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European Journal of Ageing logoLink to European Journal of Ageing
. 2017 Jun 20;15(1):57–65. doi: 10.1007/s10433-017-0434-3

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

Eleftheria Giannouli 1,✉, Otmar Bock 2, Wiebren Zijlstra 1
PMCID: PMC5840094  PMID: 29531515

Abstract

Increasing evidence indicates that mobility depends on cognitive resources, but the exact relationships between various cognitive functions and different mobility parameters still need to be investigated. This study examines the hypothesis that cognitive functioning is more closely related to real-life mobility performance than to mobility capacity as measured with standardized laboratory tests. The final sample used for analysis consisted of 66 older adults (72.3 ± 5.6 years). Cognition was assessed by measures of planning (HOTAP test), spatial working memory (Grid-Span test) and visuospatial attention (Attention Window test). Mobility capacity was assessed by an instrumented version of the Timed Up-and-Go test (iTUG). Mobility performance was assessed with smartphones which collected accelerometer and GPS data over one week to determine the spatial extent and temporal duration of real-life activities. Data analyses involved an exploratory factor analysis and correlation analyses. Mobility measures were reduced to four orthogonal factors: the factor ‘real-life mobility’ correlated significantly with most cognitive measures (between r = .229 and r = .396); factors representing ‘sit-to-stand transition’ and ‘turn’ correlated with fewer cognitive measures (between r = .271 and r = .315 and between r = .210 and r = .316, respectively), and the factor representing straight gait correlated with only one cognitive measure (r = .237). Among the cognitive functions tested, visuospatial attention was associated with most mobility measures, executive functions with fewer and spatial working memory with only one mobility measure. Capacity and real-life performance represent different aspects of mobility. Real-life mobility is more closely associated with cognition than mobility capacity, and in our data this association is most pronounced for visuospatial attention. The close link between real-life mobility and visuospatial attention should be considered by interventions targeting mobility in old age.

Keywords: Out-of-home mobility, Ageing, Life-space, Instrumented Timed Up-and-Go test, Capacity, Performance

Introduction

Mobility is a complex and multifaceted construct (Webber 2010; Wettstein et al. 2014b). It includes the ability to walk and move around, change or maintain body position, carry, move and handle objects but also more resource-intensive skills like out-of-home mobility on foot and moving around using transportation (World Health Organization 2005). Besides neuromechanical functions required for successful locomotion, mastering activities of daily living also depends on cognitive functions. From the control of routine gait, to planning and execution of complex movement sequences and interactions with traffic and ticket vending machines, cognitive abilities are crucial for all aspects of mobility. Indeed, increasing evidence indicates that, due to age-related changes in sensory motor and neuromechanical functions, mobility in older persons depends increasingly on cognitive resources. This has been documented in cross-sectional (Ble et al. 2005; Carlson et al. 1999) as well as in longitudinal studies (Buchman 2011; Soumaré 2009). Especially with regard to real-life mobility, cognitive status has been found to be more closely linked with out-of-home behavior than motivational resources (Wahl et al. 2013) and also to moderate the relationship between out-of-home behavior and well-being (Wettstein et al. 2014a).

Most studies investigating the relationship between cognition and mobility have measured overall cognitive functioning using screening tools for cognitive impairment, such as the MMSE (Crowe et al. 2010) and/or single measures of mobility like gait speed (Ble et al. 2005; Holtzer et al. 2006; Inzitari et al. 2007). However, regarding cognitive performance, it is important to use selected cognitive tests to look into specific cognitive domains which are necessary for mobility. Differentiation is also needed when it comes to measuring mobility. Recent studies have shown that mobility capacity (the highest possible level of functioning of an individual in a given domain at a certain moment) and mobility performance (what individuals do in their own current environment) (as defined in the International Classification of Functioning by the World Health Organization 2005) are two different aspects of mobility (Giannouli et al. 2016; van Lummel et al. 2015) which are therefore expected to be associated differently with specific cognitive domains.

Previous research has demonstrated a robust relationship between gait and executive functions (Martin et al. 2013; Yogev‐Seligmann et al. 2008). However, gait has been assessed in standardized laboratory settings, which differ from real life in multiple respects: In everyday situations, persons often move in a complex and visually cluttered environment, they execute a non-repetitive sequence of activities (e.g., bus riding, then shopping, then visiting a café), often engage in multitasking (e.g., looking for a product on the shelves while avoiding collisions with other shoppers), respond to unexpected events (e.g., keeping balance when the bus driver hits the brake) and they pursue those activities not because they are instructed to do so, but in order to achieve some desirable goal. In other words, standardized laboratory settings typically lack ecological validity and findings may therefore not be fully transferrable to real life (Chaytor and Schmitter-Edgecombe 2003).

Most of the existing research examining the association between mobility and cognition has focused on either one cognitive domain or on one aspect of mobility at a time and mostly only on laboratory measures. Therefore, this study explores the relationships between different aspects of cognitive functioning with measures of the two fundamentally different aspects of mobility, i.e., capacity and performance, as defined by the World Health Organization. Our study specifically aims to analyze the strength of the associations of different cognitive domains with mobility capacity and real-life mobility performance.

