A longitudinal study of measures of walking in people with Alzheimer's Disease

Gait & Posture 32 (2010) 113–117

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A longitudinal study of measures of walking in people with Alzheimer’s Disease Joanne E. Wittwer *, Kate E. Webster, Hylton B. Menz Musculoskeletal Research Centre, Faculty of Health Sciences, La Trobe University, Bundoora, VIC 3086, Australia

A R T I C L E I N F O

A B S T R A C T

Article history: Received 1 October 2009 Received in revised form 18 February 2010 Accepted 5 April 2010

Longitudinal gait measures may be used to provide baseline data for intervention studies. This has not previously been reported in people with Alzheimer’s Disease. In this study measures of walking and their variability were recorded for 19 people with Alzheimer’s Disease on two occasions 1 year apart. Matched controls were measured once. Variability was calculated using the coefficient of variation (CV). Effect size was calculated using Cohen’s d. Gait was slower and more variable in the Alzheimer’s Disease group compared to controls. Over 1 year there was a decrease in velocity (initial = 103.9 cm/s, followup = 95.1 cm/s; p < 0.05, d = 0.4) and stride length (initial = 119.6 cm, follow-up = 112.5 cm; p < 0.05, d = 0.34) and an increase in double support (initial = 24.2%, follow-up = 30.1%; p < 0.05, d = 0.99) and stride length variability (initial CV = 3.5%, follow-up CV = 4.6%; p < 0.05, d = 0.65). These changes occurred in mild as well as more severe Alzheimer’s Disease. Future research should focus on reducing this decline early in the course of the disease in order to maintain physical independence for as long as possible. ß 2010 Elsevier B.V. All rights reserved.

Keywords: Gait Biomechanics Dementia Aged

1. Introduction Cognitive impairment is the main defining characteristic of Alzheimer’s Disease (AD), however deterioration in physical ability is also a well recognised feature which begins early in the course of the disease [1–4]. In fact motor performance can decline before cognitive change is detected and may be a predictor of cognitive decline in AD [5]. Whilst longitudinal studies of people with AD have examined changes in various cognitive and behavioural domains [6], few studies have tracked changes in physical ability. A longitudinal study which used the UPDRS to measure motor signs in people with AD reported an annual increase of 3.9% of the total possible score for the posture/gait item which was rated on a scale of 0–8 [7]. Another recent longitudinal study of physical performance in people with AD used the time taken to perform activities of walking, turning and standing up and sitting down to measure change over time, but also converted this to a numerical score [8]. To our knowledge no study has specifically investigated change over time in quantitative measures of walking in people with AD. Quantifying the rate of decline in measures of gait and their variability in people with varying severity of AD will provide baseline data for potential intervention studies, for example exercise programs aimed at reducing or slowing decline in walking. One important consideration in measuring gait in this population is the need to balance the requirement for detailed

* Corresponding author. Tel.: +61 3 9479 5808; fax: +61 3 9479 5415. E-mail address: [email protected] (J.E. Wittwer). 0966-6362/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.gaitpost.2010.04.001

measures with the limitations imposed by the disease process. Measuring walking on an instrumented carpet is feasible for testing people with AD and provides measures of timing and foot placement during walking which are valid and reliable [9]. The purpose of this study was to report changes over a 1 year period in temporal and spatial measures of gait and their variability in a group of people with mild to moderate AD. 2. Methods 2.1. Participants Twenty-eight patients consecutively referred to Neurology and Geriatric Departments at a local public hospital were invited to participate in the study. To be invited, participants required a diagnosis of probable AD established by an experienced neurologist from the Memory Clinic at the hospital using a conservative application of the National Institute of Neurological and Communicative Disorders and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) classification [10]. They were also able to walk at least 50 m without assistance or use of a gait aid and follow instructions for the testing procedure. Participants were excluded if they were found on medical screening to have evidence of any other neurological, orthopaedic, respiratory, circulatory or visual condition which affected their gait or reported pain sufficient to affect walking. None were taking neuroleptic medication. Six people declined to participate, creating a group of 22 (11 male participants and 11 female participants) who commenced the study. Reasons for declining participation included anxiety about unfamiliar activities (one person), no interest in the project (three people) and insufficient time in a busy schedule (two people). A further three participants did not complete testing after 12 months (one had become unable to walk without assistance, one was unable to follow testing instructions and one declined) leaving a group of 19 who completed the study. A priori power calculations determined that with statistical significance set at a 2sided level of 0.5 (probability, type I error), a power of 0.8 (i.e. probability, type II error = 0.2) with an effect size of 0.8, a minimum of 15 participants would be required for paired comparisons.

