallucination, and ease of evaluation and alignment with medical guidelines. These advantages are important for an application targeting the general population, as any incorrect or ambiguous recommendation could result in delayed care or unnecessary anxiety. 15 – 17 The target users of this application are members of the general population who have not yet developed gastric cancer or severe gastrointestinal diseases and are not under regular medical care. These individuals experience common non-specific gastrointestinal symptoms (heartburn, acid reflux, bloating, persistent indigestion, epigastric pain, and early satiety) but do not take them seriously, and may develop gastric cancer if they do not pursue follow-up. The sources used to determine these data requirements are medical guidelines and authoritative diagnostic studies on precursor conditions of gastric cancer, which form the scientific foundation of the application. By receiving demographic information, simple medical history (such as family history of gastric cancer, H. pylori infection, gastric ulcer, etc.), and current common gastrointestinal symptoms from the user, the application assesses individual risk level based on valid rules derived from medical guidelines. It then provides educational messages and appropriate preventive recommendations, and in cases of high risk, displays explicit warnings along with urgent recommendations to consult a gastroenterologist. Thus, by focusing on primary prevention, the application guides ordinary members of the public who are at risk of delayed diagnosis of precursor conditions of gastric cancer toward specialist intervention in a timely manner. This application does not replicate general symptom checkers but complements them, ensuring that timely preventive guidance is provided to users. Therefore, among the most important features of this application are gastric cancer risk assessment, provision of personalized recommendations for preventive actions, and the ability to continuously record and monitor users’ health status. This tool, through analysis of individual data and generation of recommendations tailored to each user's specific condition, acts as a guide and early intervention tool for gastric cancer prevention. Figure 1 illustrates the schematic diagram of the proposed application presented in this study. Figure 1. Open in a new tab Schematic diagram of the informative, directive, and preventive mobile health application for gastric cancer. Overall, the aim of this study is to identify and specify data requirements related to precursor conditions of gastric cancer for the purpose of designing an informative, directive, and preventive mobile health application for gastric cancer in the form of a rule-based (non-AI) chatbot, with the goals of raising awareness, preventing gastric cancer, and providing guidance regarding its precursor conditions. Method This study was designed as a systematic review in accordance with the PRISMA 2020 protocol 18 to identify key data requirements for the design of preventive mobile health applications for gastric cancer. A comprehensive search was performed across reputable databases including PubMed, Google Scholar, Scopus, Web of Science, and UpToDate to identify relevant studies published between January 1, 2005, and March 1, 2024. The search strategy employed a combination of keywords and Medical Subject Headings (MeSH) terms related to “gastric cancer,” “precursor conditions,” and “medical guidelines.” Details of the search strategies, in accordance with PRISMA 2020 guidelines (for each database including exact search terms, Boolean operators, and time ranges) are provided in Online Appendix (Full Search Strategy). Additionally, EndNote X7 reference management software was used to collect and remove duplicate entries. Eligibility criteria Studies were included in this review if they met the following inclusion criteria: The latest version of medical guidelines or diagnostic studies related to gastric cancer, available in English. Medical guidelines or recent studies focused on diagnosis, management, or precursor conditions of gastric cancer. Medical guidelines or diagnostic studies that specifically referenced precursor conditions of gastric cancer. Exclusion criteria were as follows: Guidelines or studies not published in English. Guidelines or studies lacking clear data or recommendations on precursor conditions of gastric cancer. Guidelines for diagnosis and management of gastric cancer or its precursor conditions for which newer versions were available. Study selection In a collaborative approach to study selection, the titles and abstracts of identified articles were independently screened by two researchers (HM and VZ). If an article was evaluated as relevant by both researchers, its full text was reviewed. Any discrepancies were resolved through discussion. In total, 253 records were initially identified: 80 from PubMed, 65 from Scopus, 94 from Web of Science, and 14 from UpToDate. After removal of duplicates, 96 records remained for screening. Initial screening based on title and abstract resulted in the selection of 62 studies for full-text evaluation. In the final stage, applying inclusion criteria including the latest versions of authoritative medical guidelines and studies, 15 medical guidelines and 10 important studies were selected for final analysis. In total, 24 sources were included in the study: 14 clinical guidelines, 1 consensus statements, 5 evidence-based clinical topic reviews from UpToDate, and 4 research articles ( Table 1 ). During the data extraction process, information extracted from selected articles was entered into Excel in a structured manner. The PRISMA 2020 flow diagram illustrating these stages is presented in Figure 2 . Table 1. Characteristics of selected studies. Study Source type Location Year Asia–Pacific consensus guidelines on gastric cancer prevention 19 Clinical Guideline Asia 2008 Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with gastric cancer 20 Clinical Guideline Asia 2024 Gastric cancer, version 2.2022, NCCN clinical practice guidelines in oncology 21 Clinical Guideline America 2022 ACG and CAG Clinical Guideline: Management of Dyspepsia 22 Clinical Guideline America 2017 Brazilian Group of Gastrointestinal Tumours’ consensus guidelines for the management of gastric cancer 23 Clinical Guideline Brazil 2020 Brazilian gastric cancer association guidelines 24 Clinical Guideline Brazil 2021 Guidelines for the management of oesophageal and gastric cancer 25 Clinical Guideline UK 2011 Chinese guidelines for diagnosis and treatment of gastric cancer 2018 (English version) 26 Clinical Guideline China 2019 Chinese integrated guideline on the management of gastric precancerous conditions and lesions 12 Clinical Guideline China 2022 The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023 27 Clinical Guideline China 2024 Chinese national clinical practice guidelines on the prevention, diagnosis, and treatment of early gastric cancer 28 Clinical Guideline China 2024 Gastric cancer: ESMO–ESSO–ESTRO clinical practice guidelines for diagnosis, treatment and follow-up 29 Clinical Guideline Europe 2014 Gastric cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up 2 Clinical Guideline Europe 2022 Beyond the guidelines: the grey zones of the management of gastric cancer. Consensus statements from the gastric cancer Italian network (gain) 30 Clinical Guideline/Consensus Italy 2021 Proposal of minimum elements for screening and diagnosis of gastric cancer by an international Delphi consensus 31 Consensus Statement International 2022 Risk factors for gastric cancer 32 UpToDate evidence-based review China 2005 Association between Helicobacter pylori infection and gastrointestinal malignancy 33 UpToDate evidence-based review USA 2008 Gastric polyps 34 UpToDate evidence-based review Thailand 2015 Gastric intestinal metaplasia 35 UpToDate evidence-based review USA 2017 Gastric cancer screening 36 UpToDate evidence-based