Attitudes towards personalised nutrition recommendations in health apps: Results of a cross-sectional online survey.

OBJECTIVE: Dietary advice is increasingly shifting towards personalisation enabled by digital technologies. As personalised nutrition recommendation applications rely on user data, little is known about how users differ in attitudes towards data sharing, desired app features, and expected benefits. METHODS: In a cross-sectional survey conducted in Germany, adults aged ≥18 years completed a standardised online-questionnaire assessing participant characteristics and attitudes towards personalised nutrition in health applications. Behavioural intention (BI) was measured using the Technology Acceptance Model 3 questionnaire. Four items yielded count-based outcomes (number of selected options). Descriptive statistics, generalised additive models, and exploratory cluster analysis based on age, body mass index (BMI), and BI were conducted using RStudio. RESULTS: In total, 1,070 participants completed this cross-sectional survey (42.4 ± 15.3 years; BMI 25.6 ± 6.0 kg/m2; 75% female). An exploratory cluster analysis (age, BMI, BI) identified three user groups. Clusters differed in expected benefits, willingness to share parameters, core aspects of personalised advice, and perceived importance of app features (all P ≤ 0.01), with small to moderate effect sizes (η2 = 0.006 - 0.10). CONCLUSION: Age, BMI, and BI modify users' attitudes towards personalised dietary advice in health apps, while their overall explanatory power was limited. Responses varied within similar clusters, suggesting apps should be flexible and modular to accommodate individual preferences.