J.E. Wittwer et al. / Gait & Posture 32 (2010) 113–117

114 Table 1 Participant characteristics. Pair number

Gender

Age (years)

AD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

F F F F F F F F F M M M M M M M M M M Mean (SD) Range

a

Height (m)

Control 64 70 78 79 79 80 84 87 91 70 74 78 78 80 81 81 82 83 84

64 74 77 81 81 82 84 87 92 74 75 76 78 80 80 81 83 84 85

79.14 (6.30) 64–91

79.96 (6.03) 64–92

Weight (kg)

MMSE

AD

Control

AD

Control

AD

Control

1.71 1.60 1.68 1.63 1.68 1.67 1.61 1.60 1.45 1.79 1.70 1.56 1.73 1.71 1.71 1.58 1.80 1.70 1.75

1.62 1.71 1.71 1.55 1.56 1.58 1.61 1.52 1.44 1.71 1.75 1.84 1.73 1.79 1.82 1.71 1.87 1.77 1.68

80 67 67 79 50 65 50 56 54 75 70 81 72 77 60 73 71 71 75

52 75 73 55 55 56 60 55 45 73 77 90 73 93 82 63 97 78 63

14 24 19 22 18 24 19 17 26 27 20 13 25 25 24 13 26 24 23

29 30 28 28 29 28 29 28 26 28 30 30 29 30 29 30 30 30 28

1.67 (0.09)

1.68 (0.12)

68.05 (9.83)

69.21 (14.86)

21.21 (4.53) 13–27

28.89 (1.10) 26–30

Medicationa

Illness duration (weeks)

AD

Control

AD

2 3

135.7 53.7 243.4 208.7 38.9 109.6 53.3 20.3 10.6 64.3 63.0 33.3 27.7 0.1 16.9 29.9 10.9 105.0 26.9

1 1,3 1 3 1 3 1 1,3 1 1,3 1,2 1,3 2,3 1 3 1,3 1 1,3

2 3 3 3 3 3

3 3 3 3 3 3 2

65.91 (67.28) 0.1–243.4

1: cholinesterase inhibitors; 2: psychotropic medication; 3: anti-hypertensive medication.

The Mini Mental State Examination (MMSE) was used to evaluate cognitive dysfunction [11]. This test has a highest possible score of 30 with severity of dementia graded as questionable (MMSE range 26–29), mild (MMSE range 21–25), moderate (MMSE range 11–20) and severe (MMSE range 0–10) [12]. The group of 19 participants with AD included 11 classified as mild and eight as moderate. Nineteen control participants matched for gender and age (4 years) were recruited from a database of gait research volunteers. Recruitment to this database occurs mainly via contacts of the authors and advertising in local retirement villages. Volunteers are screened at the time of recruitment to a study using a questionnaire designed to detect musculoskeletal, neurological or other problems including pain which would affect their walking. They also complete a MMSE and are excluded if their score is below 26 as a score of 25 or below denotes impaired cognition. At the time of testing, both AD and control participants were screened for lower limb strength and range of movement and all were within normal limits. Participant characteristics are summarized in Table 1. Eight of the control participants had fallen at least once in the preceding year compared with seven from the AD group, and two participants from each group had fallen two or more times. The University Human Ethics Committee approved the study and informed consent was obtained for all participants. 2.2. Apparatus Spatial and temporal gait measures were recorded using a GAITRite1 system (CIR Systems, Inc., 60 Garlor Drive Havertown, PA 19083). The GAITRite1 walkway is a carpet embedded with individual sensors arranged 1.27 cm apart in a grid pattern which allows detection of footstep pressure as participants walk on the carpet. The carpet used in this study was 830 cm long and 89 cm wide with an active sensor area of 732 cm long and 61 cm wide. Data were sampled at a rate of 80 Hz. Application software was used to process and store spatial and temporal characteristics of participants’ walking. The system has been shown to be a reliable [9,13] and valid [14,15] measure of walking. 2.3. Procedure Participants with AD attended the Movement Laboratory at La Trobe University on two occasions, 12 months apart. Control participants attended once. The testing procedure was the same for all participants during each visit. After a brief medical history measurements of height and weight were obtained, the MMSE was administered. For the gait assessment participants were instructed to stand with feet level on the beginning of the active area of the carpet. On the command ‘‘Ready, go’’ they walked at their own comfortable speed along the carpet to a point 2 m beyond its end. Walks were started on the carpet so that there was no initial alteration in floor surface, however in order to measure only steady-state gait and according to the guidelines for spatiotemporal gait analysis in older adults [16], data from the initial 2 m of the carpet in each walk were removed using the footfall editor function of the software. Two familiarisation walks (not included in the analysis) and then at least five further