review China 2020 Why does Japan have a high incidence of gastric cancer? Comparison of gastritis between UK and Japanese patients 37 Primary Research Article Japan 2006 Gastric cancer screening in Japan: a narrative review 38 Primary Research Article UK, Japan 2022 Risk factors for gastric cancer in Japan in the 2010s: a large, long-term observational study 39 Primary Research Article Japan 2022 The burden and trend of gastric cancer and possible risk factors in five Asian countries from 1990 to 2019 40 Primary Research Article Asia 2022 Open in a new tab Figure 2. Open in a new tab The PRISMA 2020 flow diagram. Data extraction process The first reviewer (VZ) carefully extracted the required data from the selected articles. Subsequently, the second reviewer (HM) reviewed the quality of the extracted information. Any inconsistencies or ambiguities in the data were thoroughly examined during joint sessions involving all researchers (VZ, HM, RR, and MA) and resolved through scientific and collaborative discussions. The characteristics of the selected articles are presented in Table 1 . Quality assessment of studies The selected sources in this study were categorized into three groups based on their nature and type. The first group comprises fourteen clinical guidelines, developed by reputable international and regional organizations that have undergone rigorous review by expert panels. Given the inherent authority of these guidelines and their standardized development process, separate quality assessment is not necessary. The second group consists of five evidence-based clinical reviews from UpToDate. These sources are continuously updated based on the latest scientific evidence and are themselves written based on comprehensive reviews of primary research articles. Since these clinical reviews have undergone peer review and are widely used as authoritative reference sources in clinical decision-making, they are considered reliable tertiary sources and do not require separate quality assessment. The third group comprises primary research articles and one consensus statement, totaling 5 articles with diverse study designs (consensus methods, narrative reviews, cohort studies, and epidemiological studies). Given the methodological heterogeneity of these studies, quality was assessed using the Mixed Methods Appraisal Tool (MMAT version 2018). 19 MMAT provides a unified framework for evaluating different study types and has been validated for assessing the quality of diverse research designs. Each article was independently reviewed by two researchers (VZ, HM) using the appropriate MMAT criteria for its study type, and in case of disagreement, consensus was reached through discussion. The results of quality assessment are presented in Table 2 . Table 2. Quality assessment of primary research articles using MMAT 2018. Study Study Design MMAT Category Criteria 1 Criteria 2 Criteria 3 Criteria 4 Criteria 5 Score Quality Rating Kakushima et al. (2022) 31 International Delphi Consensus Qualitative Yes Yes Yes Yes No 4/5 (80%) High Naylor et al. (2006) 37 Comparative Cross-Sectional Quantitative Descriptive Yes Yes Yes No Yes 4/5 (80%) High Yashima et al. (2022) 38 Narrative Review Qualitative Yes Yes No Yes No 3/5 (60%) Moderate Takahashi et al. (2022) 39 Cohort Study Quantitative Non-randomized Yes Yes Yes Yes Yes 5/5 (100%) High Ning et al. (2022) 40 Epidemiological Prevalence Quantitative Descriptive Yes Yes Yes No Yes 4/5 (80%) High Open in a new tab MMAT 2018 assessment criteria: The Mixed Methods Appraisal Tool (MMAT) version 2018 includes two screening questions applicable to all study types, followed by five category-specific methodological quality criteria. Studies are assessed based on the following categories:Category-Specific Criteria:For Qualitative Studies (Kakushima 31 - Delphi Consensus; Yashima 38 - Narrative Review): 1. Is the qualitative approach appropriate to answer the research question? 2. Are the qualitative data collection methods adequate to address the research question? 3. Are the findings adequately derived from the data? 4. Is the interpretation of results sufficiently substantiated by data? 5. Is there coherence between qualitative data sources, collection, analysis, and interpretation?For Quantitative Descriptive Studies (Naylor 37 - Comparative Cross-Sectional; Ning 40 -Epidemiological): 1. Is the sampling strategy relevant to address the research question? 2. Is the sample representative of the target population? 3. Are the measurements appropriate? 4. Is the risk of nonresponse bias low? 5. Is the statistical analysis appropriate to answer the research question?For Quantitative Non-Randomized Studies (Takahashi 39 - Cohort Study): 1. Are the participants representative of the target population? 2. Are measurements appropriate regarding both the outcome and intervention (or exposure)? 3. Are there complete outcome data? 4. Are the confounders accounted for in the design and analysis? 5. During the study period, is the intervention administered (or exposure occurred) as intended?Scoring Method: Each criterion is rated as “Yes” (criterion met), “No” (criterion not met), or “Can't tell” (insufficient information). The overall quality is determined by the number of criteria met out of five applicable criteria per study type.Quality Rating: High: 4–5 criteria met (≥80%); Moderate: 3 criteria met (60%); Low: ≤2 criteria met (<50%). Findings With the aim of identifying precursor conditions of gastric cancer as part of the data requirements for an informative, directive, and preventive mobile health application for gastric cancer, a systematic search was conducted for the period between 2005 and 2024. The search focused primarily on medical guidelines and diagnostic studies related to gastric cancer or its precursor conditions. A total of 24 sources were included for final analysis, comprising 19 primary sources (14 medical guidelines, 1 consensus study, and 4 primary research articles) and 5 evidence-based sources. The selected studies represented a geographically diverse range of regions including Asia, the United States, Europe, the United Kingdom, Brazil, Italy, China, and Japan. To ensure methodological rigor and avoid potential selection bias, all classifications of precursor conditions and reported frequencies in Tables 3 – 5 were extracted exclusively from the 19 primary sources. The five UpToDate sources, although included in the study due to their clinical relevance and currency, were used solely as supplementary confirmatory evidence. The characteristics of these selected studies are summarized in Table 1 , and the precursor conditions of gastric cancer identified in the selected studies are detailed in Table 3 . Table 3. Precursor conditions of gastric cancer identified in primary sources. No. Location Year Study title (Reference) Primary precursor conditions identified 1 Asia 2008 Asia–Pacific consensus guidelines on gastric cancer prevention 19 Helicobacter pylori infection, family history of gastric cancer, gastritis, gastric ulcers, dyspepsia 2 Asia 2024 Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with gastric cancer 20 Gastroesophageal reflux, Epstein-Barr virus, Helicobacter pylori infection, hereditary syndromes 3 America 2022 Gastric cancer, version 2.2022, NCCN clinical practice guidelines in oncology 21 Helicobacter pylori infection, hereditary syndromes, family history of gastric cancer 4 America 2017 ACG and CAG Clinical Guideline: Management of Dyspepsia 22 Helicobacter pylori infection, dyspepsia, epigastric pain 5 Brazil 2020 Brazilian Group of Gastrointestinal Tumours’ consensus guidelines for the management of gastric cancer 23 Helicobacter pylori infection, hereditary syndromes, family history of gastric cancer, gastric ulcers, gastritis 6 Brazil 2021 Brazilian gastric cancer association guidelines 24 Family history of gastric cancer, gastritis, Helicobacter pylori infection 7 UK 2011 Guidelines for the management of oesophageal and gastric