(EA01-04-25) Personalized Nutrition Privacy Policies: A Recipe for Confusion

Scientific conference where the work was recently presented: 2024 Personalized Nutrition Innovation Day

Date on which the work was previously presented at the scientific conference listed: September 24, 2024 (University of Illinois Urbana-Champaign, Urbana, IL USA)

Objectives: To evaluate the types of user data collected by personalized nutrition companies based on their privacy policies and to assess if there were any differences by level of personalization. A secondary objective was to evaluate the readability of privacy policies and compare them by the level of personalization.

Methods: Sixty-eight personalized nutrition companies were categorized based on their level of personalization: Survey-based, Biological, Advanced Biological, or Integrated. Privacy policies were then analyzed for the kinds of data collected and readability metrics: word count, words per sentence, Flesch-Kincaid Grade Level.

Results: Eight data categories were developed, and these represented a total of 46 subcategories shown in parentheses: Personally Identifiable Information (21); Personal Background (8); Registration Information (2); Anthropometric Measurements (2); Software Information (2); Security Measures (3); User Content (3); and Data Sharing Consent (2). There were no significant differences in the frequency of data categories collected and used among the four types of companies, except for personal background, email addresses, and genetic data. Nor were there any significant differences between company types for the word count (3,530 ± 2,490, P=.40), words per sentence (22.9 ± 4.2, P=.14), or reading level (13.7 ± 1.6, P=.46) of the privacy policies.

Conclusions: Personalized nutrition companies collect a range of personal information, and the categories of data collected do not depend on the personalization level. In addition, their privacy policies are written at a collegiate level, potentially limiting comprehension for a significant portion of the U.S. population. This complexity may undermine the intended purpose of information consumers about data collection and sharing practices. This research highlights the need for improved transparency and clarity regarding the types of data collected from users and the accessibility and readability of personalized nutrition privacy policies.

Funding Sources: Personalized Nutrition Initiative, University of Illinois Urbana-Champaign