walks were completed up to a maximum of 10. Walks were not used if the participant had talked or become distracted whilst walking, as secondary tasks have been shown to alter gait characteristics [17]. Strides from multiple walks were used for analysis [18–20] for each participant. Rests were permitted as often as required. Participants were asked to bring their own comfortable lowheeled shoes and where possible the same shoes were worn for both tests. A researcher accompanied each participant during all walks to minimise the risk of falls, but remained out of the participant’s field of vision. All participants completed a falls diary for 1 year after their first laboratory attendance. Participants mailed diary sheets at the end of each month with telephone followup by a researcher if these were not received. 2.4. Data analysis All walks for each participant were processed using the application software. The number of strides available from each of the two tests for each participant with AD was then compared with the number for their matched control. The required number of strides was then removed from the end of the relevant test to create equal numbers in each. Thus, if a participant with AD had 25 strides available for analysis from their initial test and 28 from their 12 month test, and their matched control participant had 30 strides, the last 3 strides were removed from the 12 month test and the last 5 strides were removed from the control participant’s test, leaving each with 25 strides. This resulted in an average of 25.3 strides (range 20–34 strides) being used for subsequent analysis. Gait measures used for analysis were velocity, cadence, step time, stride length, support base and the percentage of the gait cycle spent in double support. The GAITRite1 software calculates step time from initial contact of one foot to that of the opposite foot. Stride length is measured from the geometric centre of the heel for consecutive footsteps of the same leg and support base is the vertical distance from heel centre of one footprint to the line of progression formed by two footprints of the opposite foot. In a gait cycle consisting of a stance and swing phase of one leg there are two periods, initial and terminal double support, when both feet are in contact with the floor. Double support is the sum of these two periods expressed as a percentage of the gait cycle time. Data from each included stride for each participant were averaged to give an individual mean and standard deviation for each of the six gait measures. The coefficient of variation (CV) which expresses the ratio of the standard deviation of the scores to the mean as a percentage was calculated as a measure of the variability of gait parameters. Paired t-tests were used to assess changes in spatiotemporal gait parameters of the AD group and their variability over the elapsed year. As the degree of cognitive impairment in the AD group, measured by MMSE scores, ranged in severity from 27 to 13, it was suggested that there might be greater deterioration over a year in walking measures in the more severely affected participants. The group was therefore divided into mild (11 participants, MMSE scores >20) and moderate (eight participants, MMSE scores 20) sub-groups and comparisons repeated for each sub-group between initial walking measures and those taken 1 year later.

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Table 2 Comparison of spatiotemporal parameters and their variability in people with AD and controls. Spatiotemporal parameters

Mean velocity (cm/s)

Mean cadence (steps/min)

Mean step time (s)

Mean stride length (cm)

Mean support base (cm)

Mean DS (% gait cycle)

AD baseline Control Independent t-test Mann–Whitney U test Effect size (Cohen’s d)

103.9 (18.7) 118.1 (19.1) p = 0.026*

104.1 (9.1) 111.0 (12.3) p = 0.058

0.58 (0.05) 0.55 (0.06) p = 0.069

119.6 (19.3) 127.5 (12.7) p = 0.147

9.2 (3.1) 8.9 (2.2) p = 0.754

24.2 (4.1) 24.7 (4.5)

0.77

0.66

0.56

0.5

0.11

p = 0.343 0.12

Variability of spatiotemporal parameters

CV mean velocity (%)

CV mean cadence (%)

CV mean step time (%)