cancer 25 Dyspepsia (patients >55 years), Helicobacter pylori infection, chronic GORD, family history of gastric cancer, iron-deficiency anemia, previous gastric surgery 8 China 2019 Chinese guidelines for diagnosis and treatment of gastric cancer 2018 (English version) 26 Helicobacter pylori infection, chronic gastritis, gastric ulcer, previous gastric surgery, iron-deficiency anemia, family history of gastric cancer 9 China 2022 Chinese integrated guideline on the management of gastric precancerous conditions and lesions 12 Helicobacter pylori infection, gastric ulcer, chronic gastritis, autoimmune gastritis, previous gastric surgery, family history of gastric cancer 10 China 2024 The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023 27 Family history of gastric cancer, hereditary syndromes, iron-deficiency anemia 11 China 2024 Chinese national clinical practice guidelines on the prevention, diagnosis, and treatment of early gastric cancer 28 Iron-deficiency anemia, family history of gastric cancer, gastritis 12 Europe 2014 Gastric cancer: ESMO–ESSO–ESTRO clinical practice guidelines for diagnosis, treatment and follow-up 29 Helicobacter pylori infection, gastritis, previous gastric surgery, hereditary syndromes 13 Europe 2022 Gastric cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up 2 Helicobacter pylori infection, gastritis 14 Italy 2021 Beyond the guidelines: the grey zones of the management of gastric cancer. Consensus statements from the gastric cancer Italian network (gain) 30 Helicobacter pylori infection, chronic gastritis, autoimmune gastritis, dyspepsia, GERD, hereditary syndromes 15 International 2022 Proposal of minimum elements for screening and diagnosis of gastric cancer by an international Delphi consensus 31 Helicobacter pylori infection, gastritis, hereditary syndromes, Epstein-Barr virus, family history of gastric cancer 16 Japan 2006 Why does Japan have a high incidence of gastric cancer? Comparison of gastritis between UK and Japanese patients 37 Helicobacter pylori infection, gastritis, gastric ulcer 17 UK, Japan 2022 Gastric cancer screening in Japan: a narrative review 38 Helicobacter pylori infection, gastritis, family history of gastric cancer 18 Japan 2022 Risk factors for gastric cancer in Japan in the 2010s: a large, long-term observational study 39 Helicobacter pylori infection 19 Asia 2022 The burden and trend of gastric cancer and possible risk factors in five Asian countries from 1990 to 2019 40 Helicobacter pylori infectio Open in a new tab Table 6. Core functionalities of the proposed application and mobile health applications related to gastric and gastrointestinal cancer. App name App type Main focus Target population Medical guidelines Features Informative Directive Warning Wearable-integrated Healthcare-connected Proposed App (Iran, 2025) Mobile App Informative, Guiding, and Preventive mHealth App Targeting Precursor Conditions for Stage 0 Gastric Cancer General population Based on the Latest Medical Guidelines, reputable Sources and Expert opinion ✓ ✓ ✓ ✗ ✓ SurgeryDiaryv1 (Ming-Tsan Lin, Taiwan, 2015) 42 Mobile App, Tablet Perioperative care (nutritional monitoring, medical information management, drainage follow-up, wound care) Patients post-gastrectomy Clinical rules, expert opinion ✓ ✓ ✗ ✗ ✓ Life Manager (Ji Yeong Soh, Republic of Korea, 2018) 43 Android App Support for quality of life, nutrition, and rehabilitation Patients with advanced gastrointestinal cancer Clinical evidence, literature review, expert opinion ✓ ✓ ✓ ✓ ✓ iCancerHealth (Donna L. Berry, United States, 2018) 44 Mobile App, Web Symptom assessment and medication management Patients with gastrointestinal malignancies Standardized adverse event assessments, clinical guidelines ✓ ✓ ✓ ✗ ✓ Go Breath (Ji Yeong Soh, South Korea, 2019) 45 Android App Enhancing patient engagement in incentive spirometry post-surgery to prevent respiratory complications Patients with gastric cancer pre-surgery Expert opinion, clinical literature ✓ ✓ ✓ ✓ ✓ SurgeryDiaryv2 (Jin-Ming Wu, Taiwan, 2019) 46 Mobile App Postoperative physical activity monitoring Patients post-gastrectomy Not specified ✓ ✗ ✗ ✓ ✗ Source (Loïs F van de Water, Netherlands, 2021) 47 Web-based Prediction Tool Improving physician communication of treatment outcomes Patients with oesophageal and gastric cancer MRC 2008, CTCAE ✗ ✗ ✗ ✗ ✗ Atris (Marijke Elizabeth de Leeuwerk, Netherlands, 2022) 48 Mobile App, Web Self-monitoring of physical activity post-discharge Patients post-gastrointestinal or lung cancer surgery ERAS, expert opinion ✗ ✗ ✗ ✓ ✓ RestoreHome (Louise Brennan, Ireland, 2022) 49 Telehealth App Multidisciplinary rehabilitation programme Upper gastrointestinal cancer survivors MRC, clinical guidelines ✓ ✓ ✓ ✓ ✓ Tomorfight (Fatemeh Dinari, Iran, 2022) 50 Mobile App Management of chemotherapy-related adverse effects Patients with gastrointestinal cancer Related literature, clinical evidence ✓ ✗ ✓ ✗ ✗ myPace (Catherine E. Huggins, Australia, 2022) 51 Mobile App Behaviour-based nutritional intervention Patients with upper gastrointestinal cancer Not specified ✓ ✓ ✗ ✗ ✓ DPS (Krishna Moorthy, United Kingdom, 2023) 52 Mobile App Digital prehabilitation for physical and psychological wellbeing Patients with oesophago-gastric cancer Macmillan, NICE, ECCG, enhanced recovery protocols ✗ ✗ ✗ ✓ ✓ iNutrition (Xiaohan Jiang, China, 2023) 53 Mobile App Personalized nutritional intervention Patients post-gastrectomy ESPEN, postoperative cancer guidelines ✓ ✓ ✗ ✗ ✓ Ada Health (Germany) 54 Chatbot (Mobile, Web) Symptom assessment and condition suggestion General population AI-supported medical guidelines ✓ ✓ ✗ ✗ ✗ Buoy Health (United States) 54 Chatbot (Mobile, Web) Symptom checking, preliminary diagnoses, and care advice General population Over 18,000 clinical research studies ✓ ✓ ✗ ✗ ✗ Your.MD/ Healthily (United Kingdom) Chatbot (Mobile, Web) Symptom checking and care advice General population AI-supported medical guidelines ✓ ✓ ✗ ✗ ✗ Open in a new tab Table 5. Classification of precursor conditions for gastric cancer risk assessment. Category Precursor conditions Frequency in primarysources ( n = 19) Percentage Category 1: Baseline Risk Factors Family history of gastric cancer 15/19 79% Hereditary syndromes 13/19 68% Previous gastric surgery 11/19 58% Category 2: Diagnosed Medical Conditions Helicobacter pylori infection 19/19 100% Gastritis 14/19 74% Gastric ulcer 11/19 58% Iron-deficiency anemia 13/19 68% Epstein-Barr virus 8/19 42% Chronic gastritis 6/19 32% Autoimmune gastritis 4/19 21% Category 3: Functional Symptoms of Upper GI Tract Dyspepsia 9/19 47% Gastroesophageal reflux disease (GERD) 7/19 37% Bloating or discomfort 5/19 26% Indigestion 3/19 16% Epigastric pain 3/19 16% Heartburn 2/19 11% Open in a new tab Through systematic review of the selected sources, a set of precursor conditions of gastric cancer was identified and extracted, classified into three main categories: baseline risk factors, previously diagnosed medical conditions, and symptoms and functional disorders of the upper gastrointestinal tract. The greatest emphasis in the studies was on H. pylori infection (19 out of 19 primary sources), family history of gastric cancer (15 cases), and various types of gastritis (14 cases) as precursor conditions of gastric cancer. This classification, along with the frequency of each item, is presented in Table 5 and is described as follows: Category 1 : Baseline risk factors include family history of gastric cancer (15 cases, 79%), hereditary syndromes (13 cases, 68%), and previous gastric surgery (11 cases, 58%), which determine an individual's baseline risk level and are not under the individual's control. Category 2 : Diagnosed medical conditions include H. pylori infection (19 cases, 100%), iron-deficiency anemia (13 cases, 68%), gastritis (14 cases, 74%), gastric ulcer (11 cases, 58%), Epstein-Barr virus (8 cases, 42%), chronic gastritis (6 cases, 32%), and autoimmune gastritis (4 cases, 21%), which have been diagnosed by