We hypothesize that cognitive functioning is more related to real-life mobility performance than to mobility capacity as measured with standardized laboratory tests. The study’s results will add to our knowledge of the associations between cognition and mobility and thus may contribute to the institution of preventive measures to delay the transition to dependency and promote higher quality of life in old age.

Methods

This study was part of the ‘Mobility study’ of the German Sport University Cologne which aimed to analyze determinants of daily life mobility in older adults. All participants completed a laboratory-based test battery including several physical, cognitive, social and psychometric tests as well as an ambulatory mobility assessment. The local ethics committee approved the study protocol, which is in accordance with the declaration of Helsinki.

Participants

Community-dwelling participants were recruited by following strategies: (1) presentations of the project at local senior citizen gatherings, (2) individual invitation letters to persons who expressed interest in participating in studies of the Institute of Movement and Sport Gerontology in the past, and (3) handing out information brochures about the study and individual approach in settings such as local doctor’s offices, pharmacies, churches, and senior sport groups. In order to increase the performance spectrum and therefore also representativeness of our sample, we also recruited participants from assisted living facilities by presenting our project during special events on-site. In total, 86 persons meeting the criteria for participation in the study were initially recruited. Inclusion criteria were: age older than 65 years, no serious neurological diseases which could interfere with functional mobility (e.g., Parkinson’s disease, acute/severe stroke with high motor/sensory deficits and multiple sclerosis.) no cardiovascular diseases, no cognitive impairment, ability to stand up from a chair independently, a physician’s written statement of non-objection for this person to participate confirming the absence of these exclusion criteria and informed consent to the study design.

Mobility measures

Mobility capacity was assessed in the laboratory using the extended, instrumented version (Salarian et al. 2010) of the Timed Up-and-Go test (Podsiadlo and Richardson 1991). Besides the parameter ‘total duration’ for the completion of the test (iTUG), the execution of each of the tests subcomponents was quantified using the data recorded by the six inertial measurement units (Opal, APDM Inc., Portland, OR, USA) attached to the participants’ bodies by calculating following variables (see Salarian et al. 2007; Salarian and Horak 2010; Salarian et al. 2004): sit-to-stand (StS) transition: peak velocity (degrees/s), range of motion of the trunk (ROM-Trunk) (degrees); Gait: stride length (m), stride velocity (m/s), cadence (steps/min); Turn: duration (s), peak velocity (degrees/s), number of steps (NOS); Turn-to-Sit (TtS) transition: peak velocity (degrees/s). Each participant completed three trials. The first trial was considered as practice, and the mean values from the second and third trial were used for analysis.

Mobility performance in real life was assessed over one week based on measuring motion–sensor and GPS data by means of smartphone technology. The detailed ambulatory mobility assessment strategy and data handling for the calculation of the used variables have been described elsewhere (Giannouli et al. 2016). In brief, from the motion–sensor data, the variable ‘Active- & Gait Time’ (AGT) was used, which is the sum of the durations (in hours) of active and gait intervals—as identified based on activity counts and metabolic equivalents (METs) (Sasaki et al. 2011) and a step detector (Ryu et al. 2013). From the GPS data following variables were used: ‘life-space’ (km2), the area within which the participants moved during the registration period; ‘Distance’ (km), the total distance travelled during the registration period; and ‘maximum action range’ (AR-max) (km), the largest straight-line distance away from the home location during the registration period.

Cognitive measures

The cognitive assessment included executive functioning measures which are necessary for both mobility capacity and mobility performance with special focus on visuospatial cognitive tasks, as they have been found to interfere with locomotor control to a greater extent than non-spatial tasks (Beurskens and Bock 2012; Cohen et al. 2016; Menant et al. 2014). More specifically, we measured planning, spatial working memory and visuospatial attention. To measure planning ability, two subtasks of the HOTAP picture-sorting test (Menzel-Begemann 2009) were used. For the first part of the test (HOTAP.A), which is based on the picture arrangement task of the Wechsler Adult Intelligence Scale, participants had to chronologically arrange pictures of everyday situations (e.g., shopping in a supermarket or refuel a car at a gas station) that were presented in a wrong order. For the second part of the test (HOTAP.B), participants had to read a story and then arrange the pictures of situations presented in front of them according to the story. In both parts, every correctly rearranged picture granted one point. The outcome variables were the sum of the points divided by the time they needed to arrange the cards.

Participants’ spatial working memory was assessed via a simple and a complex Grid-Span task (Hale et al. 1996). In both tasks, participants had to remember the locations of a series of stimuli (X signs) that were presented sequentially in the nine cells of a grid, which remained on the screen throughout the series. The difference between the simple and the complex span task concerns stimuli reproduction. In the simple task (GSS), participants had to respond by marking those cells where stimuli had been presented. In the complex span task (GSC), they had to mark cells one position to the right of the one they had been presented. Outcome variable for both test versions was the maximum number of stimuli the participants could reproduce correctly (participants had three trials for every set of stimuli).

