Examining the Influence of Demographic and Socioeconomic Factors on Disparities in Health Care App Usage: Protocol for a Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: The rapid proliferation of health care apps has transformed health care delivery, providing patients with unprecedented access to medical information and services. These apps facilitate remote consultations, appointment scheduling, medication reminders, and health monitoring, thereby enhancing patient engagement and improving health outcomes. Despite the widespread benefits, disparities in the adoption and usage of health care apps persist, influenced by demographic and socioeconomic factors. Understanding these disparities is crucial for designing interventions that promote equitable access to digital health tools. OBJECTIVE: This systematic review aims to identify and synthesize empirical studies on health care app usage disparities, focusing on demographic and socioeconomic factors. This review seeks to inform stakeholders about the key factors influencing app usage and provide insights to improve accessibility and effectiveness. Specifically, this review addresses the following research questions: (1) what are the key demographic and socioeconomic factors associated with health care app usage disparities? (2) how do these factors influence the adoption and utilization of health care apps? and (3) what are the barriers to and facilitators of effective use of health care apps? METHODS: This review will adhere to PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. Eight databases-ACM Digital Library, CINAHL, IEEE Xplore, ProQuest Nursing and Allied Health Journals, PubMed/MEDLINE, ScienceDirect, Scopus, and Web of Science-were searched for studies published in English between January 1, 2014, and June 17, 2024. Eligibility criteria include journal papers focusing on health care app usage across different demographic and socioeconomic groups. Data management will involve using Zotero for reference management and Excel for screening and eligibility assessment. Two reviewers will independently extract the data and assess the study quality and risk of bias. Descriptive statistics will be used to summarize the study characteristics. RESULTS: As of June 2025, the review is in the screening stage. The completion of data collection is anticipated by November 2025. The final results are expected to be published by late 2025. This review aims to provide comprehensive insights into the disparities in health care app usage. CONCLUSIONS: The findings of this systematic review will offer valuable insights into demographic and socioeconomic disparities in health care app usage, informing stakeholders on how to address these disparities. By identifying the factors influencing app adoption and usage, this review will contribute to the development of targeted interventions and policies to enhance digital health equity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63596.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.124 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.021 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".