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Understanding contributing factors to exoskeleton use-intention in construction: a decision tree approach using results from an online survey.
Kim, Sunwook; Ojelade, Aanuoluwapo; Moore, Albert; Gutierrez, Nancy; Harris-Adamson, Carisa; Barr, Alan; Srinivasan, Divya; Rempel, David M; Nussbaum, Maury A.
Afiliação
  • Kim S; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA.
  • Ojelade A; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA.
  • Moore A; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA.
  • Gutierrez N; School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
  • Harris-Adamson C; Department of Medicine, University of California, San Francisco, CA, USA.
  • Barr A; Department of Medicine, University of California, San Francisco, CA, USA.
  • Srinivasan D; Department of Industrial Engineering, Clemson University, Clemson, SC, USA.
  • Rempel DM; Department of Medicine, University of California, San Francisco, CA, USA.
  • Nussbaum MA; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA.
Ergonomics ; : 1-14, 2023 Dec 12.
Article em En | MEDLINE | ID: mdl-38085690
ABSTRACT
Work-related musculoskeletal disorders (WMSDs) are a major health concern in the construction industry. Occupational exoskeletons (EXOs) are a promising ergonomic intervention to help reduce WMSD risk. Their adoption, however, has been low in construction. To understand the contributing factors to EXO use-intention and assist in future decision-making, we built decision trees to predict responses to each of three EXO use-intention questions (Try, Voluntary Use, and Behavioural Intention), using online survey responses. Variable selection and hyperparameter tuning were used respectively to reduce the number of potential predictors and improve prediction performance. The importance of variables in each final tree was calculated to understand which variables had a greater influence. The final trees had moderate prediction performance. The root node of each tree included EXOs becoming standard equipment, fatigue reduction, or performance increase. Important variables were found to be quite specific to different decision trees. Practical implications of the findings are discussed.Practitioner

summary:

This study used decision trees to identify key factors influencing the use-intention of occupational exoskeletons (EXOs) in construction, using online survey data. Key factors identified included EXOs becoming standard equipment, fatigue reduction, and performance improvement. Final trees provide intuitive visual representations of the decision-making process for workers to use EXOs.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article