CV mean stride length (%)

CV mean support base (%)

CV mean DS (%)

AD baseline Control Independent t-test Mann–Whitney U test Effect size (Cohen’s d)

5.4 (2.6) 3.7 (1.0)

4.3 (1.5) 3.3 (0.5) p = 0.016*

4.3 (1.5) 3.2 (0.5) p = 0.008*

3.5 (1.3) 2.9 (0.8) p = 0.107

23.6 (13.3) 23.2 (5.3)

9.1 (2.7) 8.9 (2.7) p = 0.808

0.92

1.01

0.57

p = 0.007* 0.89

p = 0.609 0.04

0.07

Mean (SD). * p < 0.05.

In order to evaluate any initial differences in measures of walking between this community dwelling group of people with AD and healthy controls independent samples t-tests were used. Where the assumption of normality was violated Wilcoxon signed rank tests and Mann–Whitney U tests were used respectively. Cohen’s d was used to calculate effect sizes. Statistical significance was set to p < 0.05 and statistical analysis was conducted using SPSS software (Version 17.0 Chicago, IL).

3. Results 3.1. Comparison of AD baseline measures and controls When initial gait measures for this group of participants with mild to moderate AD were compared with those of the matched control participants, people with AD walked almost 14 cm/s more slowly (p < 0.05) but there were no other significant differences in the walking measures used, reflecting on average the lower severity of the disease in the group at the initial assessment (Table 2). Comparison of the variability of walking of the AD group with that of controls at the initial assessment revealed greater variability to be already evident in the AD group in measures of gait speed, cadence and step time (Table 2). 3.2. Comparison of AD baseline and 12 month follow-up measures Over the 12-month period between the two testing sessions the average comfortable speed of walking for the AD group showed a

statistically significant reduction of almost 9 cm/s (p < 0.01) (Table 3). Significant changes also occurred in average stride length which reduced by about 7 cm (p < 0.001) and the percentage of the gait cycle spent in double support which increased by just over 6% (p < 0.001). The magnitude of the difference in the means was large for double support (Table 3). The analysis of mild and moderate sub-groups revealed statistically significant changes in average measures of stride length and double support percentage in both over the 12-month period between tests (Table 3). Significant decreases in measures of velocity and cadence were found only in the moderate subgroup and in fact the mean cadence of the mild group was almost unchanged. After 1 year the only measure of variability to have significantly increased further for the AD group was that of mean stride length (Table 4). In the subgroups this significant deterioration in the stability of stride length occurred in the group with moderate AD but was not evident in the less severe group (Table 4). MMSE scores did not decline significantly for the whole group or for either sub group over the year between tests. Falls rates were similar for the AD and control groups with seven people with AD falling in the 12-month period following the baseline assessment compared with eight controls. Of those who fell, two people in each of the AD and control groups recorded two or more falls.

Table 3 Comparison of spatiotemporal parameters in participants with AD at baseline and follow-up. Mean velocity (cm/s)

Mean cadence (steps/min)

Mean step time (s)

Mean stride length (cm)

Mean support base (cm)

Mean DS (% gait cycle) 24.2 (4.1) 30.1 (7.6)

All AD Baseline Follow-up Paired t-test Wilcoxon signed ranks test Effect size (Cohen’s d)

103.9 (18.7) 95.1 (25.9) p = 0.003*

104.1 (9.1) 100.0 (12.2) p = 0.058

0.58 (0.05) 0.61 (0.10) p = 0.063

119.6 (19.3) 112.5 (23.3) p = 0.000*

9.2 (3.1) 10.0 (3.0) p = 0.102

0.4

0.39

0.39

0.34

0.27

p = 0.000* 0.99

Mild AD Baseline Follow-up Paired t-test Effect size (Cohen’s d)

111.7 (15.8) 107.6 (20.6) p = 0.076 0.23

105.9 (7.8) 105.0 (7.7) p = 0.654 0.12

0.57 (0.04) 0.58 (0.04) p = 0.643 0.26

127.3 (19.1) 122.8 (20.3) p = 0.010* 0.24

9.1 (3.3) 9.6 (3.1) p = 0.341 0.16

23.0 (3.3) 28.0 (4.7) p = 0.000* 1.29

93.1 (17.7) 77.9 (23.0) p = 0.015*

101.8 (10.6) 93.1 (14.3) p = 0.048*

0.60 (0.06) 0.66 (0.13) p = 0.069

109.0 (14.6) 98.2 (20.1) p = 0.011*

9.3 (2.9) 10.4 (3.1) p = 0.212

25.9 (4.8) 33.1 (10.0)