Visuospatial attention was assessed with the Attention Window test (Hüttermann et al. 2013). Participants fixated in the center of a screen while two sets of four different forms (light or dark gray triangles, squares, circles) were presented equidistantly from the fixation point along one of four axes (horizontal, vertical, diagonal) in different distances for 600 ms. Participants were then prompted to report the number of the light-gray triangles, first in one and then in the other set of forms. Responses were not measured in terms of speed. Outcome variables were the percentage of correct answers along all axes (AWT) as well as along the horizontal axis (AWH). Answers were considered correct only if the reported number was correct for both sets.

Statistical analyses

Data analysis consisted of four main phases: a multiple correlation approach to look into the overall relationships between all mobility and cognition parameters; a factor analytic approach for the mobility variables; a multiple correlation analysis between the cognitive variables and the resulting mobility factors and a multiple regression analysis. The principal component analysis (PCA) was conducted in order to establish underlying dimensions between different mobility parameters. Varimax rotation was used to derive orthogonal factor scores, applying the Kaiser’s eigenvalue-greater-than-one rule.

The scores of the resulting factors (which were computed using the regression method) as well as the cognitive measures were then used for another multiple correlation analysis to identify which mobility parameters best relate to cognition. Finally, a series of stepwise multiple regression analyses in which the four factors resulting from the PCA served as dependent variables and the five cognitive measures (HOTAP.A, HOTAP.B, AWT, AWH, GSS, GSC) as predictors. For all analyses, the significance level was set at 0.05 except for the Kolmogorov–Smirnov test which was used to assess the normality of the datasets and for which the significance level was set at 0.10.

Results

Descriptive statistics

From the initial 86 participants, two were excluded from the analysis for not completing the ambulatory mobility assessment, 15 for not providing a complete GPS or motion–sensor dataset and three because of missing iTUG variables. The final sample, therefore, consisted of 66 persons. In these subjects, the mean real-life mobility registration period was 7.03 ± 1.64 days. Table 1 provides a description of the sample’s demographics and also summarizes their cognitive and mobility measures.

Table 1.

Participants’ descriptive data, cognitive measures and mobility measures

Mean SD Min Max
Descriptive data
Age (total sample) (years) 72.3 5.6 65 88
Men (n = 24) 71.9 4.9 65 83
Women (n = 42) 72.6 6.0 64 88
BMI (kg/m2) 23.7 3.2 16.7 33.1
Cognitive measures
HOTAP.A 10.2 4.3 4.1 24.5
HOTAP.B 7.0 2.6 2.5 14.3
Attention Window (total) 70.4 23.5 5.7 95.0
Attention Window (horizontal) 69.7 22.6 3.4 96.6
Grid-Span (simple) 4.6 1.1 3.0 8.0
Grid-Span (complex) 3.8 0.9 3.0 6.0
Real-life mobility measures
AGT (h) 4.3 0.9 1.8 6.1
Distance (km) 116.7 64.7 13.1 289.6
Life-space (km2) 59.7 62.1 0.2 300.0
AR-max (km) 10.1 4.4 0.5 15.0
iTUG measures
iTUG (s) 16.16 2.65 11.49 30.21
Gait measures
Stride length (m) 1.41 0.12 1.06 1.62
Stride velocity (m/s) 1.39 0.16 0.82 1.77
Cadence (steps/m) 118.55 11.19 88.43 151.74
Turn measures
Duration (s) 2.24 0.70 1.23 5.04
NOS (n) 4.97 1.36 2.00 9.50
Peak velocity (degrees/s) 171.7 38.5 91.0 296.9
Sit-to-stand transition measures
ROM-Trunk (degrees) 35.4 7.2 20.3 53.2
Peak velocity (degrees/s) 117.8 46.5 68.1 349.2
Turn-to-Sit transition measures
Peak velocity (degrees/s) 180.7 42.7 78.1 273.9
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Mean average values, SD standard deviation, Min minimum values, Max maximum values, BMI body mass index, AGT active and gait time, AR-max maximum action range, iTUG instrumented Timed Up-and-Go test, NOS number of steps, ROM-Trunk range of motion of the trunk

Participants were primarily women. Men and women were similar in age. Twelve percent of the participants were living in assisted living facilities. Half of the participants were living alone and only 14% had a higher education degree. None of the participants was using gait assistance. In total, 74% of the participants reported health problems (42% were multimorbid and another 32% suffered from a single disease). The main reported health problems were: cardiovascular diseases (36% of the subjects), internal/endocrinological diseases (38%), orthopedic problems (39%), neurological/psychiatric diseases (11%) and others (2%). Regarding use of medication, 69% of the participants reported using medication (35% of the participants used multiple kinds of medication and 24% only one kind).

