Cognitive Load, Performance, Gait Analysis
Conditions
Keywords
gait, cognitive load, cognitive loading
Brief summary
The goal of this study is to determine how different types of cognitive loading affect the gait of an individual and its association with growing age. The main aim is to find out if: 1. There is a significant difference in the effect of three different methods of cognitive loading on gait parameters across age groups. 2. There is an association of cognitive loading with different age groups.
Interventions
Arithmetic test (Backward counting with serial 3 subtraction and articulation): participants will be asked to count out loud backward with serial subtraction of 3 from each number, starting with a random number provided by the researcher.
Stroop colour word test (modified Stroop test): participants will be asked to name the colour of ink that each word is printed in. This test will appear on the mobile phone in their hands while they walk to increase the effect of cognitive loading.
Participants will be asked to hold a tray of glasses filled with water and walk 10 meters to calculate the effect of cognitive loading on gait.
Sponsors
Study design
Intervention model description
research
Eligibility
Inclusion criteria
* Both male and female genders. * Age between 21-70 years. * Healthy individuals with normal systemic history. * Individuals with normal cognitive level (score between 0-7 on 6CIT test)
Exclusion criteria
* Individuals having any comorbidities. * Individuals having diagnosed gait disorders/deviations. * Non-cooperative participants
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Walking speed | Baseline | A smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the walking speed of the participant. A decrease in walking speed is the usual effect of cognitive loading on this gait parameter. |
| Gait Symmetry | Baseline | A smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the gait symmetry of the participant. A decrease in gait symmetry is the usual effect of cognitive loading on this gait parameter. |
| Step length | Baseline | A smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the step length of the participant. An increase in step length is the usual effect of cognitive loading on this gait parameter. |
| Step length variability | Baseline | A smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the step length variability of the participant. An increase in step length variability is the usual effect of cognitive loading on this gait parameter. |
| Step time | Baseline | A smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the step time of the participant. An increase in step time is the usual effect of cognitive loading on this gait parameter. |
| Step time variability | Baseline | Smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the step time variability of the participant. An increase in step time variability is the usual effect of cognitive loading on this gait parameter. |
| Step length asymmetry | Baseline | A smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the step length asymmetry of the participant. An increase in step length asymmetry is the usual effect of cognitive loading on this gait parameter. |
| Step time asymmetry | Baseline | A smartphone-based accelerometer through a mobile app named Gait & Balance (G&B app) will be used to detect the step time asymmetry of the participant. An increase in step time asymmetry is the usual effect of cognitive loading on this gait parameter. |
Countries
Pakistan