Cognitive functioning is more closely related to real-life mobility than to laboratory-based mobility parameters

Giannouli, Eleftheria; Bock, Otmar; Zijlstra, Wiebren

doi:10.1007/s10433-017-0434-3

Cited by 34 publications

(37 citation statements)

References 45 publications

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“…These gait based temporal elements are likely to consist of the same physiological construct, indeed the correlation between these two iTUG variables was 0.85 suggesting the measures were highly related. Cognition and total TUG time has been previously investigated, demonstrating that those with lower levels of cognition, perform the TUG more slowly in older people with Parkinson's disease or those with mild-moderate cognitive impairment 28,36,44,45 . The findings of the current study suggest that it is only the duration of walking phases, not turning or sit to stand, that are correlated with cognitive function.…”

Section: Discussionmentioning

confidence: 99%

“…Correlations were observed in individuals with mild cognitive impairment and the performance of the sub-phases of the iTUG 25,28 . These studies suggested cognitive function correlated with sit-tostand transition, and turning sub-phases along with some metrics of gait 25,28 . FoF, which has been linked to risk of falling and has been shown to correlate to subphases of the iTUG [29][30][31] , however these studies have only investigated individuals with Parkinson's disease.…”

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confidence: 93%

“…Previous research has identified links between iTUG (including time to completion) and various aspects linked to falls including cognition, fear of falling (FoF) and QoL. Correlations were observed in individuals with mild cognitive impairment and the performance of the sub-phases of the iTUG 25,28 . These studies suggested cognitive function correlated with sit-tostand transition, and turning sub-phases along with some metrics of gait 25,28 .…”

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confidence: 99%

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Association between the instrumented timed up and go test and cognitive function, fear of falling and quality of life in community dwelling people with dementia

Williams

Nyman

2018

JFSF

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Dementia is a highly prevalent neurodegenerative disorder characterised by changes in behaviour and cognition 1. The prevalence and incidence of dementia has been reported at approximately 15% and 3% respectively 2 and it has been reported to affect approximately 47 million people worldwide 1. People with dementia (PWD) show symptoms ranging from cognitive, social and motor impairment, all of which can significant affect quality of life (QoL) 3,4. PWD have been shown to have greater falls risk compared to those either with mild cognitive impairment or intact cognition 5. Indeed this appears to remain true for PWD within care homes 6 and for those within their own home, where the risk is double 5,7. In addition to a greater risk of falls, the risk of hip fracture has been found to be almost 5-fold that of matched-controls 5 and PWD are 58% more likely to be admitted to hospital with a hip fracture than those without dementia, where mortality following hip fracture can be as high as 30% 8. Therefore PWD are at great risk of injurious falling.

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Section: Discussionmentioning

confidence: 99%

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confidence: 93%

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confidence: 99%

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Association between the instrumented timed up and go test and cognitive function, fear of falling and quality of life in community dwelling people with dementia

Williams

Nyman

2018

JFSF

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“…Another study in 279 functionally and cognitively independent older people in retirement communities found an average wear time of 13.6 h/day (SD 1.3) [ 37 ]. The ‘Mobility Study’, which recruited older people to wear GPS trackers for 1 week had to exclude 15 of 86 participants due to missing sensor data [ 42 ]. Lastly, Liddle et al [ 38 ] reported problems with GPS measurement accuracy within the home environment.…”

Section: Novel Methods To Assess Lsmmentioning

confidence: 99%

Assessing life-space mobility for a more holistic view on wellbeing in geriatric research and clinical practice

Taylor¹,

Buchan

Veer

2018

Aging Clin Exp Res

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Life-space mobility (LSM) is a holistic measure of resilience to physical decline and social isolation in later life. To promote its use as an outcome in geriatric studies and in clinical practice, this review paper explains the concept of LSM; outlines available questionnaires for LSM assessment, provides an overview of associations between LSM and other outcomes, and discusses emerging methods to measure LSM using wearable sensors. Based on performed activity around a central geographical anchor, LSM aims to quantify the observed contraction of daily activities associated with ageing. Several questionnaires are available to assess LSM in different contexts: the University of Alabama Life-Space Assessment and the Life-Space Questionnaire (community settings), the Nursing Home Life-Space Diameter (nursing home settings) and Life Space at Home (for house-bound populations). Some studies using GPS trackers to calculate life-space parameters reported promising results. Although these techniques reduce data collection burden, battery life and older people's willingness to wear a tracker require further improvement before they can be used more widely. Regardless of the assessment method used, LSM was associated with measures of functional and cognitive abilities, nursing home admission and mortality. The current availability of instruments, the ongoing development of less burdensome data collection techniques, and evidence of construct validity support a case for promoting integration of LSM assessments into geriatric research studies and clinical practice. Ultimately, this will provide a more holistic view on older people's health and wellbeing.

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“…In light of current demographic changes, it is important to study factors contributing to an active, independent and healthy lifestyle up to and into old age. An increasing number of studies have investigated the manifold relations between mobility and physical as well as psychological health, especially in older adults (Giannouli, Bock, & Zijlstra, 2018;Kaspar, Oswald, Wahl, Voss, & Wettstein, 2015;Rantakokko et al, 2015). However, most studies that found significant relations between mobility and health outcomes focus on the physical dimension of mobility (how much activity takes place) and less on its spatial dimension (where activity takes place).…”

Section: Introductionmentioning

confidence: 99%

Towards a Framework for Assessing Daily Mobility Using GPS Data

Fillekes¹,

Giannouli²,

Zijlstra³

et al. 2018

giforum

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In health and aging studies that include GPS assessments, mobility is often represented by a single indicator such as the number of activity locations, the size of the activity space, or the total distance covered. We argue, however, that mobility is a multi-dimensional construct. In this paper, we first provide a framework to categorize and systematically organize daily-life mobility indicators. We then illustrate this framework by computing several mobility indicators based on GPS data recorded from healthy older adults. We apply a correlational approach to the computed mobility indicators as a first step towards discovering underlying dimensions of daily mobility. We found a trend towards higher correlations between mobility indicators that represent more similar properties of mobility. The framework can be used by health researchers to inform the choice of appropriate mobility indicators in the design of empirical studies.

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Cognitive functioning is more closely related to real-life mobility than to laboratory-based mobility parameters

Cited by 34 publications

References 45 publications

Association between the instrumented timed up and go test and cognitive function, fear of falling and quality of life in community dwelling people with dementia

Association between the instrumented timed up and go test and cognitive function, fear of falling and quality of life in community dwelling people with dementia

Assessing life-space mobility for a more holistic view on wellbeing in geriatric research and clinical practice

Towards a Framework for Assessing Daily Mobility Using GPS Data

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