a physician and the individual is aware of their presence. Category 3 : Symptoms and functional disorders of the gastrointestinal tract include dyspepsia (9 cases, 47%), gastroesophageal reflux disease (GERD) (7 cases, 37%), bloating or discomfort (5 cases, 26%), indigestion (3 cases, 16%), epigastric pain (3 cases, 16%), and heartburn (2 cases, 11%), whose severity and persistence may indicate serious mucosal damage in the stomach and can be self-reported by the individual. The identified precursor conditions demonstrate distinct geographical and temporal patterns across different regions ( Table 3 ). Asian guidelines (including China, Japan, and the Asia-Pacific region) place the greatest emphasis on H. pylori infection and chronic gastric inflammation. For example, H. pylori infection is mentioned in all Asian sources (100%), and gastritis is mentioned in the vast majority of them, which is consistent with the high prevalence of gastric cancer in this region and the implementation of population-based screening programs. In contrast, Western guidelines (including ACG, ESMO, NCCN, and the UK guideline) focus more on hereditary and familial factors (family history: 15 cases, 79%; hereditary syndromes: 13 cases, 68%) and warning symptoms such as persistent dyspepsia (9 cases, 47%) and chronic reflux (7 cases, 37%). This reflects the lower prevalence of H. pylori and greater emphasis on individual-centered risk assessment in these regions. Temporally, as shown in Table 3 , older guidelines (before 2015) focused primarily on H. pylori and gastritis as the main factors, while newer guidelines (between 2020 and 2024) have highlighted autoimmune gastritis (4 cases, 21%) and Epstein-Barr virus (8 cases, 42%) as emerging factors. The overall frequency distribution of precursor conditions is presented in Tables 4 and 5 . Discussion As previously stated, the data requirements needed for designing an informative, directive, and preventive mobile health application for gastric cancer are classified into three main sections: educational data requirements, diagnostic data requirements related to precursor conditions of gastric cancer, and interactive data requirements for designing user-application conversations. The primary focus of this study was on the second section, namely, the identification, examination, and integration of data requirements related to precursor conditions of gastric cancer. Comprehensive identification of these conditions forms the foundation of preventive risk assessment, which in most current applications is either overlooked or covered in a very limited and general manner. Studies also show that, out of 109 mobile health applications related to gastrointestinal diseases evaluated in a comprehensive systematic review, only two applications were designed with authoritative guidelines, including the American Gastroenterological Association guidelines. 55 This is concerning, as alignment of mobile health applications with authoritative medical guidelines is essential to prevent misinformation and recommendations not based on evidence. The evidence-based approach employed in the conceptual design of this study is based on systematic analysis of 19 primary sources including international medical guidelines, consensus studies, and up-to-date research articles. This approach not only helps potentially improve accuracy in identifying individuals at risk but also enhances the reliability and credibility of the system among users and healthcare professionals. One of the key architectural decisions in the design of this system was the selection of a rule-based decision-making engine. Rule-based systems provide complete and deterministic control over medical accuracy and eliminate the risk of hallucination that exists in large language models (LLMs). Another advantage of such systems is the ability to completely trace back to specific statements in medical guidelines, which ensures explainability and auditability. Additionally, rule-based systems have lower computational requirements, making them suitable for deployment on mobile devices and in resource-limited settings. A further advantage is the simplicity of clinical validation, as all decision-making rules can be directly reviewed and tested by clinical experts, whereas AI-based systems with their black-box nature still face significant challenges for comprehensive validation. 15 – 17 To better understand the position of the proposed application among existing solutions, a comparison was made with mobile health applications related to gastric cancer and gastrointestinal diseases. Table 6 presents the key features of the proposed application and mobile health applications related to gastric cancer and gastrointestinal diseases. Review of mobile health applications related to gastric cancer such as Surgery Diary 42 LifeManager 43 , myPace 51 , and iCancerHealth 44 shows that their predominant focus is on post-diagnostic disease management. These applications are primarily designed for patients who have completed the diagnostic process and are now in the treatment or follow-up stage. Similarly, applications such as Tumorfight 50 iNutrition 53 Atris 48 ReStOre@Home 49 and DPS 52 , 58 which provide support services in nutritional, psychological, physical, or communication dimensions, primarily focus on improving quality of life for patients during chemotherapy, after surgery, or during pre-treatment periods. The function of all these programs is treatment-oriented and dependent on the post-diagnostic stage. Even in the case of health chatbots such as Ada Health, Buoy Health, and Your.MD, although these tools perform initial symptom analysis and offer preliminary suggestions, their focus is primarily on reactive symptom response following symptom onset, not on active and personalized prevention based on systematic assessment of risk factors and precursor conditions. Furthermore, these chatbots, due to their general nature, lack the necessary expertise in identifying the precursor cascade specific to gastric cancer. Table 4. Frequency of precursor conditions across primary sources. Term/symptom/risk factor 2 12 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 36 56 57 37 38 39 40 Frequency Helicobacter pylori ( H. pylori ) infection * * * * * * * * * * * * * * * * * * * * * * * * 19 Epstein-Barr virus * * * * * * * * * 8 Gastritis * * * * * * * * * * * * * * * * * * 14 Chronic gastritis * * * * * * * * * 6 Autoimmune gastritis * * * * * * 4 Gastric ulcer * * * * * * * * * * * * * * * 11 Family history of gastric cancer * * * * * * * * * * * * * * * * * * * 15 Hereditary syndromes * * * * * * * * * * * * * * * * 13 Dyspepsia * * * * * * * * * * 9 Chronic gastro-oesophageal reflux disease (GORD) * * * * * * * 7 Indigestion * * * 3 Bloating or Discomfort * * * * * 5 Epigastric pain * * * * 3 Heartburn * * 2 Previous Gastric Surgery * * * * * * * * * * * * * 11 Iron-deficiency anemia (unexplained) * * * * * * * * * * * * * * * * 13 Open in a new tab Note : Frequencies shown are from 19 primary sources only (Refs. 2 , 12 , 19 – 31 ). UpToDate sources (Refs. 32 , 33 , 36 , 56 , 57 ) were excluded from frequency calculations but used as supplementary confirmatory evidence. Count column represents frequency across primary sources only. Therefore, the main gap in the mobile health field for gastric cancer is the absence of solutions focused on primary prevention and early detection of precursor conditions. Most current mobile health applications in gastric cancer are implemented after disease diagnosis and are treatment-focused. These applications are primarily limited to management of pre- and post-surgical periods, control during oncological treatments, and care for cancer survivors. None of these applications focus on systematic identification of precursor conditions or stage 0 gastric cancer using the Correa cascade (chronic atrophic gastritis, intestinal metaplasia, dysplasia) 9 as the main framework for pre-diagnostic risk assessment. Furthermore, claims