Journal of Neurosciences in Rural Practice
 
ORIGINAL ARTICLE
Year : 2017  |  Volume : 8  |  Issue : 1  |  Page : 79-83

Motor cognitive processing speed estimation among the primary schoolchildren by deriving prediction formula: A cross-sectional study


1 Department of Pediatric Physiotherapy, Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Mullana, Haryana, India
2 Department of Musculoskeletal Physiotherapy, Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Mullana, Haryana, India
3 Department of Graduate Studies, School of Physical and Occupational Therapy, McGill University, Montreal, Quebec, Canada
4 Department of Physiotherapy, Punjabi University, Patiala, Punjab, India

Correspondence Address:
Asir John Samuel
Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Maharishi Markandeshwar University, Mullana - 133 207, Haryana
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0976-3147.193544

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Objectives: Motor cognitive processing speed (MCPS) is often reported in terms of reaction time. In spite of being a significant indicator of function, behavior, and performance, MCPS is rarely used in clinics and schools to identify kids with slowed motor cognitive processing. The reason behind this is the lack of availability of convenient formula to estimate MCPS. Thereby, the aim of this study is to estimate the MCPS in the primary schoolchildren. Materials and Methods: Two hundred and four primary schoolchildren, aged 6–12 years, were recruited by the cluster sampling method for this cross-sectional study. MCPS was estimated by the ruler drop method (RDM). By this method, a metallic stainless steel ruler was suspended vertically such that 5 cm graduation of the lower was aligned between the web space of the child's hand, and the child was asked to catch the moving ruler as quickly as possible, once released from the examiner's hand. Distance the ruler traveled was recorded and converted into time, which is the MCPS. Multiple regression analysis of variables was performed to determine the influence of independent variables on MCPS. Results: Mean MCPS of the entire sample of 204 primary schoolchildren is 230.01 ms ± 26.5 standard deviation (95% confidence interval; 226.4–233.7 ms) that ranged from 162.9 to 321.6 ms. By stepwise regression analysis, we derived the regression equation, MCPS (ms) = 279.625–5.495 × age, with 41.3% (R = 0.413) predictability and 17.1% (R2 = 0.171 and adjusted R2 = 0.166) variability. Conclusion: MCPS prediction formula through RDM in the primary schoolchildren has been established.


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