In plain language
Athletes are asked to fill out a lot of questionnaires — in many high-performance sport programs they complete them weekly or even daily. Long questionnaires are one of the most common reasons athletes skip them or answer carelessly. This study asked whether machine learning could shrink a popular questionnaire, the Mindfulness Inventory for Sport, without losing the qualities that make it scientifically useful.
The researchers used genetic algorithms — a machine-learning technique inspired by natural selection — to search for the best possible shorter combinations of items. They built and tested the short versions using data from 859 undergraduate exercise science students and 118 golfers, checking internal consistency, test-retest reliability, content validity, factorial validity, and links with other mindfulness measures, golf handicaps, and an objective putting-accuracy task.
The genetic algorithms efficiently produced stable short versions of the measure. Reliability dipped as the questionnaire got shorter — especially when going from three to two items per subscale — but stayed acceptable, and the validity of the short versions was as good as, and sometimes better than, the full questionnaire. The work shows that machine learning can make athlete monitoring faster, cheaper, and easier to comply with, without sacrificing measurement quality.
Key findings
- Genetic algorithms efficiently generated stable shortened versions of the Mindfulness Inventory for Sport.
- Reliability decreased as the measure became shorter — most noticeably between three and two items per subscale — but remained acceptable.
- Validity metrics for the shorter versions were as good as, and sometimes better than, the full questionnaire.
- The awareness and refocusing subscales showed weak associations with golf handicap for both long and short versions.
- The non-judgment subscale showed no significant associations with golf handicap, and no subscales significantly predicted putting performance.
- Short versions were developed on 75% of a sample of 859 students and validated on the remaining data plus 118 golfers.
How to cite
APA
Noetel, M., Ciarrochi, J., Sahdra, B., & Lonsdale, C. (2019). Using genetic algorithms to abbreviate the Mindfulness Inventory for Sport: A substantive-methodological synthesis. Psychology of Sport and Exercise, 45, 101545. https://doi.org/10.1016/j.psychsport.2019.101545
BibTeX
@article{noetel2019using,
title = {Using genetic algorithms to abbreviate the Mindfulness Inventory for Sport: A substantive-methodological synthesis},
author = {Noetel, Michael and Ciarrochi, Joseph and Sahdra, Baljinder and Lonsdale, Chris},
journal = {Psychology of Sport and Exercise},
volume = {45},
pages = {101545},
year = {2019},
doi = {10.1016/j.psychsport.2019.101545}
}
Related work
- All publications by Joseph Ciarrochi (searchable, with free PDFs)
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Author: Joseph Ciarrochi (ORCID 0000-0003-0471-8100). Free copy hosted with permission for scholarly use. Please cite the published version via the DOI above.