of alignment with clinical guidelines in some of these programs are limited or ambiguous. Another important limitation of these programs is that they primarily focus on diagnosed patients in oncological pathways and overlook the pre-diagnostic at-risk population. This issue essentially limits any potential impact on reducing disease incidence through primary prevention. Additionally, no existing solution provides the complete triad of information, guidance, and prevention in an integrated framework based on medical guidelines focused on gastric cancer prevention. The conceptual design of the proposed application in this study was developed to fill this gap. This design focuses on gastric cancer prevention, and its main decision-making engine is a deterministic, rule-based system—one that is completely built upon a structured framework of precursor conditions of gastric cancer ( Tables 3 through 4 ), extracted from systematic analysis of 19 primary sources and aligned with authoritative international medical guidelines. The five UpToDate sources were used solely as supplementary confirmatory evidence and were not involved in frequency calculations ( Tables 4 and 5 ). This structure enables personalized pre-diagnostic risk assessment and sending of directive and preventive alerts for the general population and at-risk groups, without the need for prior diagnosis or enrollment in a treatment center. Additionally, this design proposes the complete triad of information, guidance, and prevention in a lightweight and reliable framework for potential widespread use in various regions, especially areas with high prevalence of gastric cancer. One of the key differences of the proposed design compared to many existing mobile health applications is its scope of accessibility and potential usability for the general public. Most existing applications are specifically designed for patients or individuals receiving medical care, and consequently are not very usable or helpful for individuals without a specific medical history. In contrast, the proposed design with a preventive approach is accessible for use by the general population. This system, by receiving initial information (such as personal characteristics, lifestyle, family history, and initial gastrointestinal symptoms) and analyzing personal health patterns, guides users toward preventive care. Such an approach transforms the system into an educational, awareness-raising, and empowering tool that, by increasing public awareness and potentially enabling early detection of precursor conditions, can play an effective role in preventing gastric cancer. The logic of the conceptual design for rule-based risk assessment was extracted from the 19 primary sources identified in Tables 3 through 4 . By relying on priorities present in medical guidelines, such as emphasis on H. pylori eradication mentioned in 19 out of 19 primary sources (100%), family history of gastric cancer (15 cases, 79%), and gastritis (14 cases, 74%), the proposed program logic processes user inputs (family history, H. pylori status, duration and severity of symptoms, etc.) in a traceable manner. In the general population, the type and severity of recorded symptoms form the basis for guidance, such that in cases of moderate to high severity, directive alerts are issued for referral to a gastroenterologist, and in cases of critical severity, serious warnings along with continuous follow-ups and urgent referrals are provided. Furthermore, individuals at higher risk, for example those with family history (79% of sources) or hereditary syndromes (68% of sources), are identified in the initial assessment stage. The symptom inputs of this group are examined with greater emphasis and precision, and more specialized guidance is provided for them. Overall, this deterministic approach aligned with medical guidelines, extracted from this systematic review, presents a conceptual framework that is rule-based, reliable, lightweight, and accessible for the general population, especially in high-prevalence areas, integrating health informatics principles with practical and personalized preventive care. Integration of precursor data into a rule-based risk assessment framework The structured framework of precursor conditions of gastric cancer identified through systematic review in three main categories ( Tables 3 through 4 ) forms the foundation of the decision-making architecture for the proposed application. The main problem and challenge in translating this specialized knowledge into a tool usable by the general public is that ordinary users are not familiar with specialized medical terminology and overlook non-specific symptoms. To address this problem, each of the three main categories has been translated into a set of understandable questions and simple observations for general users. For Category 1 (baseline risk factors), questions are designed about family history (“Have any of your first-degree relatives—father, mother, sister, brother—been diagnosed with gastric cancer?”), hereditary syndromes (“Do you have any hereditary disease that increases cancer risk?”), and history of gastric surgery (“Have you previously had surgery on your stomach?”). For Category 2 (diagnosed medical conditions), questions are identified about H. pylori infection (“Has a doctor previously diagnosed you with stomach bacteria?” or “Have you had Helicobacter pylori infection?”), gastric inflammation or ulcer (“Has a doctor told you that you have gastric inflammation or gastric ulcer?”), Epstein-Barr virus, and iron-deficiency anemia (“Have your blood tests shown that you have anemia?”). For Category 3 (symptoms and functional disorders of the upper gastrointestinal tract), simple questions are designed about dyspepsia and related symptoms including indigestion, bloating, early satiety, heartburn, acid reflux, reflux, decreased appetite, along with assessment of duration (how many weeks?) and severity (mild, moderate, severe) of these symptoms. This translation from medical terminology to lay language enables users without specialized knowledge to answer the questions, and the system, based on the responses, classifies them into one of four risk levels (low, moderate, high, very high). The core philosophy of this design is to sensitize users to symptoms that are typically overlooked. The real value of the system lies in the fact that a similar symptom has different meanings in different individuals. For example, temporary indigestion in an individual without Category 1 or 2 factors requires education and periodic monitoring, but the same symptom in an individual with family history of gastric cancer (Category 1) or H. pylori infection (Category 2) and persistence of more than six weeks requires urgent specialist evaluation. The system, by asking simple questions in the form of a chatbot, collects this information and matches it with the three main categories of precursor conditions of gastric cancer, then classifies the user into one of four risk levels and finally provides directive recommendations based on the individual's characteristics and symptoms. Unlike machine learning-based systems, this architecture is completely transparent and traceable. Table 7 presents this framework and the adaptation of the three precursor categories to user-understandable questions. Table 7. Risk assessment framework for combining precursor conditions. Risk level Combination of precursor condition categories Logic and recommended action Very High Any of Categories 1 or 2 + Category 3 with high severity and duration >6 weeks Critical warning; urgent referral within 1 week; continuous symptom monitoring High Any of Categories 1 or 2 + Category 3 with moderate severity and duration >6 weeks Priority warning; referral within 2–4 weeks; symptom monitoring Moderate Any of Categories 1 or 2 + Category 3 with mild severity OR only Category 3 with moderate severity and duration >6 weeks Preventive interventions; lifestyle modification; periodic monitoring; escalation to high if no improvement after 1–2 months Low Only Category 3 (mild symptoms with recent onset) without Categories 1 or 2 Education; self-monitoring; escalation to moderate if persistence >2 weeks Open in a new tab Note: Category 1: Baseline risk factors; Category 2: Diagnosed medical conditions; Category 3: Functional symptoms of the upper gastrointestinal tract. While the proposed design in this study focuses on prevention and awareness with an innovative approach, there are limitations and significant operational challenges in its implementation. First, lack of connectivity to wearable devices and electronic health record (EHR) systems potentially limits the system's ability to collect comprehensive data and monitor users’ health status in real-time—a capability that is well implemented in advanced applications such as Atris and DPS. Additionally, lack of integration with the official national health system may weaken the patient referral process, treatment coordination, and linking of alerts to medical actions. 