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Background: Mobile health (mHealth) apps have the potential to enhance health care service delivery. However, concerns regarding patients' confidentiality, privacy, and security consistently affect the adoption of mHealth apps. Despite this, no review has comprehensively summarized the findings of studies on this subject matter.
Objective: This systematic review aims to investigate patients' perspectives and awareness of the confidentiality, privacy, and security of the data collected through mHealth apps.
Methods: Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a comprehensive literature search was conducted in 3 electronic databases: PubMed, Ovid, and ScienceDirect. All the retrieved articles were screened according to specific inclusion criteria to select relevant articles published between 2014 and 2022.
Results: A total of 33 articles exploring mHealth patients' perspectives and awareness of data privacy, security, and confidentiality issues and the associated factors were included in this systematic review. Thematic analyses of the retrieved data led to the synthesis of 4 themes: concerns about data privacy, confidentiality, and security; awareness; facilitators and enablers; and associated factors. Patients showed discordant and concordant perspectives regarding data privacy, security, and confidentiality, as well as suggesting approaches to improve the use of mHealth apps (facilitators), such as protection of personal data, ensuring that health status or medical conditions are not mentioned, brief training or education on data security, and assuring data confidentiality and privacy. Similarly, awareness of the subject matter differed across the studies, suggesting the need to improve patients' awareness of data security and privacy. Older patients, those with a history of experiencing data breaches, and those belonging to the higher-income class were more likely to raise concerns about the data security and privacy of mHealth apps. These concerns were not frequent among patients with higher satisfaction levels and those who perceived the data type to be less sensitive.
Conclusions: Patients expressed diverse views on mHealth apps' privacy, security, and confidentiality, with some of the issues raised affecting technology use. These findings may assist mHealth app developers and other stakeholders in improving patients' awareness and adjusting current privacy and security features in mHealth apps to enhance their adoption and use.
Trial Registration: PROSPERO CRD42023456658; https://tinyurl.com/ytnjtmca.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11179037 | PMC |
http://dx.doi.org/10.2196/50715 | DOI Listing |
Neurosurgery
September 2025
Department of Neurosurgery, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA.
The integration of mobile health (mHealth) technologies is transforming neurosurgery. Despite its potential, many uses remain unrealized due to the unique challenges and complexity of developing mHealth technology. While neurosurgeons bring invaluable clinical expertise and an understanding of patient needs, the technical intricacies of application development often require collaboration with developers and computer scientists, a process that can feel unfamiliar and difficult to navigate.
View Article and Find Full Text PDFEpidemiology
September 2025
Population Science, American Cancer Society, Atlanta, Georgia, US.
Background: Linking cancer cohort participants to state cancer registries typically relies on personally identifiable information, including Social Security Numbers (SSN), which uniquely identify individuals. However, complete SSN collection can be limited due to privacy concerns. This study evaluates the sensitivity of cancer registry linkage using partial or missing SSN and examines differences by demographic characteristics.
View Article and Find Full Text PDFJ Obes Metab Syndr
September 2025
Department of Medicine, College of Medicine, Kyung Hee University, Seoul, Korea.
Although the prevalence of obesity is increasing worldwide, related treatment remains a complex challenge that requires multidimensional approaches. Recent advancements in artificial intelligence (AI) have led to the development of multimodal methods capable of integrating diverse types of data. These AI approaches utilize both multimodal data integration and multidimensional feature representations, enabling personalized, data-driven strategies for obesity management.
View Article and Find Full Text PDFPLOS Digit Health
September 2025
Department of Dermatology, Stanford University, Stanford, California, United States of America.
Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While these models demonstrate race-based and binary gender biases, anti-LGBTQIA+ bias remains understudied despite documented healthcare disparities affecting these populations. In this work, we evaluated the potential of LLMs to propagate anti-LGBTQIA+ medical bias and misinformation.
View Article and Find Full Text PDFJ Vis Exp
August 2025
School of Cyberspace Security, Zhengzhou University.
In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data.
View Article and Find Full Text PDF