0.79

0.74

0.63

0.66

0.39

Moderate AD Baseline Follow-up Paired t-test Wilcoxon signed ranks test Effect size (Cohen’s d) Mean (SD). * p < 0.05.

p = 0.036* 0.98

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Table 4 Comparison of variability of spatiotemporal parameters in participants with AD at baseline and follow-up.

All AD Baseline Follow-up Paired t-test Wilcoxon signed ranks test Effect size (Cohen’s d) Mild AD Baseline Follow-up Paired t-test Wilcoxon signed ranks test Effect size (Cohen’s d) Moderate AD Baseline Follow-up Paired t-test Wilcoxon signed ranks test Effect size (Cohen’s d)

CV mean velocity (%)

CV mean cadence (%)

CV mean step time (%)

CV mean stride length (%)

CV mean support base (%)

CV mean DS (%)

5.4 (2.6) 5.8 (2.3)

4.3 (1.5) 5.1 (2.2)

4.3 (1.5) 5.0 (2.2) p = 0.097

3.5 (1.3) 4.6 (2.1) p = 0.014*

23.6 (13.3) 21.9 (11.3)

9.1 (2.7) 8.5 (3.0) p = 0.486

p = 0.601 0.17

p = 0.968 0.44

0.38

0.65

p = 0.520 0.14

0.22

4.7 (1.4) 4.7 (1.4) p = 0.996

3.8 (1.0) 4.0 (1.1) p = 0.557

3.8 (1.0) 4.0 (1.0) p = 0.636

3.2 (1.2) 3.9 (2.1)

24.4 (8.6) 23.6 (10.5)

9.4 (2.9) 7.8 (3.4)

0

0.2

0.21

p = 0.091 0.43

p = 0.594 0.09

p = 0.155 0.53

6.2 (3.5) 7.3 (2.6)

4.9 (1.9) 6.5 (2.5) p = 0.103

4.9 (1.9) 6.5 (2.7)

3.9 (1.4) 5.4 (2.0) p = 0.045*

22.5 (18.6) 19.5 (12.6)

8.7 (2.5) 9.5 (2.0) p = 0.606

p = 0.161 0.38

0.77

p = 0.123 0.73

0.93

p = 0.674 0.2

0.38

Mean (SD). * p < 0.05.

4. Discussion There are two important findings of this study. First, significant changes occurred in a number of spatial and temporal gait measures in this population over a year. Second, whilst most changes over this period of time occurred in those with more severe dementia, significant changes in mean stride length and double support also occurred in the mild group. The increase in double support was particularly notable and evidenced by large effect sizes. This presumably indicates that the participants with AD were making a substantial compensation for decreased balance as double support may be used to control stability between steps and increase when balance is threatened [21]. The only walking measure to become significantly more variable after a year was stride length, although as measures of speed, cadence and step time were already significantly more variable compared to controls at the initial assessment, this loss of stability may be one of the earliest manifestations of change in gait measures. Decreased walking speeds have been associated with increased variability in older adults [22]. It is unlikely however that the increased variability found in our study can be attributed simply to the slower walking speed of the AD group, as a recent study demonstrated greater variability of stride length in people with AD compared to controls when walking at similar speeds [23]. Even though an association has been identified between increased stride length variability and falls in people with dementia [24] rates of falling were almost identical in the AD and control groups in our study. This may be due to the lower average severity of dementia in our group as the association in the previous study between stride length variability and falls was found for people with moderate AD. Whilst decline in walking is expected in more severe AD this study has added to the evidence that significant deterioration occurs in the earlier disease stages. It is also interesting to note that significant changes in measures of walking and their variability occurred in the absence of a significant change in MMSE scores. A recent study of cognitive decline in 107 people with AD found a significant change in MMSE scores over 1 year with an average annual rate of decline of 2.3 points [25]. Participants in the study however had not used cholinesterase inhibitors whilst most of those in our study were taking this medication which is designed to improve cognition. Even though the MMSE is widely used, its

