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Published on: February 14, 2014
Normative data for the Grooved Pegboard Test in Quebec-French middle-aged and older adults
Sophie-Anne Giguère1, Sophie Chantal2,3, Mariane Landry1,3,4
1École de psychologie, Université Laval, Québec, Canada.
This study provides normative data for the Grooved Pegboard Test (GPT) in middle-aged and older Quebec adults. Performance varies by age, sex, and handedness, aiding in identifying psychomotor impairments.
Area of Science:
- Neuroscience
- Gerontology
- Psychomotor Function
Background:
- The Grooved Pegboard Test (GPT) is a standard tool for assessing fine motor skills in older adults.
- Sociodemographic factors have been shown to influence GPT performance in previous research.
- Developing population-specific normative data is crucial for accurate clinical interpretation.
Purpose of the Study:
- To establish normative data for the Grooved Pegboard Test (GPT) in the middle-aged and elderly Quebec-French population.
- To investigate the influence of age, sex, education, and handedness on GPT performance in this demographic.
- To provide a basis for identifying psychomotor impairments in this specific population.
Main Methods:
- A sample of 266 individuals aged 50-90 years from Quebec participated.
- Multiple regression analyses were used to examine the relationship between demographic variables and GPT performance.
- Performance was assessed on trials involving both dominant and non-dominant hands.
Main Results:
- GPT performance showed a positive association with female sex and a negative association with age across all trials.
- Left-handedness was linked to better performance on non-dominant hand trials.
- An interaction between age and education positively influenced performance on the second non-dominant hand trial. Normative data were generated using regression equations.
Conclusions:
- The developed normative data will aid in the accurate assessment of psychomotor function in middle-aged and elderly Quebec-French individuals.
- Findings highlight the importance of considering demographic factors like age, sex, and handedness in GPT interpretation.
- Limitations include a gender imbalance in the sample, potentially affecting generalizability for men, particularly those with lower education levels.
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