Overall correlation approach

The initial correlation approach between all mobility and cognitive measures revealed significant correlations mostly for the Attention Window test (between r = .228 and r = .513), and especially the AWH (between r = .257 and r = .513), which correlated significantly with 12 of the 14 mobility variables from all categories. HOTAP.A and HOTAP.B correlated significantly only with real-life mobility (between r = .240 and r = .345 and between r = .241 and r = .258, respectively) and gait parameters (between r = .320 and r = .321 and between r = .241 and r = .326, respectively). The Grid-Span tasks showed the least and weakest significant correlations (between r = .205 and r = .308). All bivariate correlations between cognitive and mobility measures are presented in Table 2.

Table 2.

Uncorrected correlation coefficients (Pearson’s for the normal-distributed and Spearman for the not-normal-distributed datasets) r between all mobility measures and cognitive measures (* p < .05; ** p < .01)

HOTAP.A HOTAP.B AWT AWH GSS GSC
AGT .192 .249* .407** .441** −.045 .168
Distance .240* .258* .319** .360** .075 .222*
Life-space .345** .241* .315** .412** −.011 .183
AR-max .292** .185 .228* .345** −.001 .174
iTUG −.320** −.273* −.256* −.303** −.182 −.198
Stride length .321** .241* .149 .259* .197 .157
Stride velocity .321** .326** .273* .303** .202 .270*
Cadence .110 .153 .171 .139 −.032 .104
Turn: duration −.164 −.069 −.294** −.353** −.219* −.106
Turn: NOS −.078 .001 −.197 −.257* −.205* −.093
Turn: Peak velocity .134 .112 .357** .384** .308** .228*
StS: ROM-Trunk .067 .132 .371** .352** .173 .285*
StS: Peak velocity .108 .073 .167 .133 −.010 .156
Tts: Peak velocity .181 .198 .483** .513** .214* .241*
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AWT Attention window total (along all axes), AWH Attention window horizontal, GSS Grid-Span simple, GGC Grid-Span complex, AGT active and gait time, AR-max maximum action range, iTUG instrumented Timed Up-and-Go test, Turn: NOS number of steps during the 180° turn, StS: ROM-Trunk range of motion of the trunk during the sit-to-stand transition, StS: Peak velocity peak velocity during the sit-to-stand transition, Tts: Peak velocity peak velocity during the Turn-to-Sit transition

Principal component analysis

All 14 mobility variables (as presented in Table 1) were included in the principal component analysis, which yielded five orthogonal factors accounting for 83.2% of total variance. However, the fifth factor included only one item (stride length) and since factors with fewer than three items loading on them are considered weak and unstable (Costello and Osborne 2005; Henson 2006), it was not considered in further analyses. The Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy (.657) and the Bartlett’s test of Sphericity (p = .000) indicated that the underlying assumptions for factor analysis were met (Williams et al. 2012). The remaining four-factor structure is presented in Table 3: three factors represent iTUG-based capacity data and one factor represents real-life mobility data with GPS-derived variables and little contribution of physical activity variables.

Table 3.

Item loadings for the Varimax-rotated solution

Turn Gait Real-life mobility Sit-to-stand transition
1 2 3 4
AGT .259 .406 .484 .349
Life-space .111 −.019 .879 −.150
Distance .110 −.057 .864 .049
AR-max −.019 .078 .854 .107
iTUG −.335 −.812 −.129 −.124
Stride velocity .197 .832 .049 .092
Cadence .032 .918 −.128 .051
StS: ROM-Trunk .135 .030 .016 .855
StS: Peak velocity −.107 .141 .019 .867
Turn: Duration −.924 −.257 −.042 .044
Turn: NOS −.932 .120 −.008 .057
Turn: Peak velocity .856 .258 .140 .086
Tts: Peak velocity .594 .420 .266 .248
Explained variance (%) 35.7 16.8 14.0 9.5
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AGT active and gait time, AR-max maximum action range, iTUG instrumented Timed Up-and-Go test, StS: ROM-Trunk range of motion of the trunk during the sit-to-stand transition, StS: Peak velocity peak velocity during the sit-to-stand transition, Turn: NOS number of steps during the 180° turn

Correlation analysis

Table 4 illustrates that AW correlated significantly with most mobility parameters—as formed by the scores of the factor analysis, whereas both HOTAP tasks correlated with real-life mobility measures and HOTAP.B additionally with gait. GSS correlated significantly only with Turn variables and the GSC did not correlate significantly with any of the mobility parameters.

Table 4.

