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PRODID:Linklings LLC
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TZID:America/Phoenix
X-LIC-LOCATION:America/Phoenix
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TZOFFSETFROM:-0700
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DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203101Z
LOCATION:Grand Ballroom
DTSTART;TZID=America/Phoenix:20240911T113500
DTEND;TZID=America/Phoenix:20240911T115500
UID:HFESAM_ASPIRE - Presented by HFES_sess196_LECT478@linklings.com
SUMMARY:Can We Predict Shoe-Floor Friction by Analyzing Amazon Reviews Wit
 h ChatGPT?
DESCRIPTION:Lecture\n\nGerard Aristizábal Pla (University of Pittsburgh)\n
 \nCustomer reviews of slip resistant shoes have the potential to become an
  inexpensive and readily available resource for assessing the friction per
 formance of slip resistant shoes. The purpose of this study is to assess w
 hether narrative reviews analyzed with ChatGPT can predict friction perfor
 mance of slip resistant shoes. Specifically, we investigated the correlati
 on between a score developed form narrative reviews, the overall satisfact
 ion ratings, and available coefficient of friction (ACOF). Twenty slip-res
 istant shoes were tested with a slip tester, and their reviews were catego
 rized by generative artificial intelligence using a prompt that targeted f
 riction performance. Results show a weak association between narrative sco
 re and ACOF, an association between narrative score and overall satisfacti
 on, and an association between overall satisfaction and ACOF. In conclusio
 n, our results suggest a moderate correlation between a person's overall s
 atisfaction and objective slip resistance measures like ACOF.\n\nTrack: He
 alth Care\n\nSession Chair: Tosin Akintunde (University of Toronto)
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