Context: We employed two classification methods that characterize psycho-somatotype categorization to understand motor and cognitive performance. The Trunk Index produces three somatotypes/body type categories: ectomorphs, mesomorphs, and endomorphs, and Prakriti classifications categorizes people into three categories: Vata, Pitta, and Kapha. Comparing these two categorization methods offers insights into anthropometric measures that combine psychological and physical characteristics to account for motor and cognitive behavior.
Aims: The present study examined variations in cognitive and motor performances using the two typologies - and somato body types using cross-sectional study design.
Subjects And Methods: The study employed fifty-eight healthy young adults, classified into , , ) and , , morph body types, to examine their cognitive performance (reaction time [RT] and accuracy), and motor performance (posture stability and posture accuracy) in standing yoga postures.
Statistical Analysis Used: Analysis of covariance was performed to compare the cognitive and postural performance across the three somato and types after adjusting for age and gender as covariates. analysis of Bonferroni was performed with the consideration of Levene's test. Partial correlations were employed to investigate the correlation between postural stability and cognitive performance measures for each of the prakriti- and somato-body types as well as between the prakriti typology (scores) and trunk index values (adjusting the effects of age and gender as control variables). A < 0.05 was selected at the statistical significance level. SPSS 26.0 version was used for the analysis.
Results: Cognitive performance was observed to vary in terms of RT across somato- and prakriti body types ( < 0.05). Postural stability and cognitive performance are positively connected only for ectomorph body types ( < 0.05). Variations in motor performance were not significant. Barring ectomorph type, no other somato- and prakriti body types showed significant relationships between postural stability and cognitive performance. Likewise, the association between the features used for prakriti classification, and the trunk index scores showed marginal significance, only for a small subset of physical features of prakriti assessment ( = 0.055) (P1).
Conclusions: Comparing classifications that use psychophysical attributes might offer insights into understanding variations in measures of motor and cognitive performance in a sample of healthy individuals.
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http://dx.doi.org/10.4103/ijoy.ijoy_12_22 | DOI Listing |
BMC Psychiatry
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School of Mental Health, Bengbu Medical University, Bengbu, Anhui, 233030, China.
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January 2025
School of Allied Health Science and Practice, Engineering Math and Science Building, University of Adelaide, North Terrace, Level 4, Adelaide, South Australia, 5005, Australia.
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BMC Psychiatry
January 2025
Division of Nursing, Midwifery and Social Work, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL, UK.
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View Article and Find Full Text PDFMem Cognit
January 2025
Department of Human Sciences, Institute of Psychology, General Psychology, University of the Bundeswehr Munich, Werner-Heisenberg-Weg 39, 85577, Neubiberg, Germany.
This study explores multi-tasking by examining the effects of transitioning from dual-task to triple-task scenarios. Our research extends beyond conventional dual-task paradigms to investigate the impact of triple-task performance on two participant groups: those unprepared in single, dual, or triple tasks (N = 14) and those previously prepared in single and dual tasks (N = 13). The study consisted of a preparation phase with nine sessions and an assessment phase with eight sessions.
View Article and Find Full Text PDFBrain Imaging Behav
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Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang, Wuhan, Hubei, 430071, China.
This study investigates post-stroke cognitive impairment (PSCI) by utilizing spectral dynamic causal modeling (spDCM) to examine changes in effective connectivity (EC) within the default mode, executive control, dorsal attention, and salience networks. Forty-one PSCI patients and 41 demographically matched healthy controls underwent 3D-T1WI and resting-state functional magnetic resonance imaging on a 3.0T MRI.
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