Uncorrected correlation coefficients (Pearson’s for the normal-distributed and Spearman for the not-normal-distributed datasets) between factor scores and cognitive measures

HOTAP.A HOTAP.B AWT AWH GSS GSC
Turn .028 .010 .282* .316** .210* .056
Gait .199 .237* .179 .196 .027 .107
Real-life mobility .274* .229* .322** .396** −.012 .175
Sit-to-stand transition .114 .152 .315** .271* .086 .203
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AWT Attention window total (along all axes), AWH Attention window horizontal, GSS Grid-Span simple, GGC Grid-Span complex

* p < .05; ** p < .01

Multiple regression analysis

To evaluate the predictive ability of cognition for each of the mobility parameters—as formed by the factor analysis—four stepwise multiple regression analyses were conducted. For the stepwise model, the limit was F = 0.05 for entry and F = 0.10 for removal of variables. The predictor variables were checked for multicollinearity, and all variance inflation factor values were equal to 1 indicating no multicollinearity among factors.

The Attention Window measures were the only significant predictors retained in the four models: AWH for the models ‘Turn’ (R 2 = .102, p < .01), ‘Gait’ (R 2 = .079, p < .05), and ‘real-life mobility’ (R 2 = .135, p < .01) and AWT for the ‘sit-to-stand Transition’ model (R 2 = .086, p < .01). Table 5 summarizes the results of the multiple regression analyses.

Table 5.

Predictors and their standardized regression coefficients for the mobility-related activity measures

Turn Gait Real-life mobility Sit-to-stand transition
Predictors Beta Predictors Beta Predictors Beta Predictors Beta
AWH .319** AWH .281* AWH .368** AWT .293*
R 2 = .102** R 2 = .079* R 2 = .135** R 2 = .086*
Excluded variables
HOTAP.A −.063 HOTAP.A .132 HOTAP.A .188 HOTAP.A −.006
HOTAP.B −.155 HOTAP.B .143 HOTAP.B .088 HOTAP.B −.01
AWT −.011 AWT −.338 AWT −.154 AWH .022
GSS .116 GSS −.082 GSS −.117 GSS .009
GSC −.06 GSC .06 GSC .008 GSC .032
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AWT Attention window total (along all axes), AWH Attention window horizontal, GSS Grid-Span simple, GGC Grid-Span complex

* p < .05; ** p < .01

In order to explore the role of potential confounders for the relationship between mobility and cognition, another correlation analysis between age, gender, education, and health and the mobility parameters was applied. In our dataset, there were no significant correlations between them besides education which correlated significantly with gait (r = .311) and after controlling for it, AWH remained the only marginally significant predictor in the regression model.

Discussion

The aim of this study was to investigate the relative contribution of different cognitive domains toward mobility capacity and real-life mobility performance.

A principle component analysis reduced our mobility measures to four orthogonal factors. ‘Real-life mobility’ was an independent factor from other mobility-related factors (‘Turn,’ ‘Gait’ and ‘StS’). This is in line with previous research stating that mobility capacity and mobility performance constitute separate domains of physical function (Giannouli et al. 2016; van Lummel et al. 2015).

Similar to previous research, we found cognition to be associated with all aspects of mobility, namely, with physical activity (Buchman et al. 2008; Zhu et al. 2015), GPS-derived measures such as life-space (Tung et al. 2014) and action range (Tung et al. 2014; Wettstein et al. 2014b), gait (Buchman et al. 2011; Inzitari et al. 2007; Martin et al. 2013; Mielke et al. 2013), turn (Herman et al. 2014; Mirelman et al. 2014), sit-to-stand transitions (Herman et al. 2014; Mirelman et al. 2014) and mobility tests (Donoghue et al. 2012; Herman et al. 2011; Herman et al. 2014; McGough et al. 2011; Mirelman et al. 2014).

A specific pattern can be discerned in Table 4. Cognition had most significant associations with real-life mobility, fewer with ‘sit-to-stand’ and ‘turn’ and only one association with ‘gait.’ This is in line with the view that more complex activities require more complex cognitive contributions. Furthermore, spatially distributed attention had the most significant links to mobility, planning had fewer links and working memory had only one weak link, suggesting that mobility draws differently on different cognitive functions. The predominant role of visuospatial attention is in line with previous studies about the association between the useful field of view—a similar construct as our Attention Window and life-space (Sartori et al. 2012). It also is in accordance with studies reporting significant relationships between yaw amplitude during turns and attention (Herman et al. 2014) or visual-spatial ability (Mirelman et al. 2014). The small role of spatial working memory is in agreement with Wettstein et al. (2014b) but not with another study (Buchman et al. 2008); spatial working memory may play a minor role for mobility, or our Grid-Span test may not be sensitive enough to capture that role.

It might seem surprising that steady gait correlated little with cognition in the present study, given that earlier work found close associations, particularly with executive functioning (Martin et al. 2013; Mielke et al. 2013), working memory (Buchman et al. 2011) and attention (Inzitari et al. 2007; Martin et al. 2013). However, our preliminary correlation analysis did yield significant correlations of both gait variables with all cognitive measures (see Table 2), which indicates that correlations reported in the literature were present in our data as well, but were masked by more predominant effects in our analysis.

