A Comparison of Estimation Models of Physical Fitness Age for Elderly People using the Japan Fitness Test

Nishijima, Takeshi; Takahashi, Shinji; Matsumoto, Tatsuro; Nakano, Takahiro; Suzuki, Koya; Yamada, Hiroshi; Ohtsuka, Keisuke; Matsuda, Mitsuo; Kuno, Shinya

doi:10.5432/ijshs.4.591

Cited by 5 publications

(3 citation statements)

References 16 publications

(20 reference statements)

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“…Apart from cognitive age-gaps, another ability-based age-gap metric can be estimated using physical tness scores. There have been a handful of studies [5][6][7][8][9] that attempted to estimate physical ages of individual participants using physical tness test scores. Among them, only two [6,8] went on to compute and study physical age-gaps in relation to other outcomes.…”

Section: Introductionmentioning

confidence: 99%

Cognitive and physical age gaps in relation to mild cognitive impairment and behavioral phenotypes

Mahendran

2023

GeroScience

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We can study how fast our biological aging clocks tick by calculating the difference (i.e., age-gaps) between machine learning estimations of biological age and chronological age. While this approach has been increasingly used to study various aspects of aging, few had applied this approach to study cognitive and physical age-gaps; not much is known about the behavioral and neurocognitive factors associated with these age-gaps. In the present study, we examined these age-gaps in relation to behavioral phenotypes and mild cognitive impairment (MCI) among community-dwelling older adults.Participants (N=822, Age mean =67.6) were partitioned into equally-sized training and testing samples.Cognitive and physical age-prediction models were tted using nine cognitive and eight physical tness test scores, respectively, within the training samples, and subsequently used to estimate cognitive and physical age-gaps for each subject in the testing sample. These age-gaps were then compared among those with and without MCI, and correlated with 17 behavioral phenotypes in the domains of lifestyle, well-being, and attitudes. Across 5,000 random train-test split iterations, we showed that older cognitive age-gaps were signi cantly associated with MCI (versus cognitively normal) and worse outcomes across several well-being and attitude-related measures. Both age-gaps were also signi cantly correlated with each other. These results suggest accelerated cognitive and physical aging were linked to worse wellbeing and more negative attitudes about the self and others, and reinforce the link between cognitive and physical aging. Importantly, we have also validated the use of cognitive age-gaps in the diagnosis of MCI.

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Section: Introductionmentioning

confidence: 99%

Cognitive and physical age gaps in relation to mild cognitive impairment and behavioral phenotypes

Mahendran

2023

GeroScience

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“…The result of the FRT was the maximum distance that an individual could reach forward while standing and [6,7], and that of the hurdle walk test was the time the individual took to complete the hurdle walk test [8,9].…”

Section: Methodsmentioning

confidence: 99%

“…As standard indices, we used the results of the functional reach test (FRT) [6,7], hurdle walk test [8,9], the Tokyo Metropolitan Institute of Gerontology (TMIG) index of competence [10], fall risk assessment [11], fall experience within 1 year (fall experience), and the answer to the question: ''Are you careless?'' (carelessness).…”

Section: Methodsmentioning

confidence: 99%

Development of a new instrument for evaluating leg motions using acceleration sensors (II)

Itoh

Kumagai²,

Morioka

et al. 2011

Environ Health Prev Med

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Objective The purpose of this study was to investigate the characteristics of the average acceleration of elderly people during walking. Method The subject cohort comprised nine men and 21 women aged C63 years. Subjects walked a 10-m straight course (walk test) which required stepping over six obstacles (hurdle walk test). The average acceleration was calculated from the accelerograms. Functional reach test scores and the Tokyo Metropolitan Institute of Gerontology (TMIG) index of competence, fall risk assessment, fall experience within the last year, and carelessness were used as standard indices to estimate the dynamic postural movement and fall risk. Results The average acceleration during the walk test was not significantly correlated with the standard indices. The average accelerations at the lumbar and knee positions clustered with fall experience and carelessness, while those at the ankle and toe positions clustered with the hurdle walk test, TMIG index of competence, and fall risk assessment. Between the high-and low-risk groups classified by the conventional indices, there was a significant difference in the average acceleration at some measurement positions. The receiver operating characteristic analysis showed the possibility to discriminate the high-risk group according to the standard indices with average acceleration. Conclusions The average acceleration during walking may be a composite index that encompasses standard indices and discriminate the high-risk group. As such, it may be a useful tool to estimate the dynamic postural movement and fall risk at all measurement positions.

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Cognitive and physical age-gaps in relation to mild cognitive impairment and behavioral phenotypes

Mahendran

2023

Preprint

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Background: We can study how fast our biological aging clocks tick by calculating the difference (i.e., age-gaps) between machine learning estimations of biological age and chronological age. While this approach has been increasingly used to study various aspects of aging, few had applied this approach to study cognitive and physical age-gaps; not much is known about the behavioral and neurocognitive factors associated with these age-gaps. In the present study, we examined these age-gaps in relation to behavioral phenotypes and mild cognitive impairment (MCI) among community-dwelling older adults. Methods: Participants (N=822, Agemean=67.6) were partitioned into equally-sized training and testing samples. Cognitive and physical age-prediction models were fitted using nine cognitive and eight physical fitness test scores, respectively, within the training samples, and subsequently used to estimate cognitive and physical age-gaps for each subject in the testing sample. These age-gaps were then compared among those with and without MCI, and correlated with 17 behavioral phenotypes in the domains of lifestyle, well-being, and attitudes. Results: Across 5,000 random train-test split iterations, we showed that older cognitive and physical age-gaps were significantly associated with MCI (versus cognitively normal) and worse outcomes across several well-being and attitude-related measures. Both age-gaps were also significantly correlated with each other. Conclusions: These results suggest accelerated cognitive and physical aging were linked to worse well-being and more negative attitudes about the self and others, and reinforce the link between cognitive and physical aging. Importantly, we have also validated the use of cognitive age-gaps in the diagnosis of MCI.

show abstract

A Comparison of Estimation Models of Physical Fitness Age for Elderly People using the Japan Fitness Test

Cited by 5 publications

References 16 publications

Cognitive and physical age gaps in relation to mild cognitive impairment and behavioral phenotypes

Cognitive and physical age gaps in relation to mild cognitive impairment and behavioral phenotypes

Development of a new instrument for evaluating leg motions using acceleration sensors (II)

Cognitive and physical age-gaps in relation to mild cognitive impairment and behavioral phenotypes

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