59 Another issue is adherence to security standards and data protection; weaknesses in encryption and lack of transparency in privacy policies affect user trust, especially in areas with sensitive medical data. 60 On the other hand, user acceptance, which is influenced by factors such as health literacy, digital culture, and attitudes toward technology, plays an important role in the potential effectiveness of the application. Distrust in system recommendations or lack of sufficient understanding of how data is analyzed and interpreted may lead to incorrect use or ignoring of key alerts. 34 Nevertheless, the proposed design with its lightweight architecture, potentially high usability, and lack of need for complex infrastructure can be considered as a model for smart preventive interventions in public health systems. Especially in countries like Iran where the burden of gastric cancer is high and access to specialized services is limited, this type of approach can help reduce some of the healthcare system burden through initial screening and data-driven guidance. Overcoming the mentioned challenges requires precise design based on international standards (such as ISO 27001 for data security, FHIR for interoperability), effective interaction with the health system, and strengthening users’ digital literacy so that such tools can establish their true position in the pathway of smart gastric cancer prevention. Future directions To evaluate the identified data requirements and address the main limitation of lack of operational testing, a two-phase structured evaluation framework is proposed for practical validation of this conceptual design, considering the real constraints of Iran's health system. Phase 1: usability and feasibility assessment A mixed-methods study will be conducted with general users and healthcare providers to assess initial acceptance level, users’ understanding of preventive recommendations, compatibility with clinical workflows, and clarity of decision-making logic and alert levels. Phase 2: pilot field evaluation in public health centers A quasi-experimental study will be conducted in public health centers in high-prevalence provinces. Participants will be assigned to either the intervention group (chatbot access) or control group (usual care). The primary outcome is the rate of timely referral to gastroenterology specialists among high-risk individuals, and secondary outcomes include adherence to preventive recommendations, health awareness improvement, lifestyle changes, and user engagement. This feasible, cost-effective design is compatible with Iran's health system capabilities and will provide the preliminary evidence necessary for improving the final version of the application and designing larger-scale future studies. Study limitations This study has several important limitations that must be considered in interpreting the findings. First, some regional medical guidelines that could have been helpful were not included in the search due to lack of access to English versions. Second, the data requirements extracted in this study constitute a conceptual design and have not been tested in practice. Therefore, the clinical effectiveness of this approach in reducing mortality rates, improving early detection, or changing preventive behaviors requires field studies and controlled trials. Furthermore, the clinical utility and user acceptance of the extracted data requirements have not yet been evaluated in real-world settings. Despite relying on guidelines and authoritative studies, diagnostic accuracy, sensitivity and specificity of the proposed system, false positive and false negative rates, and the extent of impact on actual clinical decisions have not been tested. Additionally, the generalizability of findings to populations with different epidemiological patterns requires investigation. This issue highlights the necessity of conducting field research and future validation studies, as elaborated in the “Future directions” section. Conclusion This study, through an evidence-based approach, has comprehensively identified data requirements related to precursor conditions of gastric cancer through systematic review of 19 primary sources including international clinical guidelines, consensus studies, and research articles, and has classified them into three main categories: (a) baseline risk factors, (b) diagnosed medical conditions, and (c) functional symptoms of the upper gastrointestinal tract. The most important finding of this study is the systematic identification of H. pylori infection (100% of primary sources), family history of gastric cancer (79%), and gastritis (74%) as key precursor conditions that provide a foundation for preventive risk assessment in the general population. The main gap identified in the literature is the absence of mobile health solutions focused on primary prevention and early detection of precursor conditions of gastric cancer. Review of existing applications showed that most are predominantly treatment-oriented and dependent on post-diagnostic stages, overlooking the pre-diagnostic at-risk population. This study fills this gap by presenting a conceptual framework for designing a rule-based mobile health application. The proposed framework, by relying on a deterministic decision-making engine, individual data analysis, and complete alignment with authoritative medical guidelines, offers the potential to create a conceptual infrastructure for preventive interventions, awareness-raising, and guidance toward specialized care. The advantage of this approach is its design for use by the general population and its potential to enhance health literacy at the population level. Unlike treatment-oriented systems that require prior diagnosis, this framework enables initial screening and personalized risk assessment without the need for immediate access to specialists. This feature is particularly important in countries with high gastric cancer prevalence and limited access to specialized services, such as Iran. However, this study has important limitations that must be considered in interpreting the findings. First, this is a conceptual design and has not been tested in practice; therefore, clinical effectiveness, user acceptance, and impact on health outcomes require validation in future field studies. Second, operational challenges such as lack of integration with health systems, data security considerations, and users’ digital 42 literacy must be addressed during implementation stages. Third, some regional guidelines were not included in the search due to language limitations, which may limit the generalizability of the findings. Overall, this study presents the first comprehensive evidence-based framework for data requirements of a preventive mobile health application for gastric cancer. The findings of this study provide a foundation for the design and development of preventive digital tools in the field of public