To sum up, we found cognition to correlate more closely with performance than with capacity, in accordance with our hypothesis. Visuospatial attention was the most predominant predictor of mobility, which is in line with previous research showing significant relationships between attention and several aspects of mobility such as overall motor performance (Inzitari et al. 2007), mobility (Owsley and McGwin 2004) and stair descent performance (Telonio et al. 2014). Hence, it is likely that attention training also has a positive effect on motor performance. Indeed, the study of Li et al. (2010) found transfer effects of non-motor attention training on balance ability. Further studies should examine the effects of attention training on additional aspects of mobility, for example gait but also out-of-home mobility.

Although this study has the strength of examining the relationships between cognitive functioning and a wide range of mobility parameters, we acknowledge several limitations. First of all, the cross-sectional design does not allow causal conclusions. Secondly, we could have included other or additional measures of mobility and cognition in our study. Our cognitive measures were limited to only three domains, and our mobility assessment was based on typical laboratory measures in combination with general mobility measures obtained in real life. It would have been interesting to also include real-life gait measures. However, at present it still is a methodological challenge to obtain reliable measures of gait during unstandardized real-life conditions. Thirdly, our statistical analysis was suboptimal. Data loss resulted in a smaller sample size than planned and statistical significance might be inflated by the use of multiple statistical tests. In fact, Bonferroni–Holm adjustments for multiple testing reduced significance to only one single association, that between real-life mobility and AWH. Thus, Bonferroni–Holm adjustments actually highlighted the main outcome of the study, namely the close link between attention and real-life mobility.

Further studies should aim to monitor the same measures of mobility in the laboratory and in real-life, e.g., gait speed and examine the relationship between mobility and more/other cognitive domains longitudinally, focusing especially on the role of attention by assessing the attentional demands of mobility-related activities which are crucial for mastering activities of daily living in old age.

Acknowledgements

We would like to gratefully acknowledge the assistance of Sandra Arenz, Sandra Brück, Henning Voss and Julia Wobst in data collection and Sabato Mellone in data processing and data analysis.

