# Model to personalize mobiles applications according to the gamification user types for health behavioral change

## TL;DR

This study shows how different user types in mHealth apps respond to various gamification strategies, helping to personalize app design for better health behavior change.

## Contribution

The study identifies novel associations between user typologies and gamification mechanisms for mHealth app personalization.

## Key findings

- Self-monitoring, progression, challenge, and quest were preferred by over 50% of participants.
- Players favored collection and rewards, while philanthropists were less likely to prefer collection.
- Socializers preferred cooperation but avoided behavior demonstration, while free spirits favored it.

## Abstract

Adopting healthy habits improves longevity and well-being. Mobile health (mHealth) apps support such behaviors, with over 35,000 available as of 2018. Personalization and gamification are recognized as effective strategies to enhance user engagement and behavioral outcomes in mHealth applications.

This cross-sectional study explores links between user typologies (Hexad Scale) and preferences for 15 game and behavior change mechanisms.

A preference matrix, derived from the literature, was tested on data from 214 respondents (Mage = 29.42; 118 women, 89 men, 5 other). Demographics and Hexad-based user typologies were recorded. Participants selected their top five mechanisms from 15 randomized mockups with brief descriptions. Logistic and ordinal logistic regressions, with Bonferroni correction, were used to assess associations.

Significant associations were observed for five mechanisms. Philanthropists were less likely to prefer collection (OR = 0.77, p < 0.01), whereas players favored it (OR = 1.11, p < 0.05) and showed strong preferences for rewards (OR = 1.39, p < 0.01) and, to a lesser extent, self-monitoring (OR = 0.88, p < 0.05). Socializers preferred cooperation (OR = 1.14, p < 0.01) but were less inclined toward demonstration of behavior (OR = 0.92, p < 0.05). Free spirits favored demonstration of behavior (OR = 1.25, p < 0.01), while achievers were less likely to prefer it (OR = 0.86, p < 0.05). Four mechanisms, self-monitoring, progression, challenge, and quest were selected by over 50% of participants.

This study validated the preference matrix, highlighting four mechanisms, self-monitoring, progression, challenge, and quest, as broadly appealing across user profiles for mHealth design. Three novel profile–mechanism associations were identified, refining the model and underscoring the need for replication with a more diverse sample.