AI-Enabled Wearables for Fitness and Preventive Health
Author(s)
Jorge Brantes Ferreira, PhD1, Jorge Ferreira da Silva, PhD2, Fernanda Leao Ramos, PhD2, Cristiane Junqueira Giovannini, PhD2, Daniel Brantes Ferreira, PhD3.
1Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 3Ambra University, Orlando, FL, USA.
1Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 3Ambra University, Orlando, FL, USA.
OBJECTIVES: This study sought to explore how health beliefs influence use of AI-enabled fitness wearables for preventive health in Brazil, using the Health Belief Model (HBM). The research examines constructs like perceived susceptibility to chronic diseases, benefits of monitoring, and barriers to self-monitoring among general adults, highlighting behavioral motivation in preventive care.
METHODS: An online survey of 600 Brazilian adults assessed HBM constructs: perceived susceptibility to chronic diseases, perceived severity, perceived benefits (e.g., improved fitness, weight control), perceived barriers (cost, data privacy, complexity), cues to action (social media campaigns, physician advice), and self-efficacy for exercise. Wearable usage (e.g., fitness trackers) was recorded. Hierarchical cluster analysis identified population segments based on belief profiles and wearable adoption.
RESULTS: Three distinct clusters emerged: “Health-Motivated” (high perceived risk and benefit, ~65% wearable adoption), “Barrier-Centered” (high barriers, moderate risk perception, ~30% adoption), and “Low-Concern” (low perceived risk, low adoption at ~10%). The Health-Motivated cluster demonstrated a strong belief in the benefits and cues to action, resulting in high adoption rates. The Barrier-Centered group cited cost and privacy as major obstacles despite recognizing benefits. Low-concern individuals doubted the personal risk and saw minimal benefit, resulting in very low adoption rates.
CONCLUSIONS: Adoption of AI fitness wearables is driven by individuals’ health beliefs. Interventions for preventive health should target belief barriers: for example, providing subsidized devices and privacy assurances to the Barrier-Centered group, and risk-awareness campaigns for Low-Concern individuals. These insights inform public health policies on wearable incentives and education aimed at enhancing preventive health behaviors in Brazil. Tailored interventions (such as targeted education and subsidized device programs) can overcome belief-driven barriers and increase wearable use.
METHODS: An online survey of 600 Brazilian adults assessed HBM constructs: perceived susceptibility to chronic diseases, perceived severity, perceived benefits (e.g., improved fitness, weight control), perceived barriers (cost, data privacy, complexity), cues to action (social media campaigns, physician advice), and self-efficacy for exercise. Wearable usage (e.g., fitness trackers) was recorded. Hierarchical cluster analysis identified population segments based on belief profiles and wearable adoption.
RESULTS: Three distinct clusters emerged: “Health-Motivated” (high perceived risk and benefit, ~65% wearable adoption), “Barrier-Centered” (high barriers, moderate risk perception, ~30% adoption), and “Low-Concern” (low perceived risk, low adoption at ~10%). The Health-Motivated cluster demonstrated a strong belief in the benefits and cues to action, resulting in high adoption rates. The Barrier-Centered group cited cost and privacy as major obstacles despite recognizing benefits. Low-concern individuals doubted the personal risk and saw minimal benefit, resulting in very low adoption rates.
CONCLUSIONS: Adoption of AI fitness wearables is driven by individuals’ health beliefs. Interventions for preventive health should target belief barriers: for example, providing subsidized devices and privacy assurances to the Barrier-Centered group, and risk-awareness campaigns for Low-Concern individuals. These insights inform public health policies on wearable incentives and education aimed at enhancing preventive health behaviors in Brazil. Tailored interventions (such as targeted education and subsidized device programs) can overcome belief-driven barriers and increase wearable use.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MT4
Topic
Medical Technologies
Topic Subcategory
Digital Health
Disease
No Additional Disease & Conditions/Specialized Treatment Areas