Five years of app evaluation: Insights from a framework in practice - a systematic review on the m-health index and navigation database

Internet Interv. 2025 Nov 15;42:100888. doi: 10.1016/j.invent.2025.100888. eCollection 2025 Dec.

ABSTRACT

INTRODUCTION: As the number of health apps continues to rise, concerns about their quality, privacy standards, and adherence to evidence-based healthcare remain. While multiple frameworks exist to assess app quality, a standardized, cross-domain approach is lacking. The M-Health Index and Navigation Database (MIND) comprises a structured framework for app evaluation and a publicly accessible database that applies this framework to rate individual apps. This allows for longitudinal tracking of quality metrics and comparisons across different health app categories.

METHODS: We conducted a systematic review to identify studies that utilized either the MIND database or its framework to evaluate health apps. Studies were included if they used MIND in their methodology to evaluate health apps. Data were synthesized descriptively, and two-proportion z-tests were applied for comparisons.

RESULTS: We identified 22 studies, including 16 evaluating commercially available health apps. The most frequently assessed metrics were privacy policies, operating system compatibility, cost, and evidence backing. Only 15 % of mental health apps were supported by feasibility or efficacy studies, nearly one-fourth lacked a privacy policy, and 44 % explicitly disclosed sharing personal health information with third parties. Similar deficiencies were found across non-mental health domains, indicating that concerns regarding app quality and data privacy may not be unique to mental health.

DISCUSSION: Our findings suggest that evidence and privacy concerns are prevalent across almost all health app categories, highlighting the need for stronger regulatory oversight and improved validation standards. MIND serves as a valuable tool for evaluating digital health apps, supporting both app selection and cross-domain quality comparisons.

PMID:41341222 | PMC:PMC12670919 | DOI:10.1016/j.invent.2025.100888