A review of physical and cognitive interventions in aging

Bamidis, Panagiotis D.; Vivas, Ana B.; Styliadis, Charis; Frantzidιs, Christos A.; Klados, Manousos A.; Schlee, Winfried; Σιούντας, Αναστάσιος; Papageorgiou, Sokratis G.

doi:10.1016/j.neubiorev.2014.03.019

Cited by 292 publications

(262 citation statements)

References 174 publications

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“…On the other hand, the subjects in the Mild AD dataset are not age matched. Age differences have been related with changes in complex brain functional networks and cognitive decline [42]. Furthermore, studies comparing young and old AD patients have also described changes in the brain due to age [43].…”

Section: Discussionmentioning

confidence: 99%

A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

Gallego-Jutglà¹,

Solé-Casals²,

Vialatte³

et al. 2015

J. Neural Eng.

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Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer's disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy.These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5).Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

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Section: Discussionmentioning

confidence: 99%

A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

Gallego-Jutglà¹,

Solé-Casals²,

Vialatte³

et al. 2015

J. Neural Eng.

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“…Further findings show that regular physical activity, partly directly, partly mediated through the promotion of basic cognitive processes, has a favorable effect on the development of cognitive abilities (Bamidis et al, 2014). Results from training research suggest that efforts to improve cognitive performance are most likely to be successful if one does not rely on purely cognitive training, the effects of which are often limited to the specific content of the training, but on a combination of cognitive training and physical training (Rahe et al, 2015).…”

Section: Problem Statementmentioning

confidence: 98%

Improving Working Abilities, Achievement Motivation, And Active Ageing In Older Workers

Schmitt¹,

Hinner²,

Kruse³

2017

European Proceedings of Social and Behavioural Sciences

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Problem Statement: As a consequence of an aging labor force, companies' competitiveness depends more and more from their ability to recognize, support, and effectively use older employees' potentials for innovation and creativity. Research Questions: Do modular vocational trainings substantially contribute to enhancement and maintenance of working abilities, achievement motivation and active ageing in older workers by improving mental and physical fitness, and health behavior? Purpose of the Study: To put the practicability, effectiveness and sustainability of multidimensional modular intervention measures integrating cognitive training, physical training, and health education which were attuned to specific needs of diverse occupational contexts to an empirical test and further contribute to foundational research in the field of intervention programs for an ageing workforce. Research Methods: Longitudinal analyses of cognitive, physical, and health behavior-related intervention effects and sustainability in samples of older workers (45 years and older) who participated in vocational trainings developed and implemented in co-operation with Robert Bosch GmbH and Robert Bosch Foundation, Deutsche Bahn, Municipality of Heidelberg, and Chemical Industry. Evaluation of intervention effects against changes observed in a control group. Findings: All four versions of the program significantly (and substantially) improved indicators of cognitive and physical fitness, including information processing speed, concentrativeness, inhibition, working memory capacity, physical strength, endurance, and coordination. Individual counselling sessions suggest increases in health behavior, control beliefs, and overall optimism. Effects slightly decreased following end of vocational training but nevertheless provide evidence for sustainability of the program. Conclusions: Modular multidisciplinary approach and focus on the development of more general competences significantly contribute to the innovative character of the program. A particularly innovative component of the didactic concept is the private continuation of training units in everyday contexts (on the basis of individual advices, feedback, and materials offered before) which again is followed by training units in the company.

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“…With the constant development of more intelligent sensors and devices, as well as data stemming from applications such as interventions for elderly people [17], the data produced by the Internet of Things (IoT) increases in volume and variety.…”

Section: Introductionmentioning

confidence: 99%

IoT of active and healthy ageing: cases from indoor location analytics in the wild

et al. 2016

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ACKNOWLEDGMENTThis work was supported in part by the UNCAP Horizon 2020 project (grant number 643555), as well as, the business exploitation scheme of the ICT-PSP funded project LLM, namely, LLM Care which is a self-funded initiative at the Aristotle University of Thessaloniki (www.llmcare.gr). 3 AbstractRecently much research has been conducted on early detection of cognitive and physical status deterioration in elderly adults. Primarily the focus is on gait analysis methodologies exploiting average speed, however this presents an issue when used for context aware applications. Additionally data capture tends to be in short bursts over a long period, allowing for localized temporal factors, such as short term injury, to potentially skew measurements. As such this work collects gait and trajectory IoT data from elderly adults in senior homes ("in the wild") over a sustained period of time (1 year). Density based clustering algorithms are then applied to the data to provide long-term insights into how the high density regions change over time. The data is collected, analyzed and made available by the indoor analytics client utilizing available processing resources and delivers the analytics outcome even when it is hosted in hardware with constrained resources. Promising results are obtained from the long-term study, suggesting that this form of evaluation has strong potential in the analysis of cognitive and physical status deterioration.

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A review of physical and cognitive interventions in aging

Cited by 292 publications

References 174 publications

A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

Improving Working Abilities, Achievement Motivation, And Active Ageing In Older Workers

IoT of active and healthy ageing: cases from indoor location analytics in the wild

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