Funding

This study was funded by a grant from the German Sport University to the Graduate College ‘Reduced Mobility in Old Age’ and by the European Commission (FARSEEING, Seventh Framework Program, Cooperation—ICT, Grant Agreement No. 288940).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Beurskens R, Bock O. Age-related deficits of dual-task walking: a review. Neural Plast. 2012 doi: 10.1155/2012/131608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ble A, Volpato S, Zuliani G, Guralnik JM, Bandinelli S, Lauretani F, Bartali B, Maraldi C, Fellin R, Ferrucci L. Executive function correlates with walking speed in older persons: the InCHIANTI study. J Am Geriatr Soc. 2005;53:410–415. doi: 10.1111/j.1532-5415.2005.53157.x. [DOI] [PubMed] [Google Scholar]
  3. Buchman AS, Wilson RS, Bennett DA. Total daily activity is associated with cognition in older persons. Am J Geriatr Psychiatry. 2008;16(8):697–701. doi: 10.1097/JGP.0b013e31817945f6. [DOI] [PubMed] [Google Scholar]
  4. Buchman AS, Boyle PA, Leurgans SE, Barnes LL, Bennett DA. Cognitive function is associated with the development of mobility impairments in community-dwelling elders. Am J Geriatr Psychiatry. 2011;19(6):571–580. doi: 10.1097/JGP.0b013e3181ef7a2e. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Carlson MC, Fried LP, Xue Q-L, Bandeen-Roche K, Zeger SL, Brandt J. Association between executive attention and physical functional performance in community-dwelling older women. J Gerontol Ser B Psychol Sci Soc Sci. 1999;54B(5):S262–S270. doi: 10.1093/geronb/54B.5.S262. [DOI] [PubMed] [Google Scholar]
  6. Chaytor N, Schmitter-Edgecombe M. The ecological validity of neuropsychological tests: a review of the literature on everyday cognitive skills. Neuropsychol Rev. 2003;13(4):181–197. doi: 10.1023/B:NERV.0000009483.91468.fb. [DOI] [PubMed] [Google Scholar]
  7. Cohen RG, Vasavada AN, Wiest MM, Schmitter-Edgecombe M. Mobility and upright posture are associated with different aspects of cognition in older adults. Front Aging Neurosci. 2016;8:257. doi: 10.3389/fnagi.2016.00257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Costello A, Osborne J (2005) Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval 10(7)
  9. Crowe M, Andel R, Wadley VG, Okonkwo OC, Sawyer P, Allman RM. Life-space and cognitive decline in a community-based sample of African American and Caucasian older adults. J Gerontol Ser A Biol Sci Med Sci. 2008;63(11):1241–1245. doi: 10.1093/gerona/63.11.1241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Donoghue OA, Horgan NF, Savva GM, Cronin H, O’Regan C, Kenny RA. Association between Timed Up-and-Go and memory, executive function, and processing speed. J Am Geriatr Soc. 2012;60(9):1681–1686. doi: 10.1111/j.1532-5415.2012.04120.x. [DOI] [PubMed] [Google Scholar]
  11. Giannouli E, Bock O, Mellone S, Zijlstra W. Mobility in old age: capacity is not performance. Biomed Res Int. 2016 doi: 10.1155/2016/3261567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hale S, Myerson J, Rhee SH, Weiss CS, Abrams RA. Selective interference with the maintenance of location information in working memory. Neuropsychology. 1996;10(2):228–240. doi: 10.1037/0894-4105.10.2.228. [DOI] [Google Scholar]
  13. Henson RK. Use of exploratory factor analysis in published research: common errors and some comment on improved practice. Educ Psychol Meas. 2006;66(3):393–416. doi: 10.1177/0013164405282485. [DOI] [Google Scholar]
  14. Herman T, Giladi N, Hausdorff JM. Properties of the “Timed Up and Go” test: more than meets the eye. Gerontology. 2011;57(3):203–210. doi: 10.1159/000314963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Herman T, Weiss A, Brozgol M, Giladi N, Hausdorff JM. Identifying axial and cognitive correlates in patients with Parkinson’s disease motor subtype using the instrumented Timed Up and Go. Exp Brain Res. 2014;232:713–721. doi: 10.1007/s00221-013-3778-8. [DOI] [PubMed] [Google Scholar]
  16. Holtzer R, Verghese J, Xue X, Lipton R. Cognitive processes related to gait velocity: results from the Einstein aging study. Neuropsychology. 2006;20(2):215–223. doi: 10.1037/0894-4105.20.2.215. [DOI] [PubMed] [Google Scholar]
  17. Hüttermann S, Memmert D, Simons DJ, Bock O. Fixation strategy influences the ability to focus attention on two spatially separate objects. PLoS ONE. 2013;8(6):6–13. doi: 10.1371/journal.pone.0065673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Inzitari M, Newman A, Yaffe K. Gait speed predicts decline in attention and psychomotor speed in older adults: the health aging and body composition study. Neuroepidemiology. 2007;29:156–162. doi: 10.1159/000111577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Li KZH, Roudaia E, Lussier M, Bherer L, Leroux A, McKinley PA. Benefits of cognitive dual-task training on balance performance in healthy older adults. J Gerontol Ser A Biol Sci Med Sci. 2010;65A(12):1344–1352. doi: 10.1093/gerona/glq151. [DOI] [PubMed] [Google Scholar]
  20. Martin KL, Blizzard L, Wood AG, Srikanth V, Thomson R, Sanders LM, Callisaya ML. Cognitive function, gait, and gait variability in older people: a population-based study. J Gerontol Ser A Biol Sci Med Sci. 2013;68(6):726–732. doi: 10.1093/gerona/gls224. [DOI] [PubMed] [Google Scholar]
  21. McGough EL, Kelly VE, Logsdon RG, McCurry SM, Cochrane BB, Engel JM, Teri L. Associations between physical performance and executive function in older adults with mild cognitive impairment: gait speed and the Timed “Up and Go” test. Phys Ther. 2011;91:1198–1207. doi: 10.2522/ptj.20100372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Menant JC, Sturnieks DL, Brodie MAD, Smith ST, Lord SR, Woollacott M, Hermann F, et al. Visuospatial tasks affect locomotor control more than nonspatial tasks in older people. PLoS ONE. 2014;9(10):e109802. doi: 10.1371/journal.pone.0109802. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Menzel-Begemann A (2009) HOTAP – Handlungsorganisation und Tagesplanung. Testverfahren zur Erfassung der Planungsfähigkeit im Alltag, Göttingen
