EP 117. 1000BRAINS study: Motor performance in the elderly - Not all parameters decline with age

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• 作者：L. Kä ; mereit^a ; S. Lux^a ; M.E. Kirlangic^a ; C. Sproß ; ^a ; S. Caspers^a ; S.B. Eickhoff^a ; S. Moebus^b ; N. Pundt^b ; K.-H. Jö ; ckel^b ; R. Erbel^b ; K. Zilles^a ; ^c ; ^d ; K. Amunts^a ; ^d ; ^e ; M. Minnerop^a ; ^f ;
• 刊名：Clinical Neurophysiology
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：127
• 期：9
• 页码：e290-e291
• 全文大小：48 K

文摘

It is known that motion patterns change with age ( and ). But is all motor performance in the elderly age-related and if not – which parameters contribute to the greater variance in elderly? The 1000BRAINS study in collaboration with the German Heinz Nixdorf Recall Study investigates structural and functional variability of the human brain during normal aging (Caspers et al., 2014), enabling the analysis of various aspects of motor performance and its interaction with lifestyle and cognition in the identical large population. The aims of this study are to (1) measure objective parameters of motor performance, (2) analyze age dependency, and (3) investigate interactions with demographic and life style data.

In the study 300 subjects (131 women, mean age 65.9 ±$\pm$ 6.1 years, range 56.4–85.4 years) were included. Within the motor test battery (MTB, see Caspers et al., 2014) upper limb functions were assessed by different finger tapping tasks and those of lower limb by functional measures of balance and mobility. Demographic and life style data were obtained by questionnaires. Exclusion criteria were more than one missing motor score, a history of central nervous system damage, musculoskeletal disorders or depression. Statistical analysis comprised a factor analysis on the MTB, based on z-scores, and correlation analysis (p <$<$ 0.05; Bonferroni adjusted for number of factors).

Factor analysis results in four main factors, extracted by Eigenvalues >$>$1, explaining 55.2% of the variance within the data (KMO criterion = 0.614): maximum hand speed (MHS), stability of stance (SOS), gross motor skills (GMS) and self-paced internal hand speed (IHS). Lower MHS factor values were associated with higher age, lower body height and lower educational level, more depressed mood and lower alcohol intake. Lower MHS and SOS factor values were related to lower falls-related self-efficacy. Higher education, lower age and body weight, better vibration sense, higher falls-related self-efficiency, higher physical activity and preserved ability to perform pedicure were associated with higher GMS factor loadings. IHS factor values showed no significant correlations.

IHS is reflecting psychomotor speed and seems to be a stable property, varying only in the early and very late adulthood (Turgeon and Wing, 2012). Higher MHS and GMS are associated with higher education, but not IHS and SOS. The relevance of education, a marker of reserve, is well known for cognition, but has only recently been addressed for motor performance (walking speed, Elbaz et al., 2013). Walking speed is also enclosed in GMS. The reserve hypothesis fits well to the association between education and MHS/ GMS. However, motor domains seem to interact differently with age and markers of reserve. The relation to brain structure and genetic data will be investigated in future studies.