sensitivity in detecting change in cognitive function in people with AD may be lower than other disease-specific measures. There is evidence that exercise training is both feasible and effective in improving physical function for people with AD especially earlier in the course of the disease [26,27]. Exercise training has also resulted in specific improvements in gait measures for people with dementia of mixed aetiology [28]. An improvement of almost 10% in the time taken to walk 6 m was recorded for a group of people with dementia following a 6 week program of progressive resistance exercises [29]. Given that significant impairments of walking in people with AD are evident early when cognitive impairment is milder, exercise interventions commenced at this early stage and aimed specifically at improving walking and reducing its variability may be effective in delaying the resultant functional decline. The study should be interpreted in light of some limitations. It should be noted that the deterioration in gait measures documented for the group with AD in this study is not solely due to the effects of disease progression as it includes decline due to ageing of participants. The decrease in walking speed recorded over 1 year for the AD group in this study was however more than four times greater than the decline recorded in a cross-sectional study of cognitively normal people between the seventh and eighth decade [30]. The falls information included was only for the purpose of further illustrating group characteristics and the study was not powered to allow exploration of relationships between gait variables and falls rates. Whilst extensive testing of cognitive function using a variety of disease specific tests was undertaken by participants with AD at the time of their diagnosis, only the MMSE was administered at the time of the gait assessments as the latter were the primary focus of this study. Strengths of this study include the consistent application of widely accepted diagnostic criteria for participants with AD and the use of a valid and reliable system for measuring walking. 5. Conclusion The walking of a group of community dwelling people with mild to moderate AD demonstrated significant deterioration in both spatial and temporal measures over a 12-month period and this occurred in people with both mild and more severe cognitive decline. Whilst only the measure of stride length became

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significantly more variable over this period of time, measures of velocity, cadence and step time were already significantly more variable when compared with those of controls at the initial assessment. Future research should focus on potential methods of improving walking of people with AD early in the course of the disease or reducing the rate of decline in order to maintain physical independence for as long as possible. Conflict of interest statement The authors declare no conflict of interest. Acknowledgements This project was partially supported by a ‘‘Hazel Hawke Research Grant in Dementia Care’’ from Alzheimer’s Australia. A/Prof Menz is currently a National Health and Medical Research Council fellow (Clinical Career Development Award, ID: 433049). The authors thank Dr John Merory for assistance with patient recruitment and screening, Peta Andrews, Edwina Lorbach and Daniel Serrano for assistance with data collection and the study participants and their families for their involvement. References [1] Franssen EH, Souren LE, Torossian CL, Reisberg B. Equilibrium and limb coordination in mild cognitive impairment and mild Alzheimer’s disease. J Am Geriatr Soc 1999;47(4):463–9. [2] Nadkarni NK, Mawji E, McIlroy WE, Black SE. Spatial and temporal gait parameters in Alzheimer’s disease and aging. Gait Posture 2009;30(4):452–4. [3] Pettersson AF, Olsson E, Wahlund LO. Motor function in subjects with mild cognitive impairment and early Alzheimer’s disease. Dement Geriatr Cogn Disord 2005;19:299–304. [4] Wittwer JE, Andrews PT, Webster KE, Menz HB. Timing Variability during gait initiation is increased in people with Alzheimer’s disease compared to controls. Dement Geriatr Cogn Disord 2008;26(3):277–83. [5] Verghese J, Wang C, Lipton RB, Holtzer R, Xue X. Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Psychiat 2007;78(9):929–35. [6] Mohs RC, Schmeidler J, Aryan M. Longitudinal studies of cognitive, functional and behavioural change in patients with Alzheimer’s disease. Stat Med 2000;19(11–12):1401–9. [7] Scarmeas N, Hadjigeorgiou GM, Papadimitriou A, et al. Motor signs during the course of Alzheimer disease. Neurology 2004;63(6):975–82. [8] Hebert LE, Scherr PA, McCann JJ, Bienias JL, Evans DA. Change in direct measures of physical performance among persons with Alzheimer’s disease. Aging Ment Health 2008;12(6):729–34. [9] Wittwer JE, Webster KE, Andrews PT, Menz HB. Test–retest reliability of spatial and temporal gait parameters of people with Alzheimer’s disease. Gait Posture 2008;28(3):392–6.

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