health that have the potential to reduce the burden of gastric cancer through early detection and preventive interventions. Future studies should focus on practical implementation, clinical validation, and evaluation of the impact of this framework on health outcomes in different populations. Supplemental Material sj-docx-1-dhj-10.1177_20552076261425336 - Supplemental material for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer sj-docx-1-dhj-10.1177_20552076261425336.docx (22.8KB, docx) Supplemental material, sj-docx-1-dhj-10.1177_20552076261425336 for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer by Vahideh Zolfaghari, Hamid Moghaddasi, Reza Rabiei and Mitra Ahadi in DIGITAL HEALTH sj-docx-2-dhj-10.1177_20552076261425336 - Supplemental material for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer sj-docx-2-dhj-10.1177_20552076261425336.docx (14.6KB, docx) Supplemental material, sj-docx-2-dhj-10.1177_20552076261425336 for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer by Vahideh Zolfaghari, Hamid Moghaddasi, Reza Rabiei and Mitra Ahadi in DIGITAL HEALTH sj-docx-3-dhj-10.1177_20552076261425336 - Supplemental material for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer sj-docx-3-dhj-10.1177_20552076261425336.docx (14.3KB, docx) Supplemental material, sj-docx-3-dhj-10.1177_20552076261425336 for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer by Vahideh Zolfaghari, Hamid Moghaddasi, Reza Rabiei and Mitra Ahadi in DIGITAL HEALTH Footnotes ORCID iDs: Vahideh Zolfaghari https://orcid.org/0000-0001-6427-3292 Hamid Moghaddasi https://orcid.org/0000-0002-5906-0329 Reza Rabiei https://orcid.org/0000-0003-0771-7306 Mitra Ahadi https://orcid.org/0000-0002-3556-2834 Consent for publication: Not applicable. Contributors: Vahideh Zolfaghari did conceptualization, methodology, validation, investigation, resources, writing - original draft, writing - review & editing. Hamid Moghaddasi did conceptualization, methodology, validation, investigation, resources, writing - original draft, writing - review & editing, supervision, project administration. Reza Rabiei did validation, review, project administration. Mitra Ahadi did validation, review. Ethics approval and consent to participate: Not applicable. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Supplemental material: Supplemental material for this article is available online. References 1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209–249. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Lordick F, Carneiro F, Cascinu S, et al. Gastric cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol 2022; 33: 1005–1020. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Katai H, Ishikawa T, Akazawa K, et al. Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001–2007). Gastric Cancer 2018; 21: 144–154. [ DOI ] [ PubMed ] [ Google Scholar ] 4. Sumiyama K. Past and current trends in endoscopic diagnosis for early stage gastric cancer in Japan. Gastric Cancer 2017; 20: 20–27. [ DOI ] [ PubMed ] [ Google Scholar ] 5. Zheng P, Liu J. Cost-effectiveness analysis of Hp and new gastric cancer screening scoring system for screening and prevention of gastric cancer. Curr Oncol 2023; 30: 1132–1145. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Jeong Y, Mahar AL, Zagorski B, et al. Patient-level costs of treating metastatic gastric cancer by treatment strategy. Am Soc Clin Oncol 2018; 36: 167–167. [ Google Scholar ] 7. Yousefi M, Rezaei S, Khoshbaten M, et al. Cost-effectiveness analysis of different screening strategies for Helicobacter pylori infection in Iran: a model-based evaluation. Helicobacter 2023; 28: e13027. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Lage J, Uedo N, Dinis-Ribeiro M, et al. Surveillance of patients with gastric precancerous conditions. Best Pract Res Clin Gastroenterol 2016; 30: 913–922. [ DOI ] [ PubMed ] [ Google Scholar ] 9. Correa P, Piazuelo MB. The gastric precancerous cascade. J Dig Dis 2012; 13: –9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Nagayo T. Histological diagnosis of biopsied gastric mucosae with special reference to that of borderline lesions. Gann Monogr 1971; 11: 245–256. [ Google Scholar ] 11. Byrd DR, Brookland RK, Washington MK, et al. AJCC cancer staging manual. 8th ed. Springer Cham, 2017. [ Google Scholar ] 12. Wang P, Li P, Chen Y, et al. Chinese Integrated guideline on the management of gastric precancerous conditions and lesions. Chin Med 2022; 17: 38. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Kim H, Goldsmith JV, Sengupta S, et al. Mobile health application and e-health literacy: opportunities and concerns for cancer patients and caregivers. J Cancer Educ 2019; 34: –8. [ DOI ] [ PubMed ] [ Google Scholar ] 14. Marcolino MS, Oliveira JAQ, D’Agostino M, et al. The impact of mHealth interventions: systematic review of systematic reviews. JMIR Mhealth Uhealth 2018; 6: e8873. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Laymouna M, Ma Y, Lessard D, et al. Roles, users, benefits, and limitations of chatbots in health care: rapid review. J Med Internet Res 2024; 26: e56930. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Kovari A. Explainable AI chatbots towards XAI ChatGPT: a review. Heliyon 2025; 11: e42077. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Amann J, Blasimme A, Vayena E, et al. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 2020; 20: 10. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. Br Med J 2021; 372: n160. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Fock KM, Talley N, Moayyedi P, et al. Asia–Pacific consensus guidelines on gastric cancer prevention. J Gastroenterol Hepatol 2008; 23: 351–365. [ DOI ] [ PubMed ] [ Google Scholar ] 20. Shitara K, Fleitas T, Kawakami H, et al. Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with gastric cancer. ESMO Open 2024; 9: 102226. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Ajani JA, D’Amico TA, Bentrem DJ, et al. Gastric cancer, version 2.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Network 2022; 20: 167–192. [ DOI ] [ PubMed ] [ Google Scholar ] 22. Moayyedi PM, Lacy BE, Andrews CN, et al. ACG and CAG clinical guideline: management of dyspepsia. Offic J Am Colle Gastroenterol ACG 2017; 112: 988–1013. [ DOI ] [ PubMed ] [ Google Scholar ] 23. Peixoto RDA, Rocha-Filho DR, Weschenfelder RF, et al. Brazilian Group of Gastrointestinal Tumours’ consensus guidelines for the management of gastric cancer. ecancermedicalscience 2020; 14: 1126. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Barchi LC, Ramos MFKP, Dias AR, et al. Brazilian Gastric Cancer Association guidelines (part 2): update on treatment. ABCD Arquivos Brasileiros de Cirurgia Digestiva (São Paulo) 2021; 34: e1563. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Allum WH, Blazeby JM, Griffin SM, et al. Guidelines for the management of oesophageal and gastric cancer. Gut 2011; 60: 1449–1472. [ DOI ] [ PubMed ] [ Google Scholar ] 26. of the People NHC. Chinese guidelines for diagnosis and treatment of gastric cancer 2018 (English version). Chin J Cancer Res 2019; 31: 707. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Wang FH, Zhang XT, Tang L, et al. The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer, 2023. Cancer Commun 2024; 44: 127–172. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Li P, Li Z, Linghu E, et al. , Association SoDEotCM, Association CSGotCM. Chinese National clinical practice guidelines on the prevention, diagnosis, and treatment of early gastric cancer. Chin Med J 2024; 137: 887–908. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Waddell T, Verheij M, Allum W, et al. Gastric cancer: ESMO–ESSO–ESTRO clinical practice guidelines for diagnosis, treatment and follow-up. Eur J Surg Oncol 2014; 40: 584–591. [ DOI ] [ PubMed ] [ Google Scholar ] 30. Fornaro L, Spallanzani A, de Vita F, et al. Beyond the guidelines: the grey zones of the management of gastric cancer. Consensus statements from the gastric cancer Italian network (gain). Cancers (Basel) 2021; 13: 1304. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Kakushima N, Fujishiro M, Chan SM, et al. Proposal of minimum elements for screening and diagnosis of gastric cancer by an international Delphi consensus. DEN Open 2022; 2: e97. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Chan AOO, Wong B, Feldman M, et al. Risk factors for gastric cancer. UpToDate 2005; 9: 7. [ Google Scholar ] 33. Lamont JT, Feldman M. Association between Helicobacter pylori infection and gastrointestinal malignancy. 2008. 34. Mahachai V, Graham DY, Odze RD. Gastric polyps. UpToDate . Waltham, MA: Wolters Kluwer. 2015. 35. Morgan D, Feldman M, Grover S. Gastric intestinal metaplasia. UpToDate: науч электр б-ка: сайт-URL: https://pro uptodatefree ir/show/16636(дата обращения 1506 2022). 2017. 36. Chan A, Wong B. Gastric cancer screening. Waltham, MA, USA: UpToDate, 2020. [ Google Scholar ] 37. Naylor GM, Gotoda T, Dixon M, et al. Why does Japan have a high incidence of gastric cancer? Comparison of gastritis between UK and Japanese patients. Gut 2006; 55: 1545–1552. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Yashima K, Shabana M, Kurumi H, et al. Gastric cancer screening in Japan: a narrative review. J Clin Med 2022; 11: 4337. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Takahashi Y, Yamamichi N, Kubota D, et al. Risk factors for gastric cancer in Japan in the 2010s: a large, long-term observational study. Gastric Cancer 2022; 25: 481–489. [ DOI ] [ PubMed ] [ Google Scholar ] 40. Ning F-L, Lyu J, Pei J-P, et al. The burden and trend of gastric cancer and possible risk factors in five Asian countries from 1990 to 2019. Sci Rep 2022; 12: 5980. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Aromataris E, Fernandez R, Godfrey CM, et al. Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach. JBI Eviden Implement 2015; 13: 132–140. [ DOI ] [ PubMed ] [ Google Scholar ] 42. Wu JM, Yu HJ, Ho TW, et al. Tablet PC-enabled application intervention for patients with gastric cancer undergoing gastrectomy. Computer Methods and Programs in Biomedicine 2015; 119(2): 101–109. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Soh JY, Cha WC, Chang DK, et al. Development and validation of a multidisciplinary mobile care system for patients with advanced gastrointestinal cancer: interventional observation study. JMIR Mhealth Uhealth 2018; 6: e9363. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Berry D, Blonquist T, Nayak M, et al. Self-care support for patients with gastrointestinal cancer: iCancerHealth. Applied Clinical Informatics 2018; 9(4): 833–840. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Soh JY, Lee SU, Lee I, et al. A mobile phone–based self-monitoring tool for perioperative gastric cancer patients with incentive spirometer: Randomized controlled trial. JMIR mHealth and uHealth 2019; 7(2): e12204. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Wu J-M, Ho T-W, Chang Y-T, et al. Wearable-based mobile health app in gastric cancer patients for postoperative physical activity monitoring: focus group study. JMIR Mhealth Uhealth 2019; 7: e11989. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. van de Water LF, van den Boorn HG, Hoxha F, et al. Informing patients with Esophagogastric cancer about treatment outcomes by using a web-based tool and training: Development and evaluation study. Journal of Medical Internet Research 2021; 23(8): e27824. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. de Leeuwerk ME, Botjes M, van Vliet V, et al. Self-monitoring of physical activity after hospital discharge in patients who have undergone gastrointestinal or lung cancer surgery: mixed methods feasibility study. JMIR Cancer 2022; 8: e35694. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Brennan L, Sadeghi F, O’Neill L, et al. Telehealth delivery of a multi-disciplinary rehabilitation programme for upper gastro-intestinal cancer: ReStOre@ home feasibility study. Cancers (Basel) 2022; 14: 2707. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. Dinari F, Bahaadinbeigy K, Moulaei K, et al. Designing and evaluating a mobile-based self-care application for patients with gastrointestinal cancer to manage chemotherapy side effects. Med J Islam Repub Iran 2022; 36: 14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Huggins CE, Hanna L, Furness K, et al. Effect of early and intensive telephone or electronic nutrition counselling delivered to people with upper gastrointestinal cancer on quality of life: a three-arm randomised controlled trial. Nutrients 2022; 14: 3234. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Moorthy K, Halliday LJ, Noor N, et al. Feasibility of implementation and the impact of a digital prehabilitation service in patients undergoing treatment for oesophago-gastric cancer. Curr Oncol 2023; 30: 1673–1682. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Jiang X, Chen J, Yuan X, et al. Feasibility of an individualized mHealth nutrition (iNutrition) intervention for post-discharged gastric cancer patients following gastrectomy: a randomized controlled pilot trial. Nutrients 2023; 15: 1883. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Fraser H, Crossland D, Bacher I, et al. Comparison of diagnostic and triage accuracy of Ada health and WebMD symptom checkers, ChatGPT, and physicians for patients in an emergency department: Clinical data analysis study. JMIR mHealth and uHealth 2023; 11: e49995. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 55. Messner E-M, Sturm N, Terhorst Y, et al. Mobile apps for the management of gastrointestinal diseases: systematic search and evaluation within app stores. J Med Internet Res 2022; 24: e37497. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 56. Morgan D. Early gastric cancer: epidemiology, clinical manifistations, diagnosis, and staging. UpToDate Waltham, Mass: UpToDate2017. 2020. 57. Feldman M, Jensen PJ, Lamont JT. Gastritis etiology and diagnosis. UpToDate. 2021. 58. Vesel L, Hipgrave D, Dowden J, et al. Application of mHealth to improve service delivery and health outcomes: opportunities and challenges. African Popula Stud 2015; 29: 1683–1698. [ Google Scholar ] 59. Mandel JC, Kreda DA, Mandl KD, et al. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; 23: 899–908. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. Lynch JK, Fisk M. mHealth, trust and the security of personal data. In: Marston HR, Freeman S, Musselwhite C, eds. Mobile e-Health. Cham: Springer, 2017, pp.237–249. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials sj-docx-1-dhj-10.1177_20552076261425336 - Supplemental material for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer sj-docx-1-dhj-10.1177_20552076261425336.docx (22.8KB, docx) Supplemental material, sj-docx-1-dhj-10.1177_20552076261425336 for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer by Vahideh Zolfaghari, Hamid Moghaddasi, Reza Rabiei and Mitra Ahadi in DIGITAL HEALTH sj-docx-2-dhj-10.1177_20552076261425336 - Supplemental material for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer sj-docx-2-dhj-10.1177_20552076261425336.docx (14.6KB, docx) Supplemental material, sj-docx-2-dhj-10.1177_20552076261425336 for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer by Vahideh Zolfaghari, Hamid Moghaddasi, Reza Rabiei and Mitra Ahadi in DIGITAL HEALTH sj-docx-3-dhj-10.1177_20552076261425336 - Supplemental material for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer sj-docx-3-dhj-10.1177_20552076261425336.docx (14.3KB, docx) Supplemental material, sj-docx-3-dhj-10.1177_20552076261425336 for Data requirements of an informative, directive, and preventive mobile health application for gastric cancer by Vahideh Zolfaghari, Hamid Moghaddasi, Reza Rabiei and Mitra Ahadi in DIGITAL HEALTH Articles from Digital Health are provided here courtesy of SAGE Publications ACTIONS View on publisher site PDF (1023.9 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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