We tested the potential for recommender system technology to provide personalized physical activity (PA) suggestions for inactive young adults with high bodyweight. We developed a recommender system using data from the 2017 Behavioral Risk Factor Surveillance System and assessed interest in using the system among 47 young adults (mean age = 23.0 years; 63.4% female; 65.0% White; mean BMI = 29.4). Eleven of these participants (mean age = 23.6 years; 90.9% female, 63.6% White; average BMI = 28.5) also received a PA recommendation and a follow-up interview. Approximately half of the survey participants were willing to use the recommender system, and participants interested in the recommender system differed from those unwilling to try the system (e.g., more likely to be female, worse self-perceived health). Furthermore, eight of the 11 interviewees tried the PA recommended to them, but had mixed reviews of the system's accuracy. Although our recommender system requires improvements, such systems have promise for supporting PA adoption.
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Article Synopsis
- * The study tested GPT-4o as a virtual advisor, providing tailored recommendations for machine learning (ML) and deep learning (DL) algorithms based on researchers' specific data needs.
- * Results showed GPT-4o effectively recommended suitable algorithms for various radiology tasks, signaling its potential to bridge knowledge gaps and enhance research quality in the field.
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