  24. Mielke MM, Roberts RO, Savica R, Cha R, Drubach DI, Christianson T, Petersen RC, et al. Assessing the temporal relationship between cognition and gait: slow gait predicts cognitive decline in the mayo clinic study of aging. J Gerontol Ser A Biol Sci Med Sci. 2013;68(8):929–937. doi: 10.1093/gerona/gls256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Mirelman A, Weiss A, Buchman AS, Bennett DA, Giladi N, Hausdorff JM. Association between performance on Timed Up and Go subtasks and mild cognitive impairment: further insights into the links between cognitive and motor function. J Am Geriatr Soc. 2014;62(4):673–678. doi: 10.1111/jgs.12734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Owsley C, McGwin G. Association between visual attention and mobility in older adults. J Am Geriatr Soc. 2004;52(11):1901–1906. doi: 10.1111/j.1532-5415.2004.52516.x. [DOI] [PubMed] [Google Scholar]
  27. Podsiadlo D, Richardson S. The Timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142–148. doi: 10.1111/j.1532-5415.1991.tb01616.x. [DOI] [PubMed] [Google Scholar]
  28. Ryu U, Ahn K, Kim E, Kim M, Kim B, Woo S, Chang Y (2013) Adaptive step detection algorithm for wireless smart step counter. In: 2013 international conference on information science and applications (ICISA). IEEE, pp 1–4. http://doi.org/10.1109/ICISA.2013.6579332
  29. Salarian A, Horak F. iTUG, a sensitive and reliable measure of mobility. IEEE Trans Biomed Eng. 2010;18(3):1–18. doi: 10.1109/TNSRE.2010.2047606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Salarian A, Russmann H, Vingerhoets FJG, Dehollain C, Blanc Y, Burkhard PR, Aminian K. Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng. 2004;51(8):1434–1443. doi: 10.1109/TBME.2004.827933. [DOI] [PubMed] [Google Scholar]
  31. Salarian A, Russmann H, Vingerhoets FJG, Burkhard PR, Aminian K. Ambulatory monitoring of physical activities in patients with Parkinson’s disease. IEEE Trans Biomed Eng. 2007;54(12):2296–2299. doi: 10.1109/TBME.2007.896591. [DOI] [PubMed] [Google Scholar]
  32. Salarian A, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Aminian K. iTUG, a sensitive and reliable measure of mobility. IEEE Trans Neural Syst Rehabil Eng. 2010;18(3):303–310. doi: 10.1109/TNSRE.2010.2047606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Sartori AC, Wadley VG, Clay OJ, Parisi JM, Crowe M. The relationship between cognitive function and life-space: the potential role of personal control beliefs. Psychol Aging. 2012;27(2):364–374. doi: 10.1037/a0025212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011;14(5):411–416. doi: 10.1016/j.jsams.2011.04.003. [DOI] [PubMed] [Google Scholar]
  35. Soumaré A, Tavernier B, Alpérovitch A, Tzourio C, Elbaz A. A cross-sectional and longitudinal study of the relationship between walking speed and cognitive function in community-dwelling elderly people. J Gerontol Ser A Biol Sci Med Sci. 2009;64(10):1058–1065. doi: 10.1093/gerona/glp077. [DOI] [PubMed] [Google Scholar]
  36. Telonio A, Blanchet S, Maganaris CN, Baltzopoulos V, Villeneuve S, McFadyen BJ. The division of visual attention affects the transition point from level walking to stair descent in healthy, active older adults. Exp Gerontol. 2014;50(1):26–33. doi: 10.1016/j.exger.2013.11.007. [DOI] [PubMed] [Google Scholar]
  37. Tung JY, Rose RV, Gammada E, Lam I, Roy EA, Black SE, Poupart P. Measuring life space in older adults with mild-to-moderate Alzheimer’s disease using mobile phone GPS. Gerontology. 2014;60(2):154–162. doi: 10.1159/000355669. [DOI] [PubMed] [Google Scholar]
  38. van Lummel RC, Walgaard S, Pijnappels M, Elders PJM, Garcia-Aymerich J, van Dieën JH, Beek PJ. Physical performance and physical activity in older adults: associated but separate domains of physical function in old age. PLoS ONE. 2015;10(12):e0144048. doi: 10.1371/journal.pone.0144048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Wahl H, Wettstein M, Shoval N, Oswald F, Kaspar R, Issacson M, Heinik J, et al. Interplay of cognitive and motivational resources for out-of-home behavior in a sample of cognitively heterogeneous older adults: findings of the SenTra project. J Gerontol B Psychol Sci Soc Sci. 2013;68(5):691–702. doi: 10.1093/geronb/gbs106. [DOI] [PubMed] [Google Scholar]
  40. Webber S. Mobility in older adults: a comprehensive framework. Gerontologist. 2010;50(4):443–450. doi: 10.1093/geront/gnq013. [DOI] [PubMed] [Google Scholar]
  41. Wettstein M, Wahl H-W, Shoval N, Auslander G, Oswald F, Heinik J. Cognitive status moderates the relationship between out-of-home behavior (OOHB), environmental mastery and affect. Arch Gerontol Geriatr. 2014;59(1):113–121. doi: 10.1016/j.archger.2014.03.015. [DOI] [PubMed] [Google Scholar]
  42. Wettstein M, Wahl H, Diehl MK. A multidimensional view of out-of-home behaviors in cognitively unimpaired older adults: examining differential effects of related predictors. Eur J Ageing. 2014;11:1–13. doi: 10.1007/s10433-013-0292-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Williams B, Brown T, Onsman A (2012) Exploratory factor analysis: a five-step guide for novices. Aust J Paramed. Retrieved from http://ro.ecu.edu.au/jephc/vol8/iss3/1
  44. World Health Organization (2005) International classification of functioning, disability, and health. http://www.who.int/classifications/icf/en/
  45. Yogev-Seligmann G, Hausdorff JM, Giladi N. The role of executive function and attention in gait. Mov Disord. 2008;23(3):329–342. doi: 10.1002/mds.21720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhu W, Howard VJ, Wadley VG, Hutto B, Blair SN, Vena JE, Hooker SP, et al. Association between objectively measured physical activity and cognitive function in older adults-the reasons for geographic and racial differences in stroke study. J Am Geriatr Soc. 2015;63(12):2447–2454. doi: 10.1111/jgs.13829. [DOI] [PMC free article] [PubMed] [Google Scholar]

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