Accepted for/Published in: JMIR Human Factors
Date Submitted: Jun 16, 2025
Open Peer Review Period: Jun 23, 2025 - Aug 18, 2025
Date Accepted: Jan 13, 2026
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Personalizing Mobile Applications for Health Behavioral Change according to personality: cross-sectional validation of a Preference matrix
ABSTRACT
Background:
mHealth apps are increasingly popular, offering tools like health tracking and personalized reminders to support these behaviors. Personalized messaging, tailored to the user’s profile, has been shown to improve engagement and retention around health topics. Research links personality traits (based on the Big Five model) with preferred app mechanisms, leading to a preference matrix for personalizing health apps. This preference matrix includes 15 mechanisms, categorized by the Behavior Change Technique and gamification elements, guiding developers to optimize app engagement based on user profiles.
Objective:
This study aims to validate this preference matrix by assessing whether the associations between mechanisms and Big Five personality profiles proposed in the preference matrix align with the preferences of our population in an experimental context.
Methods:
This study employs a cross-sectional design. Participants completed an online survey, which collected data on demographic information, mobile health app usage, and personality. Logistic regression and logistic ordinal regression analyses were performed, adjusted using the Bonferroni correction.
Results:
The average age of the 214 respondents (118 women, 89 men, 5 others), was 29.42. Conscientiousness significantly increased the likelihood of selection for collection (OR = 1.87). For competition, both conscientiousness (OR = 3.22) and altruism (OR = 1.93) emerged as strong predictors. For rewards, conscientiousness (OR = 1.97) and neuroticism (OR = 2.36) also showed a strong predictive value. The study found that four mechanisms, self-monitoring, progression, challenge, and quest, were favored by over half of the participants.
Conclusions:
Conscientious participants showed a preference for the collection mechanism, while both conscientious and altruistic individuals were drawn to competition. Neurotic and conscientious individuals preferred the reward mechanism. Conscientiousness consistently predicted preferences for all three gamification elements, highlighting its role in influencing engagement with mHealth features.
Citation
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Copyright
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